We may earn an affiliate commission when you visit our partners.
Michael Littman,
Charles Isbell,
and
Pushkar Kolhe
Take Udacity's Machine Learning course and learn how to apply Supervised, Unsupervised and Reinforcement Learning techniques to data science problems. Learn online with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/0mdpy2/udacity
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Use code at checkout.
Valid for a limited time only
Bootcamp level quality
Complete hands-on projects and get personalized feedback with Udacity.
Take
40%
off
UDACITY40
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
ML is the ROX
SL 1 - Decision Trees
SL 2 - Regression & Classification
SL 3 - Neural Networks
Read more
Syllabus
ML is the ROX
SL 1 - Decision Trees
SL 2 - Regression & Classification
SL 3 - Neural Networks
Read more
SL 4 - Instance Based Learning
SL 5 - Ensemble B&B
SL 6 - Kernel Methods & SVMs
SL 7 - Comp Learning Theory
SL 8 - VC Dimensions
SL 9 - Bayesian Learning
SL 10 - Bayesian Inference
UL 1 - Randomized Optimization
UL 2 - Clustering
UL 3 - Feature Selection
UL 4 - Feature Transformation
UL 5 - Info Theory
RL 1 - Markov Decision Processes
RL 2 - Reinforcement Learning
RL 3 - Game Theory
RL 4 - Game Theory Continued
Outro
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores Machine Learning (ML), which is standard in industry
Taught by Michael Littman, Charles Isbell, and Pushkar Kolhe, who are recognized for their work in Machine Learning
Covers Supervised, Unsupervised, and Reinforcement Learning techniques, which are core skills for data science
Includes Random Optimization, Clustering, and Game Theory, which are more complex ML techniques
Offers quizzes and exercises that help learners apply ML concepts to real-world data
May be challenging for learners new to programming or data science
Save this course
Save Machine Learning to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning with these
activities:
Gather Machine Learning Resources
Show steps
This course covers a comprehensive overview of how to apply machine learning to solve real-world problems.
Browse courses on
Machine Learning Tools
Show steps
-
Create a spreadsheet or document to track resources.
-
Search for resources on topics like machine learning algorithms, data preprocessing, and model evaluation.
-
Organize resources by topic and level of difficulty.
Seek Guidance from Experienced Machine Learning Practitioners
Show steps
Connect with experts in the field to gain insights, receive feedback, and expand your network, which can be invaluable for your learning journey.
Show steps
-
Attend industry events and conferences.
-
Reach out to machine learning professionals on LinkedIn.
-
Ask for introductions from friends or colleagues.
-
Follow thought leaders on social media and engage with their content.
Review Basic Probability and Linear Algebra
Show steps
This course makes extensive use of linear algebra and probability. Refreshing these topics is likely to improve learning outcomes.
Browse courses on
Probability
Show steps
-
Review the concepts of probability, including Bayes' theorem and conditional probability.
-
Practice solving linear algebra problems, such as matrix multiplication and finding eigenvalues.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Organize and Review Course Materials
Show steps
Build a strong foundation by ensuring your course materials are well-organized and easily accessible, allowing for efficient review and retention.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Organize the materials by topic or module.
-
Review the materials regularly, taking notes or highlighting important concepts.
-
Summarize key points and create flashcards for quick recall.
Review Supervised Learning Basics with Andrew Ng's Coursera Course
Show steps
Understand the foundations of supervised learning, including linear regression, logistic regression, and decision trees, for a stronger understanding of the material covered in this course.
Browse courses on
Supervised Learning
Show steps
-
Enroll in Andrew Ng's Machine Learning course on Coursera.
-
Complete the first three modules of the course, which cover supervised learning concepts.
-
Take the quizzes and complete the programming assignments in each module.
-
Review the course materials and make notes for future reference.
Solve Machine Learning Coding Challenges
Show steps
This course covers a lot of coding techniques, so it will be helpful to practice coding regularly.
Browse courses on
Coding Challenges
Show steps
-
Find a website or platform that offers machine learning coding challenges.
-
Start solving coding challenges, starting with easy ones and gradually moving to more difficult ones.
-
Review your solutions and identify areas for improvement.
Solve Practice Problems on Kaggle for Supervised and Unsupervised Learning
Show steps
Enhance your problem-solving skills and apply your knowledge of supervised and unsupervised learning to real-world datasets, improving your ability to apply these techniques effectively.
Browse courses on
Supervised Learning
Show steps
-
Create an account on Kaggle.
-
Select a supervised learning competition, such as the Titanic: Machine Learning from Disaster dataset.
-
Download the dataset and explore it.
-
Build and train a supervised learning model using the dataset.
-
Submit your predictions and track your progress against other competitors.
