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Trevor Hastie,
Robert Tibshirani,
and
Jonathan Taylor
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
Read more
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
Read more
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing in this course is done in Python. There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called Statistical Learning with R – the chapter lectures are the same, but the lab lectures and computing are done using R.
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023. The pdf for this book is available for free on the book website.
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What's inside
Learning objectives
- Overview of statistical learning
- Linear regression
- Classificaiton
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Support vector machines
- Deep learning
- Survival modeling
- Unsupervised learning
- Multiple testing
- Overview of statistical learning
- Linear regression
- Classificaiton
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Support vector machines
- Deep learning
- Survival modeling
- Unsupervised learning
- Multiple testing
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces students to essential concepts of machine learning, focusing on foundational theories and practical applications
Emphasizes comprehension and application of key statistical learning concepts rather than relying heavily on mathematical formulas
Provides a comprehensive overview of modern data science techniques, including supervised and unsupervised learning methods
Incorporates hands-on Python programming exercises to reinforce concepts and enhance practical skills
Led by renowned instructors with extensive experience and expertise in statistical learning and machine learning
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Activities
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it. Supplement your coursework and achieve mastery of the topics covered
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activities:
Organize and Review Course Materials
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Strengthen your understanding by reviewing and organizing the course materials regularly.
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Create a dedicated folder or notebook for course materials.
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Organize notes, assignments, quizzes, and exams.
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Review the materials periodically to reinforce your learning.
Review Linear Algebra
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Review the concepts of linear algebra to strengthen your understanding of the mathematical foundations of machine learning.
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Linear Algebra
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Revisit fundamental concepts like vectors, matrices, and linear transformations.
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Practice solving systems of linear equations.
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Understand the concept of vector spaces, subspaces, and their properties.
Read 'Introduction to Statistical Learning'
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Gain a deeper understanding of the theory and concepts behind the machine learning methods covered in the course.
View
An Introduction to Statistical Learning: with...
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Read the book's chapters corresponding to the course material.
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Solve the exercises and practice problems provided in the book.
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Discuss the concepts with classmates or online forums.
Four other activities
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Enroll in online tutorials or courses that focus on Python for machine learning.
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Follow along with the tutorials, practicing the code snippets.
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Experiment with different datasets and algorithms.
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Join or form study groups with classmates.
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Discuss course material, share insights, and ask questions.
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Participate in online forums or discussion boards to interact with a wider community.
Complete Supervised Learning Practice Problems
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Supervised Learning
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Find practice problems in textbooks, online repositories, or MOOC platforms.
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Attempt to solve the problems on your own.
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Review solutions and compare your approach.
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Seek clarification from instructors or peers if needed.
Create a Visual Representation of Machine Learning Concepts
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Enhance your understanding and improve retention by creating visual representations of key concepts.
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Choose a concept to represent visually, such as linear regression or decision trees.
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Create a diagram, flowchart, or infographic that explains the concept.
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Share your visual representation with classmates or online.
Organize and Review Course Materials
Show steps
Strengthen your understanding by reviewing and organizing the course materials regularly.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Organize notes, assignments, quizzes, and exams.
- Review the materials periodically to reinforce your learning.
Review Linear Algebra
Show steps
Review the concepts of linear algebra to strengthen your understanding of the mathematical foundations of machine learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit fundamental concepts like vectors, matrices, and linear transformations.
- Practice solving systems of linear equations.
- Understand the concept of vector spaces, subspaces, and their properties.
Read 'Introduction to Statistical Learning'
Show steps
Gain a deeper understanding of the theory and concepts behind the machine learning methods covered in the course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters corresponding to the course material.
- Solve the exercises and practice problems provided in the book.
- Discuss the concepts with classmates or online forums.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Python Tutorials for Machine Learning
Show steps
Enhance your Python programming skills and gain practical experience in implementing machine learning algorithms.
Browse courses on
Python
Show steps
Show steps
Show steps
- Enroll in online tutorials or courses that focus on Python for machine learning.
- Follow along with the tutorials, practicing the code snippets.
- Experiment with different datasets and algorithms.
