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Dmitri Piontkovski
and
Vsevolod L. Chernyshev
The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data...
Read more
The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data...
Read more
The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability.
This online course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well.
This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.
Register for this course and see more details by visiting:
OpenCourser.com/course/mstf7i/first
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Helps prepare learners for graduate studies in Data Science through HSE University's Master's program
Provides learners with an introduction to essential Linear Algebra concepts, foundational to Machine Learning
Suitable for experienced students in Matrix Analysis or Linear Algebra who wish to transition into using Linear Algebra for Data Science
Provides opportunities to develop foundational skills in Linear Algebra for those not well-versed in the topic
Instructors Dmitri Piontkovski and Vsevolod L. Chernyshev are recognized researchers in the field of Linear Algebra
Learners are advised to have prior knowledge of Matrix Analysis or Linear Algebra before taking this course
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Reviews summary
Linear algebra essentials for machine learning
This course is a well-received introduction to linear algebra for those interested in machine learning. It provides a solid foundation in the fundamentals of linear algebra and their applications in machine learning. The course is challenging but rewarding, with many learners highlighting the programming assignments as particularly valuable.
Hands-on Python experience
"The programming assignments were really challenging! I thought I'm not gonna pass this course since it's my first time to encounter Python language. Thank you so much Prof. Piontkovski and Prof. Chernyshev for the new learnings :)"
Focuses on machine learning applications
"Quite challenging but necessary to have a deeper understanding of machine learning algorithms"
Strong theoretical foundation
"I learnt a lot with this course, good introduction to linear algebra, and good guidance to the use of sk-learn for Machine Learning."
Prepare for a challenge!
"This is a great courses, sometimes explanations could be better but in general is awesome and they teach us good applications of linear algebra in the field of machine learning. I would like to rate this course with 4.5, but Coursera does not allow us to rate in that way."
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 First Steps in Linear Algebra for Machine Learning with these
activities:
Review Prerequisites for Linear Algebra
Show steps
Reviewing essential prerequisites will ensure you have a strong foundation before starting the course.
Show steps
-
Revisit basic algebraic concepts such as solving equations, systems of equations, and manipulating expressions.
-
Practice working with vectors, matrices, and determinants.
Review Matrix Analysis
Show steps
Brushing up on your knowledge of Matrix Analysis will lay a solid foundation for understanding the concepts taught in this course.
Browse courses on
Matrix Analysis
Show steps
-
Revisit fundamental concepts such as matrix operations, determinants, and eigenvalues.
-
Practice solving systems of linear equations using matrices.
Form a Linear Algebra Study Group
Show steps
Collaborating with peers in a study group can enhance your learning experience and provide a supportive environment.
Show steps
-
Find a group of classmates or peers who are also taking the course.
-
Set regular meeting times to discuss course material, solve problems, and quiz each other.
-
Take turns leading discussions and presenting concepts to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Linear Algebra Problems
Show steps
Regular practice with linear algebra problems will strengthen your problem-solving skills and solidify your understanding of the concepts.
Show steps
-
Solve practice problems covering various linear algebra topics, such as matrix operations, vector spaces, and linear transformations.
-
Work through challenging problems to test your understanding and identify areas for improvement.
-
Review your solutions and seek clarification on any concepts you struggle with.
Compile a Linear Algebra Resource Collection
Show steps
Curating a collection of resources will provide you with a valuable reference throughout your studies.
Show steps
-
Gather online resources, such as tutorials, videos, lecture notes, and practice problems.
-
Organize the resources by topic and difficulty level.
-
Share your collection with classmates or the online community.
Develop a Linear Algebra Toolset
Show steps
Creating your own toolset will not only reinforce your understanding of linear algebra but also provide valuable resources for future use.
Show steps
-
Identify a specific problem or application that requires linear algebra techniques.
-
Design and implement a set of functions or algorithms to address the problem using linear algebra.
-
Test and refine your toolset to ensure accuracy and efficiency.
Tutor or Assist Others in Linear Algebra
Show steps
Mentoring others not only benefits them but also reinforces your own understanding of linear algebra.
Show steps
-
Offer tutoring or assistance to classmates or students who need support in linear algebra.
-
Explain concepts clearly, provide examples, and answer questions to help others grasp the material.
-
Reflect on your own understanding and identify areas where you need further improvement.
Review Prerequisites for Linear Algebra
Show steps
Reviewing essential prerequisites will ensure you have a strong foundation before starting the course.
Show steps
Show steps
Show steps
- Revisit basic algebraic concepts such as solving equations, systems of equations, and manipulating expressions.
- Practice working with vectors, matrices, and determinants.
Review Matrix Analysis
Show steps
Brushing up on your knowledge of Matrix Analysis will lay a solid foundation for understanding the concepts taught in this course.
Browse courses on
Matrix Analysis
Show steps
Show steps
Show steps
- Revisit fundamental concepts such as matrix operations, determinants, and eigenvalues.
- Practice solving systems of linear equations using matrices.
Form a Linear Algebra Study Group
Show steps
Collaborating with peers in a study group can enhance your learning experience and provide a supportive environment.
