Machine learning & artificial intelligence. Machine learning is playing an increasingly important role in computing and artificial intelligence. Suits any article on AI, algorithms, machine learning, quantum computing, artificial intelligence.
Machine learning training bootcamp is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI).
Attendees will learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction.
Learning Objectives:
Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
List similarities and differences between AI, Machine Learning and Data Mining
Learn how Artificial Intelligence uses data to offer solutions to existing problems
Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn.
Clarify how Data Mining can serve as foundation for AI and machine learning.
List the various applications of machine learning and related algorithms
Learn how to classify the types of learning such as supervised and unsupervised learning
Implement supervised learning techniques such as linear and logistic regression
Use unsupervised learning algorithms including deep learning, clustering , etc.
Learn about classification data and Machine Learning models
Select the best algorithms applied to Machine Learning
Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering
more...
Course Agenda and Topics:
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Review of Terminology and Principles
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Learning Applied to Machine Learning
Principal Component Analysis
Principles of Supervised Machine Learning Algorithms
Principles of Unsupervised Machine Learning
Regression Applied to Machines Learning
Principles of Neural Networks
Large Scale Machine Learning
Introduction to Deep Learning
Applying Machine Learning
Overview of Algorithms
Overview of Tools and Processes
Call us today at +1-972-665-9786. Learn more about course audience, objectives, outlines, pricing. Visit our website links below.
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
This presentation guide you through Machine learning with python, importance of machine learning, do you know facts of machine learning, good machine learning system requirements, types of machine learning, Uses of machine learning and what is used
for?
For more topics stay tuned with Learnbay.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
List of top Machine Learning algorithms are making headway in the world of data science. Explained here are the top 10 of these machine learning algorithms - https://www.dezyre.com/article/top-10-machine-learning-algorithms/202
This presentation guide you through Machine learning with python, importance of machine learning, do you know facts of machine learning, good machine learning system requirements, types of machine learning, Uses of machine learning and what is used
for?
For more topics stay tuned with Learnbay.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
List of top Machine Learning algorithms are making headway in the world of data science. Explained here are the top 10 of these machine learning algorithms - https://www.dezyre.com/article/top-10-machine-learning-algorithms/202
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...Simplilearn
This presentation on "Machine Learning Engineer Salary, Skills & Resume" will help you understand who is a Machine Learning engineer, the salary of a Machine Learning engineer, skills required to become a Machine Learning engineer and what a Machine Learning engineer's resume should look like. Machine Learning is the study of algorithms and data models that computer systems utilize to perform specific tasks without using instructions, relying on previous patterns. To make this possible, a Machine Learning engineer is required. Now, let us get started and understand what the job of a Machine Learning engineer looks like.
Below are the topics that we will be discussing in the presentation:
1. Introduction to Machine Learning
2. Responsibilities of a Machine Learning engineer
3. Salary Trends of a Machine Learning engineer
4. Skills of a Machine Learning engineer
5. Resume of a Machine Learning engineer
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
How Python can be used for machine learning?NexSoftsys
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Machine learning, Machine learning training bootcampTonex
This is a course for Data Scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.
Machine Learning training Bootcamp is a 3-day technical training course that covers the fundamentals of machine learning.
How machine learning helps ?
Machine learning helps to automate the data analysis process by enabling computers, machines and IoT to learn and adapt through experience applied to specific tasks without explicit programming.
Machine learning has huge potential to help wrangle and draw insights from scientific research. But it has also been successfully deployed in everyday situations, including:
Predicting traffic
Gleaning information from personal assistants
Monitoring video surveillance
Filtering email spam
Online customer support via chat bots
Online fraud detection
Personal product recommendations based on your buying/browsing habits
Course Agenda and Topics
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Large Scale Machine Learning
Overview of Algorithms
Hands-on Activities
Learn more.
Machine learning, Machine learning training bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Most Advanced Machine learning Training Bootcamp - Tonex TrainingBryan Len
Length : 3 days
Machine Learning training bootcamp is a 3-day technical and most advanced, time being training course by Tonex that covers the fundamentals of machine learning.
This is a course for Data Scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.
Machine learning computerizes the data investigation process by empowering PCs, machines and IoT to learn and adjust through experience applied to explicit undertakings without express programming.
Participants learn, appreciate and ace thoughts on machine learning ideas, key standards, and methods including regulated and unaided learning, scientific and heuristic angles, demonstrating to create calculations, expectation, straight relapse, grouping, arrangement, and forecast.
Learning Objectives:
Subsequent to finishing this course, the members will:
Find out about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
Rundown similitudes and contrasts between AI, Machine Learning and Data Mining
Figure out how Artificial Intelligence utilizes data to offer answers for existing issues
Investigate how Machine Learning goes past AI to offer data vital for a machine to learn, adjust and upgrade
Explain how Data Mining can fill in as establishment for AI and machine learning to utilize existing data to feature designs
Rundown the different utilizations of machine learning and related calculations
More
Course Agenda and Topics:
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Review of Terminology and Principles
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Learning Applied to Machine Learning
Principal Component Analysis
Principles of Supervised Machine Learning Algorithms
Principles of Unsupervised Machine Learning
Regression Applied to Machines Learning
Principles of Neural Networks
Large Scale Machine Learning
Hands-on Activities
More.
Request more information regarding machine learning training bootcamp. Visit tonex.com for course and workshop detail.
Most Advanced Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
High Value Machine Learning Seminars 2020 - Tonex TrainingBryan Len
All about learning the faces of ML. Machine learning makes it conceivable to rapidly and consequently produce models that can break down greater, progressively complex data and convey quicker, increasingly exact outcomes – even on an enormous scale.
Furthermore, by building exact models, an association has a superior possibility of distinguishing productive chances – or staying away from obscure dangers.
Machine learning applications can essentially be found wherever now, including:
Online extortion recognition
Online proposal offers, for example, those from TripAdvisor, Netflix and Amazon
Recognizing what clients are stating about you on Twitter
Traffic expectations
Video observation
Email spam and malware separating
Online client service
Machine Learning Methods :
Be that as it may, the two most mainstream are supervised learning and unsupervised learning.
