This presentation targets people with a beginner background of Artificial Intelligence and Machine Learning, so anyone interested in understanding the working of the machine learning model and how to implement them in general is welcome to join us.
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.
Top Machine Learning Tools and Frameworks for Beginners | EdurekaEdureka!
YouTube Link: https://youtu.be/v0uVu5__JGg
** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka PPT will provide you with a list of Machine Learning tools and Frameworks that one must know about.
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/
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LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Philip Rathle- Graph Boosted Artificial IntelligenceNeo4j
With AI's renaissance, consideration for how we operationalize these technologies ought to remain top of mind. This talk will discuss the intersection of graph theory, databases, and machine learning. Including how graphs can help us:
* Discover the less than obvious knowledge in context-rich knowledge graphs
* Interact with our AI/ML models in an intuitive visual fashion
* Extract complex features more reliably and more accurately
* Create a flexible system of record for AL/ML applications
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
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.
Top Machine Learning Tools and Frameworks for Beginners | EdurekaEdureka!
YouTube Link: https://youtu.be/v0uVu5__JGg
** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka PPT will provide you with a list of Machine Learning tools and Frameworks that one must know about.
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
Philip Rathle- Graph Boosted Artificial IntelligenceNeo4j
With AI's renaissance, consideration for how we operationalize these technologies ought to remain top of mind. This talk will discuss the intersection of graph theory, databases, and machine learning. Including how graphs can help us:
* Discover the less than obvious knowledge in context-rich knowledge graphs
* Interact with our AI/ML models in an intuitive visual fashion
* Extract complex features more reliably and more accurately
* Create a flexible system of record for AL/ML applications
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
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/
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.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
Wolfram Alpha (also styled Wolfram Alpha and Wolfram Alpha) is a computational knowledge engine or answer engine developed by Wolfram Research. It is an online service that answers factual queries directly by computing the answer from externally sourced "curated data", rather than providing a list of documents or web pages that might contain the answer as a search engine might.
Unleashing the Power of Machine Learning Prototyping Using Azure AutoML and P...Luca Zavarella
This session will show how to quickly implement a Machine Learning model using Azure Automated ML and the Python SDK. In addition, the new toolkits developed by Microsoft that allow to easily evaluate both the performance of the prototyped model and to explain its behavior to executives and stakeholders will be shown during the demo.
(https://datasaturdays.com/events/datasaturday0001.html)
How to use 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
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/
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.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
Wolfram Alpha (also styled Wolfram Alpha and Wolfram Alpha) is a computational knowledge engine or answer engine developed by Wolfram Research. It is an online service that answers factual queries directly by computing the answer from externally sourced "curated data", rather than providing a list of documents or web pages that might contain the answer as a search engine might.
Unleashing the Power of Machine Learning Prototyping Using Azure AutoML and P...Luca Zavarella
This session will show how to quickly implement a Machine Learning model using Azure Automated ML and the Python SDK. In addition, the new toolkits developed by Microsoft that allow to easily evaluate both the performance of the prototyped model and to explain its behavior to executives and stakeholders will be shown during the demo.
(https://datasaturdays.com/events/datasaturday0001.html)
How to use 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
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
Slide about working of federated learning and the introduction of machine learning and how user privacy is preserved in future machine learning approach.
Building Your Dream Machine Learning Team with Python Expertiseriyak40
Building a proficient team adept in technical skills, domain expertise, and robust communication is vital in revolutionizing your industry. This ensures effective utilization of Python's machine-learning capabilities and the realization of project ideas through meticulous planning.
POPULAR MACHINE LEARNING SOFTWARE TOOLSrahul804591
The current world and activities are highly dependent on technology and its various devices. In this technological era, one can find it extremely normal for us to come across certain tech terms for instance Digital Marketing, Artificial Intelligence, Python, Machine Learning and many more. Here, we will be focusing on Machine Learning plus its interesting productive tools.
visit us :- https://kvch.in/best-machine-learning-training-noida
GenerativeAI and Automation - IEEE ACSOS 2023.pptxAllen Chan
Generative AI has been rapidly evolving, enabling different and more sophisticated interactions with Large Language Models (LLMs) like those available in IBM watsonx.ai or Meta Llama2. In this session, we will take a use case based approach to look at how we can leverage LLMs together with existing automation technologies like Workflow, Content Management, and Decisions to enable new solutions.
[DevDay2019] Hands-on Machine Learning on Google Cloud Platform - By Thanh Le...DevDay.org
By recent release on Google Cloud Platform, Google focus on the era of AI/ML technological change, it lets us bring the powerful machine learning features to the mobile application whether it is for Android/iOS and whether experienced/beginner machine learning developer. The purpose of this topic is to share our use case on how to make your model as serving by bringing it to the cloud.
Top Artificial Intelligence Tools & Frameworks in 2023.pdfYamuna5
Artificial intelligence has facilitated the processing and use of data in the business world. With the growth of AI and ML, data scientists and developers now have more AI tools and frameworks to work with. We believe it's important for machine learning platforms to be easy to use for business people who need results, but also powerful enough for technical teams who want to push the boundaries of data analysis with customizable extensions. The key to success is choosing the right AI framework or machine learning library.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
10. Machine Learning
What Why
How
“The goal of machine
learning is to build computer
systems that can adapt and
learn from their experience.”
• Economically efficient.
• consider larger data.
• Can formalize learning
problem to explicitly
identify goals and criteria.
When
14. The Prosses of Machine Learning
Data
Preparation
Feature
Engineering
Data
Modeling
Performance
Measure
1 2 3 4
DATA ALGORITHMS MODEL
Performance
Improvement
5
22. Reinforcement Learning
Reinforcement learning: the agent that acts on its environment, it receives some evaluation of
its action (reinforcement), but is not told of which action is the correct one to achieve its goal
29. TensorFlow
TensorFlow is an open source framework developed by researchers to run machine
learning, deep learning and other statistical and predictive analytics workloads.
TensorFlow
Architecture
Preprocessing the
data
Build the model
Train and estimate
the model
30.
31. Register in Quick Lab
01
Start AI Platform Lab
02
Follow the instructions
03
YOUR
MODEL IS
READY
04
How?