Show steps
Show steps
Show steps
- Find a group of classmates or peers who are also taking the course.
- Set regular meeting times to discuss course material, solve problems, and quiz each other.
- Take turns leading discussions and presenting concepts to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Linear Algebra Problems
Show steps
Regular practice with linear algebra problems will strengthen your problem-solving skills and solidify your understanding of the concepts.
Show steps
Show steps
Show steps
- Solve practice problems covering various linear algebra topics, such as matrix operations, vector spaces, and linear transformations.
- Work through challenging problems to test your understanding and identify areas for improvement.
- Review your solutions and seek clarification on any concepts you struggle with.
Compile a Linear Algebra Resource Collection
Show steps
Curating a collection of resources will provide you with a valuable reference throughout your studies.
Show steps
Show steps
Show steps
- Gather online resources, such as tutorials, videos, lecture notes, and practice problems.
- Organize the resources by topic and difficulty level.
- Share your collection with classmates or the online community.
Develop a Linear Algebra Toolset
Show steps
Creating your own toolset will not only reinforce your understanding of linear algebra but also provide valuable resources for future use.
Show steps
Show steps
Show steps
- Identify a specific problem or application that requires linear algebra techniques.
- Design and implement a set of functions or algorithms to address the problem using linear algebra.
- Test and refine your toolset to ensure accuracy and efficiency.
Tutor or Assist Others in Linear Algebra
Show steps
Mentoring others not only benefits them but also reinforces your own understanding of linear algebra.
Show steps
Show steps
Show steps
- Offer tutoring or assistance to classmates or students who need support in linear algebra.
- Explain concepts clearly, provide examples, and answer questions to help others grasp the material.
- Reflect on your own understanding and identify areas where you need further improvement.
Career center
Learners who complete First Steps in Linear Algebra for Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists play a crucial role in extracting insights and patterns from complex data. The concepts of Linear Algebra are widely used in the field of Machine Learning, which is a key component of Data Science. By taking this course, you will gain a solid foundation in Matrix Analysis and Linear Algebra, which will enhance your ability to work with data in vector and matrix form and solve complex problems using Linear Algebra methods. This course will help you develop the necessary skills to effectively perform data analysis tasks and drive informed decision-making in the field of Data Science.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building, deploying, and maintaining Machine Learning models. Linear Algebra forms the mathematical backbone of many Machine Learning algorithms, including linear regression, support vector machines, and neural networks. This course will provide you with a comprehensive understanding of the fundamental concepts of Linear Algebra and their application in Machine Learning. By mastering these concepts, you will be well-equipped to design, implement, and optimize Machine Learning models for various real-world problems.
Data Analyst
Data Analyst
Data Analysts play a critical role in examining and interpreting data to uncover trends and provide insights for decision-making. Linear Algebra provides a powerful framework for working with data in matrix form, which is commonly encountered in data analysis tasks. This course will equip you with the knowledge and skills to effectively analyze and visualize data using Linear Algebra methods. By understanding the underlying mathematical concepts, you will gain a deeper understanding of data patterns and be able to derive meaningful insights from complex datasets.
Business Analyst
Business Analyst
Business Analysts help organizations understand their business processes and make data-driven decisions. Linear Algebra is increasingly used in business analysis for tasks such as forecasting, optimization, and risk assessment. This course will provide you with a solid foundation in Linear Algebra, enabling you to leverage its techniques to analyze business data, identify patterns, and develop actionable recommendations. By mastering these concepts, you will enhance your ability to solve complex business problems and contribute to organizational success.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. Linear Algebra is used in various areas of software development, such as computer graphics, image processing, and numerical simulations. This course will provide you with a strong foundation in Linear Algebra, enabling you to apply its principles in software development. By understanding the mathematical underpinnings of Linear Algebra, you will be better equipped to design efficient and robust software solutions.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data for various applications. Linear Algebra is essential for statistical modeling and data analysis. This course will provide you with a comprehensive understanding of Linear Algebra and its application in Statistics. By mastering these concepts, you will be well-equipped to develop statistical models, analyze data, and draw meaningful conclusions from complex datasets.