Machine Learning Seminars
Tonex offers a 3-day Machine Learning Training Bootcamp where members will learn, fathom and ace thoughts on machine learning ideas, key standards, systems including:
Supervised and unsupervised learning,
Numerical and heuristic viewpoints,
Demonstrating to create calculations,
Straight relapse,
Bunching, grouping and expectation.
Who Should Attend Machine Learning Seminars:
Anybody whose work interfaces with data examination who needs to learn key ideas, definitions, calculations, and down to earth instances of what is conceivable in Machine Learning and Artificial Intelligence.
Chiefs who need the vision and comprehension of the numerous chances, costs, and likely execution jumps in prescient demonstrating, particularly as they relate to a lot of literary (or comparative) data.
Why Choose Tonex for Machine Learning ?
– Course motivation can be custom fitted to fit the necessities of your association.
– Reasonably evaluated classes educated by the best mentors is the explanation a wide range of associations from Fortune 500 organizations to government's most significant offices return for refreshes in courses and hands-on workshops
– Ratings arranged from understudy criticism post-course assessments show an astonishing 98 percent fulfillment score.
Request more information regarding machine learning seminars 2020. Visit tonex.com for course and workshop detail.
High Value Machine Learning Seminars 2020 - Tonex Training
https://www.tonex.com/machine-learning-seminars/
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...Simplilearn
This presentation on "Machine Learning Engineer Salary, Skills & Resume" will help you understand who is a Machine Learning engineer, the salary of a Machine Learning engineer, skills required to become a Machine Learning engineer and what a Machine Learning engineer's resume should look like. Machine Learning is the study of algorithms and data models that computer systems utilize to perform specific tasks without using instructions, relying on previous patterns. To make this possible, a Machine Learning engineer is required. Now, let us get started and understand what the job of a Machine Learning engineer looks like.
Below are the topics that we will be discussing in the presentation:
1. Introduction to Machine Learning
2. Responsibilities of a Machine Learning engineer
3. Salary Trends of a Machine Learning engineer
4. Skills of a Machine Learning engineer
5. Resume of a Machine Learning engineer
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
How Python can be used for machine learning?NexSoftsys
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Machine learning, Machine learning training bootcampTonex
This is a course for Data Scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.
Machine Learning training Bootcamp is a 3-day technical training course that covers the fundamentals of machine learning.
How machine learning helps ?
Machine learning helps to automate the data analysis process by enabling computers, machines and IoT to learn and adapt through experience applied to specific tasks without explicit programming.
Machine learning has huge potential to help wrangle and draw insights from scientific research. But it has also been successfully deployed in everyday situations, including:
Predicting traffic
Gleaning information from personal assistants
Monitoring video surveillance
Filtering email spam
Online customer support via chat bots
Online fraud detection
Personal product recommendations based on your buying/browsing habits
Course Agenda and Topics
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Large Scale Machine Learning
Overview of Algorithms
Hands-on Activities
Learn more.
Machine learning, Machine learning training bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Most Advanced Machine learning Training Bootcamp - Tonex TrainingBryan Len
Length : 3 days
Machine Learning training bootcamp is a 3-day technical and most advanced, time being training course by Tonex that covers the fundamentals of machine learning.
This is a course for Data Scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.
Machine learning computerizes the data investigation process by empowering PCs, machines and IoT to learn and adjust through experience applied to explicit undertakings without express programming.
Participants learn, appreciate and ace thoughts on machine learning ideas, key standards, and methods including regulated and unaided learning, scientific and heuristic angles, demonstrating to create calculations, expectation, straight relapse, grouping, arrangement, and forecast.
Learning Objectives:
Subsequent to finishing this course, the members will:
Find out about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
Rundown similitudes and contrasts between AI, Machine Learning and Data Mining
Figure out how Artificial Intelligence utilizes data to offer answers for existing issues
Investigate how Machine Learning goes past AI to offer data vital for a machine to learn, adjust and upgrade
Explain how Data Mining can fill in as establishment for AI and machine learning to utilize existing data to feature designs
Rundown the different utilizations of machine learning and related calculations
More
Course Agenda and Topics:
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Review of Terminology and Principles
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Learning Applied to Machine Learning
Principal Component Analysis
Principles of Supervised Machine Learning Algorithms
Principles of Unsupervised Machine Learning
Regression Applied to Machines Learning
Principles of Neural Networks
Large Scale Machine Learning
Hands-on Activities
More.
Request more information regarding machine learning training bootcamp. Visit tonex.com for course and workshop detail.
Most Advanced Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
High Value Machine Learning Seminars 2020 - Tonex TrainingBryan Len
All about learning the faces of ML. Machine learning makes it conceivable to rapidly and consequently produce models that can break down greater, progressively complex data and convey quicker, increasingly exact outcomes – even on an enormous scale.
Furthermore, by building exact models, an association has a superior possibility of distinguishing productive chances – or staying away from obscure dangers.
Machine learning applications can essentially be found wherever now, including:
Online extortion recognition
Online proposal offers, for example, those from TripAdvisor, Netflix and Amazon
Recognizing what clients are stating about you on Twitter
Traffic expectations
Video observation
Email spam and malware separating
Online client service
Machine Learning Methods :
Be that as it may, the two most mainstream are supervised learning and unsupervised learning.
Machine Learning Seminars
Tonex offers a 3-day Machine Learning Training Bootcamp where members will learn, fathom and ace thoughts on machine learning ideas, key standards, systems including:
Supervised and unsupervised learning,
Numerical and heuristic viewpoints,
Demonstrating to create calculations,
Straight relapse,
Bunching, grouping and expectation.
Who Should Attend Machine Learning Seminars:
Anybody whose work interfaces with data examination who needs to learn key ideas, definitions, calculations, and down to earth instances of what is conceivable in Machine Learning and Artificial Intelligence.
Chiefs who need the vision and comprehension of the numerous chances, costs, and likely execution jumps in prescient demonstrating, particularly as they relate to a lot of literary (or comparative) data.