Follow Machine Learning Tutorials
Show steps
This course covers a wide range of machine learning, so it will be helpful to supplement with tutorials on specific topics.
Show steps
-
Find a reputable source of machine learning tutorials.
-
Choose a tutorial that covers a topic you are interested in or struggling with.
-
Follow the tutorial step-by-step and complete the exercises.
Follow a TensorFlow Tutorial on Convolutional Neural Networks
Show steps
Advance your skills by learning about convolutional neural networks, which are essential for image and computer vision tasks, and apply them to practical problems.
Browse courses on
Convolutional Neural Networks
Show steps
-
Visit the TensorFlow website and select the 'Tutorials' section.
-
Choose the 'Convolutional Neural Networks' tutorial and follow the instructions.
-
Complete the exercises and build a CNN model.
-
Test your model on a dataset and analyze the results.
Create a Machine Learning Pipeline
Show steps
This course covers the entire machine learning pipeline, from data preprocessing to model evaluation.
Browse courses on
Machine Learning Pipeline
Show steps
-
Choose a dataset and a machine learning algorithm.
-
Preprocess the data, including cleaning, transforming, and feature engineering.
-
Train and evaluate the machine learning model.
-
Deploy the model into production.
Read Michael Jordan's 'Machine Learning' for a Comprehensive Overview
Show steps
Gain a deep understanding of the fundamental principles of machine learning, including both supervised and unsupervised techniques, to supplement and expand your knowledge from this course.
View
Algorithmic Mathematics in Machine Learning
on Amazon
Show steps
-
Purchase or borrow Michael Jordan's 'Machine Learning' book.
-
Read the first seven chapters, which cover supervised and unsupervised learning.
-
Take notes and highlight important concepts.
-
Complete the exercises and review the solutions.
Create a Visualization to Explain a Reinforcement Learning Algorithm
Show steps
Deepen your understanding of reinforcement learning by creating a visual representation of an algorithm, which can help clarify concepts and improve retention.
Browse courses on
Reinforcement Learning
Show steps
-
Choose a reinforcement learning algorithm, such as Q-learning or SARSA.
-
Break down the algorithm into smaller steps.
-
Design a visual representation of each step.
-
Create the visualization using a tool like PowerPoint or Google Slides.
-
Present your visualization to explain the algorithm.
Volunteer as a Tutor for Beginners in Machine Learning
Show steps
Enhance your understanding by teaching others, as teaching requires a deep grasp of the subject matter and can reveal areas where your knowledge may be weaker.
Show steps
-
Contact local schools, community centers, or online platforms to offer your tutoring services.
-
Prepare lesson plans that cover basic machine learning concepts.
-
Meet with students regularly to provide guidance and support.
-
Evaluate student progress and make adjustments to your teaching approach as needed.
Gather Machine Learning Resources
Show steps
This course covers a comprehensive overview of how to apply machine learning to solve real-world problems.
Browse courses on
Machine Learning Tools
Show steps
Show steps
Show steps
- Create a spreadsheet or document to track resources.
- Search for resources on topics like machine learning algorithms, data preprocessing, and model evaluation.
- Organize resources by topic and level of difficulty.
Seek Guidance from Experienced Machine Learning Practitioners
Show steps
Connect with experts in the field to gain insights, receive feedback, and expand your network, which can be invaluable for your learning journey.
Show steps
Show steps
Show steps
- Attend industry events and conferences.
- Reach out to machine learning professionals on LinkedIn.
- Ask for introductions from friends or colleagues.
- Follow thought leaders on social media and engage with their content.
Review Basic Probability and Linear Algebra
Show steps
This course makes extensive use of linear algebra and probability. Refreshing these topics is likely to improve learning outcomes.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review the concepts of probability, including Bayes' theorem and conditional probability.
- Practice solving linear algebra problems, such as matrix multiplication and finding eigenvalues.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Organize and Review Course Materials
Show steps
Build a strong foundation by ensuring your course materials are well-organized and easily accessible, allowing for efficient review and retention.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Organize the materials by topic or module.
- Review the materials regularly, taking notes or highlighting important concepts.
- Summarize key points and create flashcards for quick recall.
Review Supervised Learning Basics with Andrew Ng's Coursera Course
Show steps
Understand the foundations of supervised learning, including linear regression, logistic regression, and decision trees, for a stronger understanding of the material covered in this course.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Enroll in Andrew Ng's Machine Learning course on Coursera.
- Complete the first three modules of the course, which cover supervised learning concepts.
- Take the quizzes and complete the programming assignments in each module.
- Review the course materials and make notes for future reference.
Solve Machine Learning Coding Challenges
Show steps
This course covers a lot of coding techniques, so it will be helpful to practice coding regularly.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Find a website or platform that offers machine learning coding challenges.