Participate in Study Groups and Discussions
Show steps
Enhance your learning by collaborating and discussing course concepts with peers.
Show steps
Show steps
Show steps
- Join or form study groups with classmates.
- Discuss course material, share insights, and ask questions.
- Participate in online forums or discussion boards to interact with a wider community.
Complete Supervised Learning Practice Problems
Show steps
Reinforce your understanding of supervised learning algorithms by solving practice problems.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Find practice problems in textbooks, online repositories, or MOOC platforms.
- Attempt to solve the problems on your own.
- Review solutions and compare your approach.
- Seek clarification from instructors or peers if needed.
Create a Visual Representation of Machine Learning Concepts
Show steps
Enhance your understanding and improve retention by creating visual representations of key concepts.
Show steps
Show steps
Show steps
- Choose a concept to represent visually, such as linear regression or decision trees.
- Create a diagram, flowchart, or infographic that explains the concept.
- Share your visual representation with classmates or online.
Career center
Learners who complete Statistical Learning with Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists combine statistical knowledge, programming skills, and business acumen to extract insights from data. This role demands proficiency in statistical modeling, machine learning, and data analysis. The Statistical Learning with Python course provides a comprehensive foundation for these skills. The course covers topics such as regression, classification, time series analysis, and natural language processing, which are essential for data science. Additionally, the course emphasizes practical implementation using Python, which is a widely used language in data science.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models to solve real-world problems. This role requires a strong foundation in statistical learning and programming. The Statistical Learning with Python course can equip you with the essential skills for this career. The course covers topics such as supervised and unsupervised learning, model evaluation, and feature engineering, which are fundamental to machine learning. Furthermore, the practical Python implementation of these techniques will provide you with hands-on experience in building and deploying machine learning solutions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and solve problems. This role requires a deep understanding of statistical principles and methodologies. The Statistical Learning with Python course can provide you with a solid foundation in statistics. The course covers topics such as probability, inference, hypothesis testing, and regression analysis, which are essential for a Statistician. Additionally, the course emphasizes practical implementation using Python, which is increasingly used in statistical analysis.
Data Analyst
Data Analyst
Data Analysts collect, process, study, and visualize data to help organizations make informed decisions. To be successful in this role, you need to understand statistical techniques and programming languages. The Statistical Learning with Python course can provide you with the necessary skills to excel as a Data Analyst. The course covers topics such as data preprocessing, regression, classification, model selection, and machine learning algorithms, which are crucial for data analysis. By taking this course, you will gain a deep understanding of data analysis principles and practical experience in applying these techniques using Python.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks for organizations. This role requires expertise in statistical modeling, data analysis, and risk management. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as probability, regression, and time series analysis, which are crucial for risk analysis. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in building risk models and analyzing risk data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts apply mathematical and statistical techniques to financial data to make investment decisions. This role requires expertise in statistical modeling, risk assessment, and financial analysis. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as regression, time series analysis, and financial modeling, which are crucial for quantitative analysis. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in building financial models and analyzing financial data.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risks. This role requires expertise in statistical modeling, probability, and financial analysis. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as regression, time series analysis, and financial modeling, which are crucial for actuarial work. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in building financial models and analyzing financial data.
Business Analyst
Business Analyst
Business Analysts use data and analytics to improve business processes and make informed decisions. This role requires proficiency in data analysis, statistical modeling, and business acumen. The Statistical Learning with Python course can provide you with a strong foundation for these skills. The course covers topics such as data visualization, regression, and decision-making tools, which are essential for business analysis. Additionally, the course emphasizes practical implementation using Python, which is widely used in business analytics.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to medical and health-related data. This role requires expertise in statistical modeling, data analysis, and medical knowledge. The Statistical Learning with Python course provides a strong foundation for these skills. The course covers topics such as regression, survival analysis, and clinical trials, which are essential for biostatistics. Furthermore, the practical Python implementation of these techniques will give you hands-on experience in analyzing medical data and drawing meaningful conclusions.
Market Researcher
Market Researcher
Market Researchers study market trends and consumer behavior to help businesses make informed decisions. This role requires expertise in data analysis, research methods, and marketing principles. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as survey design, data analysis, and segmentation, which are relevant to market research. Additionally, the practical Python implementation of these techniques will give you hands-on experience in analyzing market data and drawing meaningful conclusions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve complex business problems. This role requires expertise in optimization, simulation, and decision-making. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as linear programming, integer programming, and decision trees, which are relevant to operations research. Additionally, the practical Python implementation of these techniques will give you hands-on experience in building models and analyzing data to solve business problems.
Financial Analyst
Financial Analyst
Financial Analysts use financial data and analysis to make investment decisions. This role requires expertise in financial modeling, valuation, and risk assessment. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as regression, time series analysis, and financial modeling, which are relevant to financial analysis. Additionally, the practical Python implementation of these techniques will give you hands-on experience in building financial models and analyzing financial data.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events in specified populations. This role requires expertise in biostatistics, data analysis, and public health principles. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as survival analysis, spatial epidemiology, and outbreak investigation, which are relevant to epidemiology. Additionally, the practical Python implementation of these techniques will give you hands-on experience in analyzing epidemiological data and drawing meaningful conclusions.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure and systems. This role requires expertise in data management, database systems, and programming. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in data analysis and statistical modeling. The course covers topics such as data cleaning, feature engineering, and machine learning algorithms, which are relevant to data engineering. Additionally, the practical Python implementation of these techniques will give you hands-on experience in working with data and building data pipelines.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This role requires expertise in programming languages, software design, and algorithms. The Statistical Learning with Python course may be useful in this role, as it provides a foundation in statistical modeling and machine learning. The course covers topics such as data analysis, regression, and classification, which are relevant to software development. Additionally, the practical Python implementation of these techniques will give you hands-on experience in building software applications that leverage data and statistical methods.
For more career information including salaries, visit:
OpenCourser.com/course/46og0x/statistical
Reading list
We've selected 13 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
Statistical Learning with Python.
Is the basis for the Statistical Learning with Python course and provides a detailed overview of statistical learning methods. It is recommended as either a primary or secondary reading.
Classic textbook on machine learning. It provides a comprehensive overview of the field and valuable reference for students and practitioners.
Save
Practical guide to data mining methods. It provides a step-by-step approach to using these methods to solve real-world problems.
Practical guide to using reinforcement learning for real-world problems. It provides a step-by-step approach to using these methods to build real-world applications.
More advanced treatment of statistical learning methods than the James et al. book. However, it is still accessible to most students and provides additional depth.
Practical guide to using natural language processing for real-world problems. It provides a step-by-step approach to using these methods to build real-world applications.
Practical guide to using computer vision for real-world problems. It provides a step-by-step approach to using these methods to build real-world applications.
Practical guide to using Python for data analysis. It provides a step-by-step approach to using these methods to solve real-world problems.
Practical guide to using NumPy and Pandas for data analysis. It provides a step-by-step approach to using these methods to solve real-world problems.
Practical guide to using machine learning for real-world problems. It provides a step-by-step approach to using these methods to build real-world applications.
Practical guide to using deep learning for real-world problems. It provides a step-by-step approach to using these methods to build real-world applications.
Practical guide to using machine learning for hackers. It provides a step-by-step approach to using these methods to build real-world applications.
Save
Comprehensive overview of deep learning methods. It useful resource for students who want to learn more about this rapidly growing field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/46og0x/statistical
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Effort
3 to 5 hours per week for 11 weeks
Level
Introductory
Via
edX
Institution
Stanford University
Instructors
Trevor Hastie
Robert Tibshirani
Jonathan Taylor
Session
December 5, 2023
- August 26, 2024
Language
English
Transcript
English
Good to know
Introduces students to essential concepts of machine learning, focusing on foundational theories and practical applications
Emphasizes comprehension and application of key statistical learning concepts rather than relying heavily on mathematical formulas
Provides a comprehensive overview of modern data science techniques, including supervised and unsupervised learning methods
Incorporates hands-on Python programming exercises to reinforce concepts and enhance practical skills
Led by renowned instructors with extensive experience and expertise in statistical learning and machine learning
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