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Intro to Machine Learning

Katie Malone and Sebastian Thrun

Take Udacity's Introduction to Machine Learning course which provides a foundational understanding of machine learning. Learn online and prepare for a ML career today.

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What's inside

Syllabus

Meet with Sebastian and Katie to discuss machine learning.
Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops core foundational knowledge in machine learning and is taught by experienced instructors
Provides hands-on practice with supervised and unsupervised learning algorithms, preparing learners for practical applications in the field
Suitable for beginners seeking to enter the field of machine learning and develop foundational skills
Covers a wide range of topics, from supervised to unsupervised learning, feature engineering, model evaluation, and more
Leverages industry-standard tools and libraries such as Scikit-Learn, providing learners with practical experience in real-world scenarios
Course prerequisites may be required for learners with no prior background in machine learning or programming

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Reviews summary

Foundational intro to machine learning

According to learners, this course provides a solid, foundational introduction to machine learning concepts and algorithms. Students frequently highlight the practical focus and the usefulness of mini-projects and the final project, which help solidify understanding. The course covers a broad range of essential algorithms like Naive Bayes, SVM, Decision Trees, and K-Means. However, some reviewers find the pace can be very fast and that it requires stronger programming and math prerequisites than might be initially assumed. While many find the explanations clear, others feel certain topics lack sufficient depth and require supplementary materials. Overall, it's seen as a good starting point for those looking to prepare for an ML career, provided they come with the necessary foundational skills.
Utilizes the Scikit-learn library.
"The Scikit-learn implementations were very helpful."
"Uses scikit-learn extensively which is very practical for implementation."
"Implementing algorithms with Scikit-learn was straightforward thanks to the course."
Broad overview of fundamental ML algorithms.
"Good overview of ML concepts. Covers many algorithms like Naive Bayes, SVM, Trees, Regression, K-Means. PCA and feature selection were useful topics."
"The syllabus covers all the main beginner algorithms."
"Explains core algorithms like Naive Bayes, SVM, and Decision Trees quite clearly."
Hands-on coding and projects are valuable.
"Loved the practical focus and the final project was challenging but rewarding. The explanations for SVMs and Decision Trees were clear. The Scikit-learn implementations were very helpful."
"The mini-projects helped solidify concepts. Good balance of theory and practice."
"The hands-on coding assignments and the final project are the most valuable parts of the course."
Provides a good starting point for ML.
"Fantastic starting point! Naive Bayes and SVM lessons were particularly clear. The mini-projects helped solidify concepts."
"A solid introduction. The section on evaluation metrics was very helpful. The syllabus covers all the main beginner algorithms."
"This course is great for anyone new to machine learning and wants a comprehensive overview of the basics."
Some topics aren't covered in detail.
"The linear regression section felt a bit rushed, and I struggled with the outlier detection part. Needed external resources for deeper understanding."
"Covers breadth over depth. Useful for a survey but not deep skill building."
"Expected more depth."
"Some complex concepts like PCA could use more detailed explanations."
Requires prior programming/math background.
"Requires solid programming basics."
"Found it moved too fast. The prerequisites aren't strongly enforced, so pure beginners might struggle with the coding parts."
"Completely lost. Assumes too much prior knowledge, especially in programming and linear algebra."
"Not suitable for true beginners as advertised; strong Python and math skills are a must."
Course moves quickly through topics.
"Sometimes felt a bit fast-paced, but the core ideas were well-explained. Requires solid programming basics."
"Found it moved too fast. The prerequisites aren't strongly enforced, so pure beginners might struggle with the coding parts."
"Covers a lot of ground quickly."
"The pace is relentless, making it difficult for true beginners to keep up without prior knowledge."