- Start solving coding challenges, starting with easy ones and gradually moving to more difficult ones.
- Review your solutions and identify areas for improvement.
Solve Practice Problems on Kaggle for Supervised and Unsupervised Learning
Show steps
Enhance your problem-solving skills and apply your knowledge of supervised and unsupervised learning to real-world datasets, improving your ability to apply these techniques effectively.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Create an account on Kaggle.
- Select a supervised learning competition, such as the Titanic: Machine Learning from Disaster dataset.
- Download the dataset and explore it.
- Build and train a supervised learning model using the dataset.
- Submit your predictions and track your progress against other competitors.
Follow Machine Learning Tutorials
Show steps
This course covers a wide range of machine learning, so it will be helpful to supplement with tutorials on specific topics.
Show steps
Show steps
Show steps
- Find a reputable source of machine learning tutorials.
- Choose a tutorial that covers a topic you are interested in or struggling with.
- Follow the tutorial step-by-step and complete the exercises.
Follow a TensorFlow Tutorial on Convolutional Neural Networks
Show steps
Advance your skills by learning about convolutional neural networks, which are essential for image and computer vision tasks, and apply them to practical problems.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Visit the TensorFlow website and select the 'Tutorials' section.
- Choose the 'Convolutional Neural Networks' tutorial and follow the instructions.
- Complete the exercises and build a CNN model.
- Test your model on a dataset and analyze the results.
Create a Machine Learning Pipeline
Show steps
This course covers the entire machine learning pipeline, from data preprocessing to model evaluation.
Browse courses on
Machine Learning Pipeline
Show steps
Show steps
Show steps
- Choose a dataset and a machine learning algorithm.
- Preprocess the data, including cleaning, transforming, and feature engineering.
- Train and evaluate the machine learning model.
- Deploy the model into production.
Read Michael Jordan's 'Machine Learning' for a Comprehensive Overview
Show steps
Gain a deep understanding of the fundamental principles of machine learning, including both supervised and unsupervised techniques, to supplement and expand your knowledge from this course.
View
Algorithmic Mathematics in Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow Michael Jordan's 'Machine Learning' book.
- Read the first seven chapters, which cover supervised and unsupervised learning.
- Take notes and highlight important concepts.
- Complete the exercises and review the solutions.
Create a Visualization to Explain a Reinforcement Learning Algorithm
Show steps
Deepen your understanding of reinforcement learning by creating a visual representation of an algorithm, which can help clarify concepts and improve retention.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose a reinforcement learning algorithm, such as Q-learning or SARSA.
- Break down the algorithm into smaller steps.
- Design a visual representation of each step.
- Create the visualization using a tool like PowerPoint or Google Slides.
- Present your visualization to explain the algorithm.
Volunteer as a Tutor for Beginners in Machine Learning
Show steps
Enhance your understanding by teaching others, as teaching requires a deep grasp of the subject matter and can reveal areas where your knowledge may be weaker.
Show steps
Show steps
Show steps
- Contact local schools, community centers, or online platforms to offer your tutoring services.
- Prepare lesson plans that cover basic machine learning concepts.
- Meet with students regularly to provide guidance and support.
- Evaluate student progress and make adjustments to your teaching approach as needed.
Career center
Learners who complete Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys and maintains machine learning systems. These systems can be used for a variety of tasks, such as fraud detection, spam filtering and image recognition. Udacity's Machine Learning course teaches learners how to build machine learning systems using various algorithms, including algorithms for supervised learning (decision trees, support vector machines, artificial neural networks) and unsupervised learning (clustering, principal component analysis) and reinforcement learning.
Data Scientist
Data Scientist
A Data Scientist applies scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. Data Scientists use Machine Learning and other related techniques to make data useful and applicable. Being familiar with Supervised, Unsupervised and Reinforcement learning is important to the role. Udacity's Machine Learning course covers all three of these techniques.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. Quantitative Analysts may use machine learning techniques to develop new trading strategies and improve the performance of existing strategies. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Quantitative Analysis.
Data Analyst
Data Analyst
A Data Analyst collects, processes and analyzes data to help businesses make informed decisions. Data Analysts use a variety of statistical and machine learning techniques to extract insights from data. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Data Analysis.
Investment Manager
Investment Manager
An Investment Manager manages investments for individuals and institutions. Investment Managers may use machine learning techniques to develop new trading strategies and improve the performance of existing strategies. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Investment Management.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve complex business problems. Operations Research Analysts may use machine learning techniques to develop new solutions and improve the performance of existing solutions. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Operations Research.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data and makes recommendations to investors. Financial Analysts may use machine learning techniques to develop new trading strategies and improve the performance of existing strategies. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Finance.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess risk and uncertainty. Actuaries may use machine learning techniques to develop new risk assessment models and improve the performance of existing models. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Actuarial Science.