Why Choose Tonex for Machine Learning ?
– Course motivation can be custom fitted to fit the necessities of your association.
– Reasonably evaluated classes educated by the best mentors is the explanation a wide range of associations from Fortune 500 organizations to government's most significant offices return for refreshes in courses and hands-on workshops
– Ratings arranged from understudy criticism post-course assessments show an astonishing 98 percent fulfillment score.
Request more information regarding machine learning seminars 2020. Visit tonex.com for course and workshop detail.
High Value Machine Learning Seminars 2020 - Tonex Training
https://www.tonex.com/machine-learning-seminars/
AI and machine learning. Maybe the most discussed and energizing improvement in the AI field is the advancement of Machine Learning,
an information examination strategy that encourages PCs to do what easily falls into place for people and creatures: gain as a matter of fact.
Machine Learning is extensively characterized into three kinds:
Support learning –
Solo learning —
Managed learning —
Machine Learning Training Courses covers a wide cluster of points including:
The Basics of Machine Learning
Well known Machine Learning Methods
Wording and Principles
Machine Learning Tools and Algorithms
Applied Artificial Intelligence and Machine Learning
Standards of Neural Networks
Introduction to Deep Learning
Our Machine Learning Training Bootcamp is particularly advantageous for occupied experts who need to remain current in their fields yet have restricted opportunity to be away from the workplace.
Learn more.
AI and Machine Learning Training Courses
https://www.tonex.com/it-and-technology-courses/artificial-intelligence-ai-and-machine-learning-ml/
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Travis Cox, Kathy Applebaum, and Kevin McClusky from Inductive Automation will discuss key concepts and best practices, show demos, and answer questions from the audience, to help you start integrating ML into your day-to-day processes.
Learn more about:
• Practical ways to use ML in your factory or facility
• What you'll need to get started
• Existing ML tools and platforms
• And more
Travis Cox, Kathy Applebaum, and Kevin McClusky from Inductive Automation will discuss key concepts and best practices, show demos, and answer questions from the audience, to help you start integrating ML into your day-to-day processes.
Learn more about:
• Practical ways to use ML in your factory or facility
• What you'll need to get started
• Existing ML tools and platforms
• And more
Artificial Intelligence AI Training BootcampBryan Len
Artificial intelligence (AI) discipline includes anything related to making machines smart related to robotics, autonomous driving, IoT or software application. If you are getting them smart, then it’s AI.
Artificial Intelligence Training Bootcamp features the fundamentals of Artificial
Intelligence (AI), Machine Learning, Deep Learning, Neural Networks, Sensor Fusion, and other AI concepts.
Tonex AI Training Formats :
All the participants will work with Artificial Intelligence Tools, AI Programming Tools, Data Science Tools, Advanced Analytics Tools, and Machine and Deep Learning algorithms and methods, AI programming languages and tools to structure intelligent agents, deep learning algorithms, and neural networks.
Course designed for :
AI training bootcamp is a 3 days course mainly designed for
Engineers
Project managers
Analysts and
Anyone else interested in artificial intelligence.
Learn about :
Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression
understanding of key Artificial Intelligence (AI) concepts
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Neural Networks
Learn how Artificial Intelligence uses data and more
Course Outlines :
Core Concepts and Techniques behind Artificial Intelligence (AI)
Data Science Overview
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP)
The Basics of Machine Learning
Learning Applied to AI
Principles of Supervised Algorithms
Principles of Unsupervised Learning
Overview of Algorithms, Tools and Processes
Case Studies and Workshops
Request more information.
Visit tonex.com for course and workshop detail. Browse Tonex online courses. You can take course from home now.
Artificial Intelligence Training Bootcamp | AI Training
https://www.tonex.com/training-courses/artificial-intelligence-training-bootcamp/
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.
Machine learning algorithms are programs that can learn from data and improve from experience without human intervention.
Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or instance-based learning.
Some of the applications beings used right now include:
Virtual personal assistants like Alexa and Siri
Email spam and malware filtering
Search engine result refining
Product recommendations
Online fraud detection
Traffic and weather predictions
Machine Learning for Control Training
Course by Tonex
Machine Learning for Control Training is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI), and the fundamentals of control theory.
Course outline
The Basics of Machine Learning
Popular Machine Learning Methods
Review of Terminology and Principles
Machine Learning Concepts
Introduction to Control
Types of controllers
Frequency-Domain Approach Machine Learning Control Basics
Machine learning for control training
https://www.tonex.com/training-courses/machine-learning-for-control-training/
The Ultimate Machine Learning Career Path for 2022Soumya Suman
This ppt is about a Machine Learning career. It will tell you about the machine learning, reasons to choose machine learning, different roles offered in the area of machine learning, roles and responsibilities of different roles, salaries of machine learning engineers, companies hiring Machine Learning Engineers, and many more things.
MoogleLabs offer the best machine-learning solution to solve a wide range of use cases in multiple industries.ML algorithms have the power to understand, and improve their situational awareness, and build on it. Visit our website to learn more about it.
How to Build a Data Closed-loop Platform for Autonomous Driving?Yu Huang
Introduction;
data driven models for autonomous driving;
cloud computing infrastructure and big data processing;
annotation tools for training data;
large scale model training platform;
model testing and verification;
related machine learning techniques;
Conclusion.
Supervised learning is a fundamental concept in machine learning, where a computer algorithm learns from labeled data to make predictions or decisions. It is a type of machine learning paradigm that involves training a model on a dataset where both the input data and the corresponding desired output (or target) are provided. The goal of supervised learning is to learn a mapping or relationship between inputs and outputs so that the model can make accurate predictions on new, unseen data.v
Overview Of Data Science Course
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
5G AI & Digital Twins Training Course. 5G AI & Digital Twins is a 2 days course presented by Tonex. 5G and AI are the 2 most essential elements that strongly fuel future innovations, and they are inherently synergistic. AI advancements can help improve. Digital Twins: New Technology That Has Doubled in Importance Digital Twins have become doubly important to organizations. Key principles of Digital Twins and how it relates to integration of digital engineering, modeling and simulations, AI/ML, 3D and integration for service and product-related data and systems.