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 Intro to Machine Learning with these activities:
Organize notes
Help you refresh your foundational understanding of machine learning and algorithms
Show steps
  • Review your notes from previous courses or tutorials on machine learning.
  • Identify sections in your notes that need more attention.
  • Organize your notes by topic and subtopic.
  • Summarize key concepts and algorithms in your own words.
  • Test your understanding by attempting practice problems or quizzes online.
Explore Machine Learning Libraries
Help you become familiar with popular machine learning libraries and their functionalities
Show steps
  • Choose a machine learning library such as scikit-learn or TensorFlow.
  • Follow online tutorials or documentation to learn about the library's basic functions.
  • Experiment with different algorithms and data sets using the library.
  • Explore advanced features of the library, such as hyperparameter tuning or data visualization.
Classification Drills
Help you strengthen your understanding of different classification algorithms and their applications
Show steps
  • Solve practice problems involving classification tasks.
  • Implement classification algorithms from scratch using Python or another programming language.
  • Analyze the results of your algorithms and identify areas for improvement.
  • Participate in online coding challenges or competitions related to classification.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Machine Learning Study Group
Help you reinforce your learning, clarify concepts, and exchange ideas with peers
Show steps
  • Form a study group with classmates.
  • Meet regularly to discuss course material, solve problems together.
  • Share resources, such as notes, practice questions, or online tutorials.
  • Provide each other with feedback and support.
Mini Machine Learning Project
Help you apply your learning by building a small-scale machine learning project
Show steps
  • Identify a real-world problem that can be solved using machine learning.
  • Collect and prepare a data set for your project.
  • Choose and implement a machine learning algorithm to solve your problem.
  • Evaluate the performance of your algorithm and make necessary adjustments.
  • Present your project to classmates or colleagues for feedback.
Attend a Machine Learning Workshop
Help you gain practical experience and learn from experts in the field
Show steps
  • Search for upcoming machine learning workshops in your area or online.
  • Select a workshop that aligns with your interests and learning goals.
  • Attend the workshop and actively participate in discussions and activities.
  • Network with other attendees and instructors.
  • Follow up on any resources or materials provided at the workshop.
Contribute to an Open-Source Machine Learning Project
Help you gain hands-on experience, build your portfolio, and contribute to the machine learning community
Show steps
  • Identify an open-source machine learning project that interests you.
  • Read the project documentation and understand its goals and architecture.
  • Find a small task or issue to work on and submit a pull request.
  • Collaborate with other contributors and the project maintainers.
  • Document your contributions and share your experience with others.

Career center

Learners who complete Intro to Machine Learning will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer designs and builds machine learning models that can be used to solve real-world problems. This course can help you develop the skills you need to be successful in this role by teaching you about the fundamentals of machine learning, including supervised learning, unsupervised learning, and deep learning. You will also learn how to use software tools such as Python and R to implement machine learning models.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
A Data Scientist uses data to solve business problems. This course can help you develop the skills you need to be successful in this role by teaching you about machine learning algorithms, data visualization techniques, and statistical methods. You will also learn how to use software tools such as Python and R to analyze data and communicate your findings to non-technical audiences.
See salaries and explore the career path for Data Scientist
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to find trends and patterns. This course can help you develop the skills you need to be successful in this role by teaching you about machine learning algorithms, data visualization techniques, and statistical methods. You will also learn how to use software tools such as Python and R to analyze data.
See salaries and explore the career path for Data Analyst
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course can help you develop the skills you need to be successful in this role by teaching you about the fundamentals of computer science, including data structures, algorithms, and software design. You will also learn how to use software tools such as Python and Java to develop software applications.
See salaries and explore the career path for Software Engineer
Product Manager
A Product Manager is responsible for the development and marketing of a product. This course can help you develop the skills you need to be successful in this role by teaching you about the product development process, including market research, product design, and product launch. You will also learn how to use software tools such as Jira and Confluence to manage product development.
See salaries and explore the career path for Product Manager
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course can help you develop the skills you need to be successful in this role by teaching you about business analysis techniques, including process mapping, data analysis, and financial modeling. You will also learn how to use software tools such as Visio and Excel to create business analysis reports.
See salaries and explore the career path for Business Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and making investment recommendations. This course can help you develop the skills you need to be successful in this role by teaching you about financial analysis techniques, including ratio analysis, discounted cash flow analysis, and regression analysis. You will also learn how to use software tools such as Excel and Bloomberg to analyze financial data.
See salaries and explore the career path for Financial Analyst
Marketing Analyst
A Marketing Analyst is responsible for analyzing marketing data and making marketing recommendations. This course can help you develop the skills you need to be successful in this role by teaching you about marketing analysis techniques, including market research, customer segmentation, and campaign analysis. You will also learn how to use software tools such as Google Analytics and Adobe Analytics to analyze marketing data.
See salaries and explore the career path for Marketing Analyst
Operations Analyst
An Operations Analyst is responsible for analyzing business operations and identifying opportunities for improvement. This course can help you develop the skills you need to be successful in this role by teaching you about operations analysis techniques, including process mapping, data analysis, and simulation modeling. You will also learn how to use software tools such as Visio and Excel to create operations analysis reports.
See salaries and explore the career path for Operations Analyst
Risk Analyst
A Risk Analyst is responsible for identifying and assessing risks to an organization. This course can help you develop the skills you need to be successful in this role by teaching you about risk analysis techniques, including risk identification, risk assessment, and risk mitigation. You will also learn how to use software tools such as Risk Manager and Palisade DecisionTools to analyze risks.
See salaries and explore the career path for Risk Analyst
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. This course can help you develop the skills you need to be successful in this role by teaching you about statistical methods, including data collection, data analysis, and statistical modeling. You will also learn how to use software tools such as R and SAS to analyze data.
See salaries and explore the career path for Statistician
Actuary
An Actuary is responsible for assessing and managing financial risks. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about financial analysis techniques, including ratio analysis, discounted cash flow analysis, and regression analysis. You will also learn how to use software tools such as Excel and Bloomberg to analyze financial data.
See salaries and explore the career path for Actuary
Economist
An Economist is responsible for studying the economy and making economic forecasts. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about economic principles, including microeconomics, macroeconomics, and econometrics. You will also learn how to use software tools such as R and EViews to analyze economic data.
See salaries and explore the career path for Economist
Financial Planner
A Financial Planner is responsible for helping clients achieve their financial goals. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about financial planning techniques, including investment planning, retirement planning, and estate planning. You will also learn how to use software tools such as MoneyGuidePro and eMoney Advisor to create financial plans.
See salaries and explore the career path for Financial Planner
Insurance Agent
An Insurance Agent is responsible for selling and servicing insurance policies. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about insurance products, including life insurance, health insurance, and property insurance. You will also learn how to use software tools such as InsureMe and AgentPro to manage insurance policies.
See salaries and explore the career path for Insurance Agent