Financial Analyst
Financial Analyst
Financial Analysts use data to make informed decisions about investments and financial strategies. Linear Algebra is used in financial modeling and risk assessment. This course will provide you with a foundation in Linear Algebra, enabling you to apply its techniques in financial analysis. By understanding the mathematical principles of Linear Algebra, you will be better equipped to analyze financial data, develop models, and make sound investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. Linear Algebra is widely used in Operations Research for tasks such as optimization, scheduling, and resource allocation. This course will provide you with a solid foundation in Linear Algebra, enabling you to apply its principles in Operations Research. By mastering these concepts, you will be well-equipped to analyze complex systems, develop efficient solutions, and contribute to organizational success.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty in various fields, such as insurance, finance, and healthcare. Linear Algebra is used in actuarial science for tasks such as risk modeling and premium calculation. This course will provide you with a foundation in Linear Algebra, enabling you to apply its principles in actuarial work. By understanding the mathematical foundations of Linear Algebra, you will be better equipped to analyze data, develop models, and make informed decisions in the field of actuarial science.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Linear Algebra is widely used in quantitative finance for tasks such as portfolio optimization and risk management. This course will provide you with a solid foundation in Linear Algebra, enabling you to apply its principles in quantitative finance. By mastering these concepts, you will be well-equipped to develop and implement quantitative models for financial analysis and decision-making.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying, assessing, and mitigating risks within an organization. Linear Algebra is used in risk management for tasks such as risk modeling and portfolio optimization. This course will provide you with a foundation in Linear Algebra, enabling you to apply its principles in risk management. By understanding the mathematical foundations of Linear Algebra, you will be better equipped to analyze data, develop models, and make informed decisions to manage risk effectively.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. Linear Algebra is used in economics for tasks such as modeling market equilibrium and analyzing economic data. This course will provide you with a foundation in Linear Algebra, enabling you to apply its principles in economics. By understanding the mathematical foundations of Linear Algebra, you will be better equipped to analyze economic data, develop models, and make informed decisions in the field of economics.
Operations Manager
Operations Manager
Operations Managers are responsible for planning, organizing, and controlling the production and delivery of goods and services. Linear Algebra is used in operations management for tasks such as supply chain optimization and production scheduling. This course will provide you with a foundation in Linear Algebra, enabling you to apply its principles in operations management. By understanding the mathematical foundations of Linear Algebra, you will be better equipped to analyze data, develop models, and make informed decisions to improve operational efficiency.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and controlling projects to achieve specific objectives. Linear Algebra is used in project management for tasks such as resource allocation and scheduling. This course will provide you with a foundation in Linear Algebra, enabling you to apply its principles in project management. By understanding the mathematical foundations of Linear Algebra, you will be better equipped to analyze data, develop models, and make informed decisions to ensure project success.
Business Consultant
Business Consultant
Business Consultants provide advice and guidance to businesses on various aspects of their operations. Linear Algebra may be used in business consulting for tasks such as data analysis and process optimization. This course may be helpful in providing a foundation in Linear Algebra, enabling you to apply its principles in business consulting. By understanding the mathematical foundations of Linear Algebra, you will be better equipped to analyze data, develop models, and make informed recommendations to help businesses improve their performance.
For more career information including salaries, visit:
OpenCourser.com/course/mstf7i/first
Reading list
We've selected 11 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
First Steps in Linear Algebra for Machine Learning.
Textbook on machine learning with Scikit-Learn, Keras, and TensorFlow. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning.
Classic textbook on machine learning. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning.
Save
Classic textbook on deep learning. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks.
Classic textbook on reinforcement learning. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.
Save
Classic textbook on matrix analysis. It covers the basics of matrix analysis, as well as more advanced topics such as matrix norms and matrix decompositions.
Save
Classic textbook on matrix computations. It covers the basics of matrix computations, as well as more advanced topics such as matrix decompositions and singular value decomposition.
Comprehensive treatment of linear algebra with applications to machine learning. It covers the basics of linear algebra, as well as more advanced topics such as matrix decompositions and singular value decomposition.
Textbook on linear algebra and optimization for machine learning. It covers the basics of linear algebra and optimization, as well as more advanced topics such as kernel methods and support vector machines.
Textbook on numerical linear algebra. It covers the basics of numerical linear algebra, as well as more advanced topics such as iterative methods for solving linear systems and eigenvalue computations.
Classic textbook on linear algebra and is used by many universities as a textbook for undergraduate linear algebra courses. It covers the basics of linear algebra, including vectors, matrices, determinants, and eigenvalues and eigenvectors. It also has a section on applications of linear algebra to machine learning.
Widely used textbook for undergraduate linear algebra courses. It is written in a clear and concise style and covers the basics of linear algebra, including vectors, matrices, determinants, and eigenvalues and eigenvectors.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mstf7i/first
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Time
20 hours
Via
Coursera
Institutions
National Research University Higher School of Economics
HSE University
Instructors
Dmitri Piontkovski
Vsevolod L. Chernyshev
This course belongs to a
collection
called
Mathematics for Data Science. It's comprised of four courses:
- First Steps in Linear Algebra for Machine Learning (viewing)
- Discrete Math and Analyzing Social Graphs
- Calculus and Optimization for Machine Learning
- Probability Theory, Statistics and Exploratory Data Analysis
Good to know
Helps prepare learners for graduate studies in Data Science through HSE University's Master's program
Provides learners with an introduction to essential Linear Algebra concepts, foundational to Machine Learning
Suitable for experienced students in Matrix Analysis or Linear Algebra who wish to transition into using Linear Algebra for Data Science
Provides opportunities to develop foundational skills in Linear Algebra for those not well-versed in the topic
Instructors Dmitri Piontkovski and Vsevolod L. Chernyshev are recognized researchers in the field of Linear Algebra
Learners are advised to have prior knowledge of Matrix Analysis or Linear Algebra before taking this course
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