Software Engineer
Software Engineer
A Software Engineer designs, develops and maintains software applications. Software Engineers may use machine learning techniques to develop new features and improve the performance of existing applications. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Software Engineering.
Risk Manager
Risk Manager
A Risk Manager identifies and manages risks for businesses. Risk Managers may use machine learning techniques to develop new risk management strategies and improve the performance of existing strategies. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Risk Management.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and recommends solutions to improve efficiency and productivity. Business Analysts may use machine learning techniques to help identify trends and patterns in data. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Business Analysis.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. Product Managers may use machine learning techniques to identify new market opportunities and develop new product features. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Product Management.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and executing marketing campaigns. Marketing Managers may use machine learning techniques to target customers and measure the effectiveness of marketing campaigns. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Marketing.
Research Scientist
Research Scientist
A Research Scientist conducts research in a specific field of science or technology. Research Scientists may work in academia, industry or government. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Research Science.
Sales Manager
Sales Manager
A Sales Manager is responsible for leading and managing a team of salespeople. Sales Managers may use machine learning techniques to identify new sales leads and close deals. Udacity's Machine Learning course may be useful as part of a broader education for those wishing to enter the field of Sales.
For more career information including salaries, visit:
OpenCourser.com/course/0mdpy2/udacity
Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning.
This textbook classic introduction to machine learning. It covers a wide range of topics, from the basics of supervised and unsupervised learning to more advanced topics such as reinforcement learning and neural networks.
Save
Comprehensive guide to deep learning, a powerful new technique for machine learning. It covers everything from the basics of neural networks to the latest advances in deep learning research.
Is the definitive textbook on reinforcement learning, a powerful technique for training agents to perform complex tasks. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a comprehensive introduction to Bayesian reasoning and its applications to machine learning. It valuable resource for anyone who wants to learn more about Bayesian methods.
Classic introduction to statistical learning, a powerful technique for modeling and understanding data. It valuable resource for anyone who wants to learn more about statistical learning.
Save
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on statistical methods. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It valuable resource for anyone who wants to learn more about probabilistic machine learning.
Practical guide to machine learning for programmers. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
Practical guide to machine learning using Python. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
Practical guide to machine learning using R. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
Save
Provides a practical guide to machine learning for business professionals. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
Provides a gentle introduction to machine learning for beginners. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
Provides a practical guide to machine learning for web developers. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
Provides a practical guide to machine learning for finance professionals. It covers a wide range of topics, from the basics of machine learning to more advanced topics such as deep learning and natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0mdpy2/udacity
Share
Similar courses
Here are nine courses similar to
Machine Learning.
Machine Learning for Investment Professionals
OpenCourser.com/course/yk4uvd/machine
Machine Learning for Investment Professionals
Most relevant
Deep Learning and Reinforcement Learning
OpenCourser.com/course/ppm31d/deep
Deep Learning and Reinforcement Learning
Most relevant
Designing a Machine Learning Model
OpenCourser.com/course/vd09tg/designing
Designing a Machine Learning Model
Most relevant
Implementing Predictive Analytics with TensorFlow
OpenCourser.com/course/oge404/implementing
Implementing Predictive Analytics with TensorFlow
Most relevant
The Nuts and Bolts of Machine Learning
OpenCourser.com/course/jdxlbe/the
The Nuts and Bolts of Machine Learning
Most relevant
Fundamentals of Machine Learning in Finance
OpenCourser.com/course/9uekog/fundamentals
Fundamentals of Machine Learning in Finance
Most relevant
Applied Data Science for Data Analysts
OpenCourser.com/course/zzx65u/applied
Applied Data Science for Data Analysts
Most relevant
Advanced Machine Learning
OpenCourser.com/course/5ob1m8/advanced
Advanced Machine Learning
Most relevant
Unsupervised Learning, Recommenders, Reinforcement Learning
OpenCourser.com/course/mcyn60/unsupervised
Unsupervised Learning, Recommenders, Reinforcement...
Most relevant
Via
Udacity
Instructors
Michael Littman
Charles Isbell
Pushkar Kolhe
Language
English
Good to know
Explores Machine Learning (ML), which is standard in industry
Taught by Michael Littman, Charles Isbell, and Pushkar Kolhe, who are recognized for their work in Machine Learning
Covers Supervised, Unsupervised, and Reinforcement Learning techniques, which are core skills for data science
Includes Random Optimization, Clustering, and Game Theory, which are more complex ML techniques
Offers quizzes and exercises that help learners apply ML concepts to real-world data
May be challenging for learners new to programming or data science
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser