Top 10 Applications of Machine Learning | Machine Learning Application Examples | Edureka
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** Machine Learning Training with Python: https://www.edureka.co/python ** This "Top 10 Applications of Machine Learning" PPT will give you an idea of how vast the machine learning is and how commonly you are using it in your day to day life. Check out our playlist for more videos: http://bit.ly/2taym8X
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Machine Learning
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
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The document is an agenda for a seminar on machine learning techniques and tools. It will cover an introduction to machine learning, common techniques like classification, clustering and regression. It will also discuss tools for machine learning like Apache Mahout, Weka, Spark MLLib and R. Finally, it will include a hands-on demonstration of machine learning algorithms and discuss benefits of using machine learning.
Introduction to Machine Learning
Here are the key calculations:
1) Probability that persons p and q will be at the same hotel on a given day d is 1/100 × 1/100 × 10-5 = 10-9, since there are 100 hotels and each person stays in a hotel with probability 10-5 on any given day.
2) Probability that p and q will be at the same hotel on given days d1 and d2 is (10-9) × (10-9) = 10-18, since the events are independent.
Machine Learning
This is the presentation of Machine learning.Showing the advantage of machine learning and brief introduction of types of machine learning.
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This document provides an introduction to machine learning. It discusses how machine learning allows computers to learn from experience to improve their performance on tasks. Supervised learning is described, where the goal is to learn a function that maps inputs to outputs from a labeled dataset. Cross-validation techniques like the test set method, leave-one-out cross-validation, and k-fold cross-validation are introduced to evaluate model performance without overfitting. Applications of machine learning like medical diagnosis, recommendation systems, and autonomous driving are briefly outlined.
Machine Learning and its Applications
Machine learning and its applications was a gentle introduction to machine learning presented by Dr. Ganesh Neelakanta Iyer. The presentation covered an introduction to machine learning, different types of machine learning problems including classification, regression, and clustering. It also provided examples of applications of machine learning at companies like Facebook, Google, and McDonald's. The presentation concluded with discussing the general machine learning framework and steps involved in working with machine learning problems.
Machine learning
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
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This document introduces machine learning concepts through a webinar presentation. It begins with introductions and definitions of machine learning from Wikipedia and O'Reilly. It then provides examples of artificial intelligence and machine learning applications. The main machine learning concepts covered include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is described as learning from labeled examples, while unsupervised learning finds patterns in unlabeled data. Reinforcement learning involves an agent interacting with an environment and receiving rewards or punishments to achieve goals. Examples of reinforcement learning applications include autonomous vehicles and game playing agents. In closing, the presenter thanks college administrators and attendees for their participation.
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Machine Learning
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
Machine learning
The document is an agenda for a seminar on machine learning techniques and tools. It will cover an introduction to machine learning, common techniques like classification, clustering and regression. It will also discuss tools for machine learning like Apache Mahout, Weka, Spark MLLib and R. Finally, it will include a hands-on demonstration of machine learning algorithms and discuss benefits of using machine learning.
Introduction to Machine Learning
Here are the key calculations:
1) Probability that persons p and q will be at the same hotel on a given day d is 1/100 × 1/100 × 10-5 = 10-9, since there are 100 hotels and each person stays in a hotel with probability 10-5 on any given day.
2) Probability that p and q will be at the same hotel on given days d1 and d2 is (10-9) × (10-9) = 10-18, since the events are independent.
Machine Learning
This is the presentation of Machine learning.Showing the advantage of machine learning and brief introduction of types of machine learning.
An introduction to Machine Learning
This document provides an introduction to machine learning. It discusses how machine learning allows computers to learn from experience to improve their performance on tasks. Supervised learning is described, where the goal is to learn a function that maps inputs to outputs from a labeled dataset. Cross-validation techniques like the test set method, leave-one-out cross-validation, and k-fold cross-validation are introduced to evaluate model performance without overfitting. Applications of machine learning like medical diagnosis, recommendation systems, and autonomous driving are briefly outlined.
Machine Learning and its Applications
Machine learning and its applications was a gentle introduction to machine learning presented by Dr. Ganesh Neelakanta Iyer. The presentation covered an introduction to machine learning, different types of machine learning problems including classification, regression, and clustering. It also provided examples of applications of machine learning at companies like Facebook, Google, and McDonald's. The presentation concluded with discussing the general machine learning framework and steps involved in working with machine learning problems.
Machine learning
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
Introduction to Machine Learning
This document introduces machine learning concepts through a webinar presentation. It begins with introductions and definitions of machine learning from Wikipedia and O'Reilly. It then provides examples of artificial intelligence and machine learning applications. The main machine learning concepts covered include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is described as learning from labeled examples, while unsupervised learning finds patterns in unlabeled data. Reinforcement learning involves an agent interacting with an environment and receiving rewards or punishments to achieve goals. Examples of reinforcement learning applications include autonomous vehicles and game playing agents. In closing, the presenter thanks college administrators and attendees for their participation.
Machine learning
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
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Machine Learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed. It uses algorithms to recognize patterns in data and make predictions. The document discusses common machine learning algorithms like linear regression, logistic regression, decision trees, and k-means clustering. It also provides examples of machine learning applications such as face detection, speech recognition, fraud detection, and smart cars. Machine learning is expected to have an increasingly important role in the future.
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- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
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The document describes a machine learning certification training offered by Edureka. It covers topics like introduction to data science, machine learning applications, types of machine learning including supervised, unsupervised and reinforcement learning. For supervised learning, it discusses algorithms like linear regression, logistic regression, decision trees, random forest and Naive Bayes classifier. It also explains machine learning life cycle and concepts like model fitting, clustering and applications of machine learning.
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The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
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2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
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YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | Edureka
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | Edureka
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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Python Programming Tutorial | Edureka
YouTube Link:https://youtu.be/CVv8zhYEjUE
Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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Top 5 PMP Certifications | Edureka
YouTube Link:https://youtu.be/LvgqSMlIXFs
Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | Edureka
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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Linux Mint Tutorial | Edureka
YouTube Link: https://youtu.be/xHUiYEIcY_I
** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| Edureka
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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Importance of Digital Marketing | Edureka
YouTube Link:https://youtu.be/phPCkkWT76k
*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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RPA in 2020 | Edureka
YouTube Link: https://youtu.be/R132INtDg9k
** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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Email Notifications in Jenkins | Edureka
YouTube Link: https://youtu.be/mb8WOHejlT8
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | Edureka
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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Cognitive AI Tutorial | Edureka
Cognitive computing aims to mimic human reasoning and behavior to solve complex problems. It works by simulating human thought processes through adaptive, interactive, iterative and contextual means. Cognitive computing supplements human decision making in sectors like customer service and healthcare, while artificial intelligence focuses more on autonomous decision making with applications in finance, security and more. A use case of cognitive AI is using it to assess skills, find relevant jobs, negotiate pay, suggest career paths and provide salary comparisons and job openings to help humans.
AWS Cloud Practitioner Tutorial | Edureka
YouTube Link: https://youtu.be/0djPrlaxx_U
Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | Edureka
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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Big Data on AWS Tutorial | Edureka
YouTube Link: https://youtu.be/ge4qhkl9uKg
AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | Edureka
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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Introduction to DevOps | Edureka
YouTube Link: https://youtu.be/GJQ36pIYbic
DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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- 4. Faster Route Selection ▪ Google Maps - THE app which we use every time we go out ▪ Despite of the usual traffic, you are on the fastest route ▪ Everyone who is using the Google Maps is contributing in making the app more accurate
- 5. Auto friend tagging suggestion
- 6. Auto friend tagging suggestion ▪ Facebook suggests if you wan to tag the person in the pic ▪ Uses Facebook's face detection and recognition algorithms ▪ Based on the project “Deepface”
- 7. Auto friend tagging suggestion
- 8. Alt Tags
- 9. Alt Tags
- 10. Alt Tags
- 12. DATA SCIENCE TRAINING www.edureka.co/data-science Machine Learning at Uber ▪ Personalized Application ▪ Estimated Time of Arrival
- 14. Ads Recommendation ▪ Recommends ads based on your search history ▪ Machine Learning is used in generating recommendation ▪ 35 % of Amazon’s revenue is generated by its recommendation system
- 16. Machine Learning at Apple ▪ Smartphone with facial recognition ▪ Core of the face detection - machine learning algorithms ▪ Powerful Apple A11 Chip
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- 20. ▪ How does Netflix generates a list of movies similar to your interest? ▪ 75 % of users selects movies based on Netflix’s recommendation Recommender System
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- 25. ▪ Language is no more a barrier! ▪ Free, fast, and providing almost accurate translation ▪ Real-time translation using Camera
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