Program Outline:
Fundamentals of 5G
5G Networks, Integration, Migration
Machine Learning for MIMO
Intelligent Deployment
Digital Twin 101
Types of Digital Twins
Digital Twin Market and Industries
Practical Applications
Need help? Get quick response.
Sign up for this course
https://tonex.digital/course/5g-ai-digital-twins/
MBSE (Model Based Systems Engineering) for Dod (Dept of Defense), Digital Engineering Training. MBSE Fundamentals training for DoD (Department of Defense) will help you learn the connection between Digital Engineering (DE) and Model Based Systems Engineering (MBSE). It has really been a focal point for the U.S. Department of Defense (DoD).
Digital Engineering training, MBSE Fundamentals for DoD. Digital Engineering (DE) (also known as model-based engineering or model-based systems engineering) helps to streamline the way defense programs collect, retain, and share data.
Sign up for training.
https://www.tonex.com/training-courses/digital-engineering-training-mbse-fundamentals-for-dod/
https://mbse.one/course/mbse-fundamentals-for-dod/
Engineering Project Management Training, Learn the PMI keys, Project manageme...Tonex
Project management skills for engineers: industry perceptions and implications for engineering project management course.
Learn more. Ask a question.
Engineering Project Management Training
https://www.tonex.com/training-courses/engineering-project-management-training/
Revenue Assurance (RA), Telecom RA Training CourseTonex
Revenue Assurance (RA), Telecom RA Training Course.
Learn how to revenue assurance audit checklist, revenue assurance finance & telecom, revenue assurance function & framework.
Did you know Revenue assurance (RA) telecommunication services, is the use of data quality and process improvement methods that improve profits, revenues and cash flows without influencing demand ?
Request more information. Sign up as a group.
https://www.tonex.com/training-courses/revenue-assurance-training/
Tonex Training offer top notch 5G courses online, onsite and Live online. This is a great opportunity to learn about 5G NR in depth. Business group training is available.
Sign up for "5G Systems Engineering" Training.
https://www.tonex.com/training-courses/5g-wireless-training/
Tonex offers dozens of most essential root cause analysis training, courses and certifications available online, onsite and live online. Sign up as a group.
Request more information.
https://www.tonex.com/systems-engineering-training/root-cause-analysis/
Proposal Engineering Hands-On Workshop, Architecture, Construction and Engine...Tonex
Proposal engineering hands on-training workshop.
Tonex Training offers 3 days hands-on training workshop on “Engineering Business Proposals”. All the participants will have great opportunities to learn about:
What is Proposal Engineering ?
Which professionals need it ?
How to business proposal engineering ?
What is Request for Proposal (RFP) ?
Engineering Proposal Writing facts
Tonex business proposal engineering course
Workshop and case studies.
Does your proposal include project costs, work schedules & informative captions for all graphics ? Learn Proposal engineering mastermind ! Get granted !”
https://www.tonex.com/training-courses/proposal-engineering-workshop-hands-on/
Cyber security hands on-training.
Learn advanced applications of Cyber Security to embedded systems.
It’s all about Cyber and Security.
Proudly presented by: Tonex.Com
Index / Highlights:
Understanding cyber security, risk and action tools.
Integrating Cybersecurity and Enterprise Risk Management (ERM).
What is Secure Embedded Systems ? How does it protect ?
Advance methods & procedure to analyze, reverse, debug ?
Value of risk assessment methodologies, failure analysis ?
How to set up and measure successful mission control system ?
Which professionals need to learn cyber security approaches ?
Case studies and workshop.
Request more information
Sign up for Hands-On Cybersecurity Course
https://www.tonex.com/training-courses/cyber-security-embedded-systems-training-bootcamp-hands-on/
“Do you think many employees already have Bluetooth-enabled devices around the office, so what should IT managers do to ensure they make the most of the technology”?
Is This ? OKAY,
Learn How Bluetooth Can Work For Your Business.
https://www.tonex.com/training-courses/bluetooth-low-energy-training-hands-on-ble-training/
Learn how manufacturers use root cause analysis, rca training for manufacturersTonex
Learn how manufacturers use root cause analysis, rca training for manufacturers.
Index / Highlights
Why is root cause analysis used in manufacturing ?
How manufacturers use root cause analysis ?
What are the benefits RCA comes with ?
Implementing root cause analysis basics
What are the “5 why’s” ?
What is Failure Modes effect analysis (FMEA) ?
What is “Scatter Diagram” ?
What is “Fishbone Diagram” ?
Role of RCA in “6 Sigma” ?
Which professionals should learn ?
RCA for manufacturers : Training structures
Workshops and case studies.
RCA for Manufacturers, Workshop, Case-studies
Interactive Course By: Tonex Training
Request more information
https://www.tonex.com/training-courses/root-cause-analysis-for-manufacturers/
Hands-On MBSE and SysML Training
Presentation Topics/ Highlights:
What is Spacecraft MBSE with SysML ?
Space Systems, SSWG, CubeSats
Why MBSE and SysML are important to NASA ?
Who should attend this course ?
Learning opportunities
Methods of learning
Course topics / Workshops
Join this specialized hands-on training. Request more information. Sign up for course.
Spacecraft Model-based Systems Engineering (MBSE) with SysML
https://www.tonex.com/training-courses/spacecraft-model-based-systems-engineering-mbse-with-sysml-hands-on-mbse-and-sysml-training/
Course by: Tonex.Com
C Programming and Coding Standards, Learn C ProgrammingTonex
C Programming and Coding Standards. It’s all about “C” programming !
“C” is extensively used in the embedded systems software development.
Tonex Training offers “C” Programming and Coding Standards for Safety-Critical Systems. This 2-day practical course is super recommended for software engineers and programmers.
What topics are essential for C coding standards ?