Reading list

We've selected 12 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 Intro to Machine Learning.
Cover image
Deep Learning
Save
An authoritative reference on deep learning, providing a comprehensive overview of the field. Suitable for both researchers and practitioners, it covers a wide range of topics from basics to advanced concepts.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
A practical guide to machine learning using popular Python libraries. Focuses on building real-world applications and provides clear explanations of core concepts.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
The Molecular Basis of Cancer
Save
Provides a practical introduction to machine learning using Python. Covers a range of topics, including supervised and unsupervised learning, as well as real-world case studies.
The Molecular Basis of Cancer
Hardcover
$$$$
The Molecular Basis of Cancer: Expert Consult -...
Hardcover
$$$$
Molecular Basis of Cancer
Hardcover
$$$
The Molecular Basis of Cancer
Kindle Edition
$$$$
Cover image
Deep Learning
Save
A comprehensive textbook that provides a thorough foundation in machine learning. Covers a wide range of topics, including statistical learning, Bayesian methods, and deep learning.
Deep Learning: Foundations and Concepts
Hardcover
$$$
Cover image
An Introduction to Statistical Learning
Save
An introduction to statistical learning methods for data analysis. Covers a variety of topics, including linear regression, classification, and clustering.
An Introduction to Statistical Learning: with...
Hardcover
$$$
Cover image
Machine Learning
Save
Provides a broad overview of machine learning, covering both the theoretical foundations and practical applications. Suitable for beginners and experienced practitioners alike.
Machine Learning: The Art and Science of Algorithms...
Kindle Edition
$$
Machine Learning: ECML 2001: 12th European...
Paperback
$$$
Machine Learning: The Art and Science of Algorithms...
Paperback
$$
Machine Learning and Knowledge Discovery in...
Paperback
$$$
Machine Learning and Knowledge Discovery in...
Paperback
$$$
Cover image
Probabilistic Machine Learning
Save
A textbook that provides a probabilistic approach to machine learning. Covers a variety of topics, including Bayesian inference, graphical models, and deep learning.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Data Mining
Save
A comprehensive textbook that covers data mining techniques and algorithms. Provides a solid foundation for readers who want to learn about data mining and its applications.
Data Mining: Concepts and Techniques (The Morgan...
Paperback
$$$
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Cover image
Fundamentals of Machine Learning for Predictive...
Save
A textbook that provides a practical introduction to machine learning for predictive analytics. Covers a range of topics, including data preprocessing, model selection, and evaluation.
Fundamentals of Machine Learning for Predictive...
Hardcover
$$$
Fundamentals of Machine Learning for Predictive...
Kindle Edition
$$
Cover image
Machine Learning with R
Save
A comprehensive guide to machine learning using the R programming language. Covers a variety of topics, including data preprocessing, model selection, and evaluation.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Machine Learning Q and AI
Save
A practical guide to machine learning using the Python programming language. Covers a variety of topics, including data preprocessing, model selection, and evaluation.
Machine Learning Q and AI: 30 Essential Questions...
Paperback
$$
Machine Learning Q and AI: 30 Essential Questions...
Kindle Edition
$$
Cover image
Mathematics for Machine Learning
Save
A non-technical introduction to machine learning. Covers a variety of topics, including supervised and unsupervised learning, as well as applications in various fields.
Mathematics for Machine Learning
Kindle Edition
$$

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Effort
3 days
Via
Udacity
Instructors
Katie Malone
Sebastian Thrun
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Develops core foundational knowledge in machine learning and is taught by experienced instructors
Provides hands-on practice with supervised and unsupervised learning algorithms, preparing learners for practical applications in the field
Suitable for beginners seeking to enter the field of machine learning and develop foundational skills
Covers a wide range of topics, from supervised to unsupervised learning, feature engineering, model evaluation, and more
Leverages industry-standard tools and libraries such as Scikit-Learn, providing learners with practical experience in real-world scenarios
Course prerequisites may be required for learners with no prior background in machine learning or programming
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