Performing hazard analysis, root cause analysis,
Adopting right safety measures,
Safety-critical ISO 26262 applications,
C-language guidelines,
Software Failure Modes,
Analyzing SIL-2 Level Safety Requirements,
Applying MISRA C Compliance,
Sign up for “C” Programming Training
https://www.tonex.com/training-courses/c-programming-and-coding-standards-for-safety-critical-systems/
Tonex "Software Engineering Training" Top 5 Courses For Cybersecurity Profess...Tonex
Tonex "Software Engineering Training" Top 5 Courses For Cybersecurity Professionals Like You.
Software Engineering is an engineering division or section what relates to the progression of software product. It also use distinct scientific values, techniques, and actions.
Learn Cybersecurity and Software.
Software market analysis
Which professionals are recommended?
How to get started?
Top 5 software engineering courses
For your software skills improvement.
Software Security:
Secure Software Development Training
https://www.tonex.com/training-courses/secure-software-development-training/
Coding Security:
Software Safety Programming, Coding Standards
https://www.tonex.com/training-courses/software-safety-programming-and-software-coding-standards/
Network Security:
Software Engineering Training Bootcamp Course
https://www.tonex.com/training-courses/software-engineering-training-crash-course/
Secure Software Engineering:
Software Engineering For Embedded Systems
https://www.tonex.com/training-courses/software-engineering-for-embedded-systems-training-workshop/
Software Testing:
Software Reliability Testing Training Crash Course
https://www.tonex.com/training-courses/software-reliability-testing-training-crash-course/
Learn more.
https://bit.ly/3nlKsJk
Cyber Security Certificate.
Learn vulnerabilities and risk management through variety of workshops, labs, and in class activities.
Earn Your Professional Certificate in Cybersecurity.
What you will learn :
The foundation and history behind the cyber security.
Different types of threats and attacks
Risk management techniques in networks
The exploits and their different types and consequences
Authentication and password related issues
Enroll To This Course
https://tonex.live/cyber-security-certificate/
https://www.tonex.com/training-courses/cyber-security-certificate-part-1-vulnerabilities-and-risk-management/
Electronic Warfare Threat Modeling and Simulation TrainingTonex
Electronic warfare (EW) is the use of electromagnetic energy or directed energy and integrated network functions to perform military and intelligence missions.
An electronic warfare (EW) system is any configuration of EW technology designed and built to perform military or intelligence missions on one or more air, ground, sea or space platforms.
These configurations usually consist of multiple EW devices and coordinating scalable subsystems, including several subsystems that house multiple devices in a single unit.
The electromagnetic spectrum includes the range of all electromagnetic radiation, including
Radio Waves
Microwaves
Infrared Rays
Ultraviolet Light
Visible Light
X-rays And Gamma Rays
EW are often wont to control the EM spectrum to detect, analyze and track potential threats, making things aware that a rustic and its allies got to prepare defensive measures before each level, diplomatic opinions and offensive plans conflict.
EW has enabled Joint Electromagnetic Spectrum Operations (JEMSO), which allows our forces to use, attack and protect the EM operational environment.
Electronic attacks, protection and support are important electronic warfare functions, including:
Field advantages, including land, air, sea, space, cyberspace
Advantage within the EW
Destroy a given EW related signal
Use radio waves, infrared or lasers to confuse or disable the enemy’s electronic devices
Prevent the receiver from getting stuck
Support and operations to reinforce detection and mitigation.
Create and generate the data needed to disrupt the EW
Radar that collects enemy radio signals or senses incoming missiles
Electronic Warfare Threat Modeling and Simulation Training by Tonex
Electronic warfare threat modeling and simulation training provides modeling and simulation of classic and new threat environments applied to the foundation of electronic warfare (EW).
Learning Objectives
List the basics of modern electronic warfare concepts, architecture and technology
Discuss the application of electronic warfare concepts in ground, air and naval surface warfare
List the function and operational sensitivity of weapon systems to EW
Understand EW application modeling, simulation and network-centric architecture
Describe threat modeling and simulation
And More.
Course Outline:
What is electronic warfare
Overview of EW key concepts
Intelligence, surveillance, and reconnaissance (ISR) threats applied in the new EW environment
Threats of modern and emerging radar systems
Overview of threats to EW functions in the new environment
Threats to electronic warfare capabilities
EW Environment Modeling and Simulation
Learn More:
https://www.tonex.com/training-courses/electronic-warfare-threat-modeling-simulation-training/
Join Business Writing Skills Training and Write clearTonex
Almost all business activities are planned, intentionally, executed and analyzed in written form.
These forms include reports, report summaries, letters, memos and e-mails, and any documents related to business facts. Mutually they are a paper repertoire, recording the proposals, activities and results of numerous business transactions.
4 main types of business writing include:
Description: This writing form provides readers with the information they need to follow the new process at work.
Informational: This type of writing provides readers with reference information and can be used to make decisions in the organization.
Persuasive: Professionals use persuasive writing to attract readers to make specific decisions.
Transactional: Employees use this type of text in daily business communication to share information or get specific responses from colleagues or customers.
Like leadership, most of the people don't have innate writing skills. However, when communicating with others about contracts, recommendations, or other matters involving rock bottom line, having good business writing skills could also be different.
Unfortunately, within the business world, messy emails, incorrectly formatted reports, and rigid, unfriendly content often become the norm.
Writing is a process consisting of several interrelated steps:
Preparation
Research
Organizing
Drafting
Review and Revision
For constructive business writing skills:
Know the audience
Clearly state the purpose
Use concise language
Keeping writing freed from errors
Use active voice
Well organized ideas
State facts rather than opinions
Show confidence
Use simple format
Maintain the ability to adapt to different types of writing
Tonex offers Business Writing Skills Training
Business Writing Skills Training provides you with the simplest practices that business men and ladies got to know so as to write down clear, successful, professional business documents, containing e-mail, memos, letters, and reports. you'll learn a scientific technique of writing that facilitate writing and leads your audience through the content.
Learn About
Styles and formulas to meet different writing requirements
Familiar with powerful opening and closing to attract and maintain attention
Think rationally through obstructive thoughts and build documents through modeling information
Determine the needs and prospects of readers to set goals and priorities
Arrange opinions and generate content .
How to edit and proofread the final version
And More.
Course Outline:
Business Writing Skills Overview
Constructing Your Documents
Writing Reports To Consider Business Issues
Highlighting Benefits to the Audience
How to Persuade Your Readers
Communicating What You Really Mean via E-mail
TONEX Hands-On Workshop Sample
Learn More:
https://www.tonex.com/training-courses/business-writing-skills-training/
Conflict Management Training, Learn The Causes Of ConflictTonex
A workforce full of conflicts may be under extreme pressure, and high levels of pressure can cause conflicts. Both need to be managed to make the workplace function effectively.
Conflicts rarely resolve on their own-in fact, conflicts usually escalate if they are not proactively handled appropriately. This is why leaders need to make plans to deal with inevitable conflicts.
The fact is that leadership and conflict go hand in hand. Leadership is a full-contact sport, and if you cannot or will not resolve conflicts in a healthy and productive way, you should not assume a leadership role.
Conflict management is a practice that can identify and handle conflicts wisely, fairly and effectively.
At present, conflict management has been recognized as a key skill for successfully managing employees, especially considering the competitive nature of employees competing for recognition in typical business structures.
The purpose of conflict management is to enhance learning and group outcomes, including effectiveness or performance in the organizational environment.
Ineffective conflict management makes companies suffer money, inhibits action and hinders innovation.
Effects of Good Conflict Management Skills
Improved Relationships
Increased Productivity
Improved Communication
Decrease Turnover
Reduce Absenteeism
Increase Employee Engagement
Conflict Management Training Course by Tonex
Conflict Management Training is a workshop style training program where participants learn the causes of conflict and how to prevent issues before they escalate.
Conflict management training produces key life skills that can be used in and outside the workplace.
In addition, participating in conflict is an important leadership skill, and employees seeking training and experience in this area may have better opportunities for promotion within the organization.
Participants will also learn about stress management techniques and how to become more aware of conflict and its impact on the workplace.
In turn, this can help employees focus on productivity, thereby improving interpersonal and communication skills in the office.
Learning Objectives
Classify types of conflict and their causes.
Talk about and implement the conflict resolution process.
Recognize conflicts and how to prevent an issue before it escalates.
Expand communications skills and techniques.
Apply stress management techniques.
Training Outline
Overview of Conflict Management
Consequences of Conflict
Positive Results for the Group, Negative Results
Conflict Outcomes
Emotions in Conflicts
Principals of Conflict Resolution
Conflict Resolution Process
Conflict Reduction
Negotiation
Workshop
Learn More:
https://www.tonex.com/training-courses/conflict-management-training/
Big data is a term that describes the huge amount of data (structured and unstructured) that floods the enterprise every day.
Big Data includes the quantity of data , the speed or speed at which it's created and picked up , and therefore the variety or scope of the info points being covered. It very often comes from several sources and arrives in multiple formats.
From the perspective of a project manager or project manager, big data does not necessarily revolve around the amount of data that individuals and companies deal with. Data can be obtained from any source and analyzed to find the answer for the following purposes:
Reduce the time cut costs
Wise decision
Optimized product
New products development
Your present project management and soft skills are likely ultimate for establishing the framework for a replacement or existing Big Data project team and their projects. you only got to enhance the talents and knowledge you have already got .
This is where Tonex training can help.
Tonex Offers Big Data for Project and Program Managers Training
participants will find out how to profit from big data in their projects and programs
Why does one Need This Training?
Need project managers with big data expertise and business awareness
Must have expert judgment ability to use technology
The plan manager should assist in expanding and coordinating tasks throughout the project
Audience
Project managers
Program managers
Big data analytics
Decision makers of organizations
Strategic leaders
Executives
Training Objectives
Describe the big data analytics
Explain the business values of massive data
Talk about the opportunities and challenges of using big data
Choose if big data analytics serve their client’s interest, situation and knowledge
Manage data analytic projects
Assess risks related to the large data
Distinguish between a knowledge analytic project and a fishing expedition
Decide the best approach
Conclude the time to stop the analysis
Talk about how project management can be used to sustain your data analytics capability
Elaborate how big data can be used to secure the progress of the project
Identify what analytics should be implemented
Course Outline:
Overview to Big Data and Project/Program Management
Project Management Process
Where Does Big Data Analytics expertise is Required?
Introduction to Big Data Management
Big Data Challenges
The Status of Big Data Management
Data Science Methods
Technical Practices for Big Data Management
Analytic Exercises and Big Data Management
Applicable Programming Languages
Corporation Practices for Big Data Management
Top Priorities of Big Data Management
Choosing the Best Strategy
Organizational Leadership
Tonex Hands-On Sample Workshop
Learn More:
https://www.tonex.com/training-courses/big-data-project-program-managers-training/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Knowledge engineering: from people to machines and back
Machine Learning Training Bootcamp
1. Call Us Today: +1-972-665-9786
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
TAKE THIS COURSE
Since 1993, Tonex has specialized in providing industry-leading training, courses, seminars,
workshops, and consulting services. Fortune 500 companies certified.
Machine Learning
Training Bootcamp
2. Since 1993, Tonex has specialized in providing industry-leading training,
courses, seminars, workshops, and consulting services.
Why Tonex ?
--Presenting highly customized learning solutions is what we do. For over 30
years TONEX has worked with organizations in improving their
understanding and capabilities in topics often with new development,
design, optimization, regulations and compliances that, frankly, can be
difficult to comprehend.
--Ratings tabulated from student feedback post-course evaluations show an
amazing 98 percent satisfaction score.
--Reasonably priced classes taught by the best trainers is the reason all kinds
of organizations from Fortune 500 companies to government’s most
important agencies return for updates in courses and hands-on workshops
3. Machine learning & artificial intelligence. Machine
learning is playing an increasingly important role
in computing and artificial intelligence. Suits any
article on AI, algorithms, machine learning,
quantum computing, artificial intelligence.
Machine learning training bootcamp is a 3-day
technical training course that covers the
fundamentals of machine learning, a form and
application of artificial intelligence (AI).
Attendees will learn, comprehend and master
ideas on machine learning concepts, key
principles, techniques including: supervised and
unsupervised learning, mathematical and
heuristic aspects, modeling to develop algorithms,
prediction, linear regression, clustering,
classification, and prediction.
TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
4. TAKE THIS COURSEMachine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Machine Learning
Training Bootcamp
5. TAKE THIS COURSE
Learn differences and similarities between Machine Learning, Artificial Intelligence, Deep
Learning, Data Mining and Data Warehouse. Artificial Intelligence uses models built by Machine
Learning to create intelligent behavior applied to businesses, marketing and sales, operations,
autonomous cars, games and industrial automation by prediction based on rules and using
programming languages and algorithms.
Machine learning based on artificial intelligence provides the ability to learn about newer data
sets without being programmed explicitly using methods of data analysis. Machine Learning takes
advantages of Data Mining techniques, statistics, other key principles and learning algorithms to
build models to predict future outcomes. Math and programming are the basis for many of the
machine learning algorithms. Using machine learning as a tool, the machine must automatically
learn the parameters of models from the data. Using larger datasets, better accuracy and
performance is achieved.
Machine learning and data mining can use the same key algorithms to discover patterns in your
data and dataset. In machine learning, the computers, machines and IoT devices must
automatically learn the parameters of models from the data using self-learning algorithms to
reveal insights and provide feedback in near real-time.
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
6. TAKE THIS COURSE
Machine learning, for example, can be used in proactive maintenance to continuously monitor
the performance of simple or complex industrial systems, applications and events. Using the
ability to learn and adapt, makes it the optimal choice for improvements in ongoing processes,
and to automatically predict and prevent failures.
Learn how Machine Learning can automatically process and analyze huge volumes of complex
data. Machine learning powers innovative automated technologies such as recommendation
engines, facial recognition, financial losses from stock market and bonds, fraud protection, self-
driving autonomous cars, robotics, industrial automation and future applications.
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
7. TAKE THIS COURSEMachine Learning Training Bootcamp
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Machine Learning
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Learning Objectives
• Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
• List similarities and differences between AI, Machine Learning and Data Mining
• Learn how Artificial Intelligence uses data to offer solutions to existing problems
• Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn.
• Clarify how Data Mining can serve as foundation for AI and machine learning.
• List the various applications of machine learning and related algorithms
• Learn how to classify the types of learning such as supervised and unsupervised learning
• Implement supervised learning techniques such as linear and logistic regression
• Use unsupervised learning algorithms including deep learning, clustering , etc.
• Learn about classification data and Machine Learning models
• Select the best algorithms applied to Machine Learning
• Make accurate predictions and analysis to effectively solve potential problems
• List Machine Learning concepts, principles, algorithms, tools and applications
• Learn the concepts and operation of support neural networks, vector machines, kernel SVM,
naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest
neighbors, K-means and clustering
• Comprehend the theoretical concepts and how they relate to the practical aspects
• Be able to model a wide variety of robust machine learning algorithms
Machine Learning Training Bootcamp
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Course Agenda and Topics:
• The Basics of Machine Learning
• Machine Learning Techniques, Tools and Algorithms
• Data and Data Science
• Review of Terminology and Principles
• Applied Artificial Intelligence (AI) and Machine Learning
• Popular Machine Learning Methods
• Learning Applied to Machine Learning
• Principal Component Analysis
• Principles of Supervised Machine Learning Algorithms
• Principles of Unsupervised Machine Learning
• Regression Applied to Machines Learning
• Principles of Neural Networks
• Large Scale Machine Learning
• Introduction to Deep Learning
• Applying Machine Learning
• Overview of Algorithms
• Overview of Tools and Processes
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Machine Learning
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The Basics of Machine Learning:
• What is Machine Learning?
• Emergence and applications of Artificial Intelligence and Machine Learning
• Basics of Artificial Intelligence
• Basics of Machine Learning
• Basics of Data Mining
• Data Mining versus Machine Learning versus Data Science
• Data Mining and patterns
• Why is machine learning important?
• Creating good machine learning systems
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Machine Learning Techniques, Tools and Algorithms:
• Supervised, unsupervised, semi supervised and reinforcement learning
• Basic tools and ideas in Machine Learning
• Supervised Machine Learning problems and solutions
• Supervised Machine Learning tasks subgroups: regression and classification
• Unsupervised Machine Learning
• Unsupervised tasks and generative modelling
• Reinforcement Learning, Hybrids and Beyond
• Data preparation capabilities
• Techniques of Machine Learning
• Polynomial regression
• Linear regression
• Random forest
• Decision tree regression
• Gradient descent and regularization
• Classification
• Logistic regression
• K-nearest neighbors
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• Support vector machines
• Naive Bayes
• Kernel support vector machines
• Decision tree classifier
• Random forest classifier
• Clustering algorithms
• K-means clustering
• Bias and variance trade-off
• Representation learning
• Data Preprocessing
• Data preparation
• Feature engineering and scaling
• Data and Datasets
• Dimensionality reduction
Machine Learning Training Bootcamp
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Machine Learning
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Data and Data Science:
• Principles of Data science
• Programming, logical reasoning, mathematics and statistics
• Data Engineering versus Data Science
• Time series comparison
• Neural Networks
• Steps to Machine Learning
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Review of Terminology and Principles:
• Math Refresher
• Concepts of linear algebra
• Probability and statistics
• Algorithms
• Automation and iterative processes
• Scalability
• Ensemble modeling
• Framing
• Generalization
• Machine Learning methods
• Classification
• Training and Training Set
• Validation
• Representation
• Regularization
• Logistic Regressions
• Neutral Nets
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• Multi class Neutral Nets
• Embeddings
• Basic Algebra and Calculus
• Basic Python
• Chain rule
• Concept of a derivative
• Gradient or slope
• Linear algebra
• Logarithms, and logarithmic equations
• Matrix multiplication
• Mean, median, outliers and standard deviation
• Partial derivatives
• Sigmoid function
• Statistics
• Tanh
• Tensor and tensor rank
• Trigonometry
• Variables, coefficients, and functions
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Applied Artificial Intelligence (AI) and Machine Learning:
• Machine Learning prediction with models
• Artificial Intelligence behaving and reasoning
• Applications of Machine Learning
• Machine Learning algorithms
• Models
• Techniques
• Statistics and Math
• Algorithms
• Programming
• Patterns and Prediction
• Intelligent Behavior
• Statistics quantifies numbers
• Machine learning generalizing information from large data sets
• Principles to detect and extrapolate patterns
• Machine Learning System Analysis and Design
• Support Vector Machines
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Machine Learning
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Popular Machine Learning Methods:
• Supervised learning and unsupervised learning
• Supervised learning algorithms and labeled data
• Trained using labeled examples
• Classification, regression, prediction and gradient boosting
• Supervised learning and patterns
• Predicting the values of the label on additional unlabeled data
• Using historical data to predict likely future events
• Unsupervised learning and unlabeled data
• Unsupervised learning against data that has no historical labels
• Semi supervised learning
• Using both labeled and unlabeled data for training
• Classification, regression and prediction
• Reinforcement learning
• Robotics, gaming and navigation
• Discovery through trial and error
• The agent (the learner or decision maker)
• The environment (everything the agent interacts with)
• Actions (what the agent can do)
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Learning Applied to Machine Learning:
• Application of Supervised versus Unsupervised Learning
• Case Study: credit card transactions as fraudulent charges
• Self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value
decomposition
• Face recognition
Principal Component Analysis:
• Anomaly detection
• Deep learning
• Neural networks
• Learning with deep neural networks
• Deep neural networks and hidden layers and multiple types of hierarchies
• Deep learning as a type of machine learning
• Regularization
• Machine learning models need to generalize well to new examples that the model has not
seen in practice.
• Tools to prevent models from overfitting the training data.
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Machine Learning
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Principles of Supervised Machine Learning Algorithms:
• Machine Learning algorithms mind map
• What is supervised machine learning?
• How does it relate to unsupervised machine learning?
• Classification and regression supervised learning problems
• Clustering and association unsupervised learning problems
• Algorithms used for supervised and unsupervised problems
• Supervised Machine Learning as a majority of practical machine learning
• Supervised learning problems grouping into regression and classification problems
• Principles of “Classification”
• Principles of “Regression”
• Popular examples of supervised machine learning algorithms
• Linear regression for regression problems
• Random forest for classification and regression problems
• Support vector machines for classification problems
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Principles of Unsupervised Machine Learning:
• The goal for unsupervised learning
• Modeling the underlying structure or distribution in the data
• Ways to learn more about the data
• Algorithms to discover and present the interesting structure in the data
• Unsupervised learning problems grouping into clustering and association problems
• Principles of “Clustering”
• Ways to discover the inherent groupings in the data
• Principles of “Association”
• Ways to discover rules that describe large portions of your data
• Examples of unsupervised learning algorithms
• K-means for clustering problems
• Apriori algorithm for association rule learning problems
• Semi-Supervised Machine Learning
• Unlabeled data and a mixture of supervised and unsupervised techniques
• Collecting and storing unlabeled data
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Regression Applied to Machines Learning:
• Linear Regression with One Variable
• Application of linear regression
• Method for learning
• Linear Algebra Review
• Refresher on linear algebra concepts
• Models with multiple variables
• Linear Regression with Multiple Variables
• Implement the learning algorithms in practice
• Logistic Regression
• Logistic regression is a method for classifying data into discrete outcomes
• Logistic regression to classify a credit card transaction as fraud or not fraud
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Principles of Neural Networks:
• Neural Networks Representation
• Principles behind neural networks and models
• Neural Networks Learning
• Backpropagation algorithm
• Learn parameters for a neural network.
• Implementing your own neural network for credit card fraud
• Advice for Applying Machine Learning
• Best practices for applying machine learning in practice
• Best ways to evaluate performance of the learned models
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Large Scale Machine Learning:
• Real-world case studies
• Interactive visualizations of algorithms in action
• Pattern Recognition
• Accuracy
• Case Study: Marketing Campaign
• Working with Regression
• Prediction
• Classification
• Logistic Regression
• Unsupervised Learning with Clustering
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Introduction to Deep Learning:
• Principles of Deep Learning
• Artificial Neural Networks
• TensorFlow
• Learning complicated patterns in large amounts of data
• Identifying objects in images and words in sounds
• Automatic language translation
• Medical diagnoses
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Applying Machine Learning:
• Applying machine learning to IoT
• Financial services
• DoD
• Government
• Health care
• Marketing and sales
• Oil and gas
• Renewable Energy
• Transportation
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Overview of Algorithms:
• Associations and sequence discovery
• Bayesian networks
• Decision trees
• Expectation maximization
• Gaussian mixture models
• Gradient boosting and bagging
• Kernel density estimation
• K-means clustering
• Local search optimization techniques
• Multivariate adaptive regression splines
• Nearest-neighbor mapping
• Neural networks
• Principal component analysis
• Random forests
• Self-organizing maps
• Sequential covering rule building
• Singular value decomposition
• Support vector machines
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Overview of Tools and Processes:
• Comprehensive data quality and management
• GUIs for building models and process flows
• Interactive data exploration
• Visualization of model results
• Comparisons of different machine learning models
• Identify the best machine learning models
• Automated ensemble model evaluation
• Repeatable and reliable results
• Integrated, end-to-end platforms to automate data-to-decision process
• Exploratory Data Analysis with R
• Loading, querying and manipulating data in R
• Cleaning raw data for modeling
• Reducing dimensions with Principal Component Analysis
• Identifying outliers in data
• Working with Unstructured Data
• Mining unstructured data
• Constructing recommendation engines
• Machine learning with neural networks
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Machine Learning
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