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Luis Serrano
Newly updated for 2024!
After completing this course, learners will be able to:
• Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.
Read more
Newly updated for 2024!
After completing this course, learners will be able to:
• Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.
Read more
Newly updated for 2024!
After completing this course, learners will be able to:
• Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.
• Apply common vector and matrix algebra operations like dot product, inverse, and determinants
• Express certain types of matrix operations as linear transformations
• Apply concepts of eigenvalues and eigenvectors to machine learning problems
Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning.
Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works.
Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.
This is a beginner-friendly program, with a recommended background of at least high school mathematics (functions, basic algebra). We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries), as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2: Calculus for Machine Learning and Data Science and Course 3: Probability and Statistics for Machine Learning and Data Science, of this specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/0ng7es/linear
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What's inside
Syllabus
Week 1: Systems of linear equations
Matrices are commonly used in machine learning and data science to represent data and its transformations. In this week, you will learn how matrices naturally arise from systems of equations and how certain matrix properties can be thought in terms of operations on system of equations.
Read more
Syllabus
Week 1: Systems of linear equations
Matrices are commonly used in machine learning and data science to represent data and its transformations. In this week, you will learn how matrices naturally arise from systems of equations and how certain matrix properties can be thought in terms of operations on system of equations.
Read more
Week 2: Solving systems of linear equations
In this week, you will learn how to solve a system of linear equations using the elimination method and the row echelon form. You will also learn about an important property of a matrix: the rank. The concept of the rank of a matrix is useful in computer vision for compressing images.
Week 3: Vectors and Linear Transformations
An individual instance (observation) of data is typically represented as a vector in machine learning. In this week, you will learn about properties and operations of vectors. You will also learn about linear transformations, matrix inverse, and one of the most important operations on matrices: the matrix multiplication. You will see how matrix multiplication naturally arises from composition of linear transformations. Finally, you will learn how to apply some of the properties of matrices and vectors that you have learned so far to neural networks.
Week 4: Determinants and Eigenvectors
In this final week, you will take a deeper look at determinants. You will learn how determinants can be geometrically interpreted as an area and how to calculate determinant of product and inverse of matrices. We conclude this course with eigenvalues and eigenvectors. Eigenvectors are used in dimensionality reduction in machine learning. You will see how eigenvectors naturally follow from the concept of eigenbases.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the core mathematical concepts in machine learning and data science
Provides innovative pedagogy in mathematics to help learners understand concepts quickly
Uses visualizations to help learners see how the math works in real-world applications
Builds a foundation for learners to understand and apply more complex machine learning algorithms
Taught by Luis Serrano, who is renowned in the field of machine learning
Students are expected to have a high school level understanding of mathematics and a basic familiarity with Python
Save this course
Save Linear Algebra for Machine Learning and Data Science to your list so you can find it easily later:
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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 Linear Algebra for Machine Learning and Data Science with these
activities:
Read 'Linear Algebra Done Right'
Show steps
Complement your course materials with an in-depth reference to enhance your understanding of linear algebra.
View
Linear Algebra Done Right (Undergraduate Texts...
on Amazon
Show steps
-
Read selected chapters relevant to the course topics.
-
Work through practice problems to reinforce your understanding.
-
Use the book as a resource to clarify concepts and explore advanced topics.
Review matrix basics
Show steps
Familiarize yourself with the basics of matrices, including definitions, operations, and properties.
Browse courses on
Matrices
Show steps
-
Review the definition and types of matrices.
-
Practice basic matrix operations, such as addition, subtraction, and multiplication.
-
Identify and understand matrix properties, such as singularity and rank.
Participate in study groups
Show steps
Enhance your learning by collaborating with peers and discussing concepts in study groups.
Show steps
-
Join or form a study group with other students.
-
Discuss course material, share insights, and work through problems together.
-
Provide feedback and support to your peers.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve systems of linear equations
Show steps
Reinforce your understanding of solving systems of linear equations by practicing various techniques.
Browse courses on
Systems of Linear Equations
Show steps
-
Practice solving systems using the elimination method.
-
Identify and use the row echelon form to solve systems.
-
Apply matrix properties to simplify and solve systems.
Explain matrix transformations
Show steps
Deepen your understanding of matrix transformations by explaining their concepts and applications.
Browse courses on
Linear Transformations
Show steps
-
Define linear transformations and their properties.
-
Illustrate how matrices represent linear transformations.
-
Apply matrix transformations to real-world scenarios.
Explore the applications of eigenvalues and eigenvectors
Show steps
Expand your knowledge by investigating the applications of eigenvalues and eigenvectors in machine learning and data science.
Browse courses on
Eigenvalues
Show steps
-
Identify the concepts of eigenvalues and eigenvectors.
-
Understand the geometric interpretation of eigenvalues and eigenvectors.
-
Explore how eigenvalues and eigenvectors are used in machine learning algorithms, such as dimensionality reduction.
Develop a mathematical model using matrices
Show steps
Apply your knowledge to a practical project by creating a mathematical model that utilizes matrices.
Browse courses on
Matrix Operations
Show steps
-
Identify a real-world problem that can be modeled using matrices.
-
Develop the mathematical model using matrices to represent the variables and relationships.
-
Solve the model using matrix operations.
-
Validate and interpret the results in the context of the problem.
Build a machine learning application using matrix operations
Show steps
Integrate your learning by developing a machine learning application that involves matrix operations.
Browse courses on
Machine Learning
Show steps
-
Choose a machine learning task, such as classification or regression.
-
Gather and prepare data for your application.
-
Design and implement a machine learning model that utilizes matrix operations.
-
Train and evaluate your model.
-
Deploy your application and monitor its performance.
Read 'Linear Algebra Done Right'
Show steps
Complement your course materials with an in-depth reference to enhance your understanding of linear algebra.
View
Linear Algebra Done Right (Undergraduate Texts...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters relevant to the course topics.
- Work through practice problems to reinforce your understanding.
- Use the book as a resource to clarify concepts and explore advanced topics.
Review matrix basics
Show steps
Familiarize yourself with the basics of matrices, including definitions, operations, and properties.
Browse courses on
Matrices
Show steps
Show steps
Show steps
- Review the definition and types of matrices.
- Practice basic matrix operations, such as addition, subtraction, and multiplication.
- Identify and understand matrix properties, such as singularity and rank.
Participate in study groups
Show steps
Enhance your learning by collaborating with peers and discussing concepts in study groups.
Show steps
Show steps
Show steps
- Join or form a study group with other students.
- Discuss course material, share insights, and work through problems together.
- Provide feedback and support to your peers.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve systems of linear equations
Show steps
Reinforce your understanding of solving systems of linear equations by practicing various techniques.
Browse courses on
Systems of Linear Equations
Show steps
Show steps
Show steps
- Practice solving systems using the elimination method.
- Identify and use the row echelon form to solve systems.
- Apply matrix properties to simplify and solve systems.
Explain matrix transformations
Show steps
Deepen your understanding of matrix transformations by explaining their concepts and applications.
Browse courses on
Linear Transformations
Show steps
Show steps
Show steps
- Define linear transformations and their properties.
- Illustrate how matrices represent linear transformations.
- Apply matrix transformations to real-world scenarios.
Explore the applications of eigenvalues and eigenvectors
Show steps
Expand your knowledge by investigating the applications of eigenvalues and eigenvectors in machine learning and data science.
Browse courses on
Eigenvalues
Show steps
Show steps
Show steps
- Identify the concepts of eigenvalues and eigenvectors.
- Understand the geometric interpretation of eigenvalues and eigenvectors.
- Explore how eigenvalues and eigenvectors are used in machine learning algorithms, such as dimensionality reduction.
Develop a mathematical model using matrices
Show steps
Apply your knowledge to a practical project by creating a mathematical model that utilizes matrices.
Browse courses on
Matrix Operations
Show steps
Show steps
Show steps
- Identify a real-world problem that can be modeled using matrices.
- Develop the mathematical model using matrices to represent the variables and relationships.
- Solve the model using matrix operations.
- Validate and interpret the results in the context of the problem.
Build a machine learning application using matrix operations
Show steps
Integrate your learning by developing a machine learning application that involves matrix operations.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a machine learning task, such as classification or regression.
- Gather and prepare data for your application.
- Design and implement a machine learning model that utilizes matrix operations.
- Train and evaluate your model.
- Deploy your application and monitor its performance.
Career center
Learners who complete Linear Algebra for Machine Learning and Data Science will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models to solve business problems. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Machine Learning Engineers. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Data Scientists. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Software Engineers who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Data Analysts who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to help businesses understand their customers and make better decisions. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Market Researchers who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Quantitative Analysts who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations and provide advice to businesses. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Financial Analysts who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Business Analyst
Business Analyst
Business Analysts use data to help businesses improve their operations and make better decisions. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Business Analysts who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Product Managers who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Consultant
Consultant
Consultants provide advice and expertise to businesses in a variety of areas. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Consultants who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical models to help businesses improve their operations and make better decisions. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Operations Research Analysts who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses make better decisions. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Statisticians who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess and manage risks for businesses. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Risk Analysts who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Data Engineer
Data Engineer
Data Engineers design and build the systems that store and process data for businesses. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Data Engineers who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess and manage risks for businesses. The Linear Algebra for Machine Learning and Data Science course teaches you the mathematical foundations of machine learning, which is a critical skill for Actuaries who want to work in the field of machine learning. This course will help you build a strong foundation in linear algebra, which will be essential for your success in this role.
For more career information including salaries, visit:
OpenCourser.com/course/0ng7es/linear
Reading list
We've selected six 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
Linear Algebra for Machine Learning and Data Science.
Classic textbook on linear algebra that provides a comprehensive introduction to the subject. It covers all of the topics that are covered in the course, and it does so in a clear and concise manner. The book is also well-written and engaging, making it a pleasure to read.
More advanced introduction to linear algebra. It covers all of the topics that are covered in the course, but it does so in a more rigorous and detailed manner. The book is also well-written and engaging, making it a pleasure to read.
Save
这本中文书是计算机程序设计艺术的好资料。它涵盖了本课程中涉及的所有主题，但采取了更应用的方式。这本书也写得很好，引人入胜，读起来很有趣。
More advanced treatment of linear algebra. It covers all of the topics that are covered in the course, but it does so in a more rigorous and detailed manner. The book is also well-written and engaging, making it a pleasure to read.
Great introduction to convex optimization. It covers all of the topics that are covered in the course, but it does so in a more applied manner. The book is also well-written and engaging, making it a pleasure to read.
Save
Graduate-level textbook on matrix analysis. It covers all of the topics that are covered in the course, but it does so in a more rigorous and detailed manner. The book is also well-written and engaging, making it a pleasure to read.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0ng7es/linear
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Effort
At the rate of 5 hours per week, it will take you around 4 weeks to complete.
Level
Introductory
Via
Coursera
Institution
DeepLearning.AI
Instructor
Luis Serrano
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Mathematics for Machine Learning and Data Science. It's comprised of three courses:
- Probability & Statistics for Machine Learning & Data Science
- Calculus for Machine Learning and Data Science
- Linear Algebra for Machine Learning and Data Science (viewing)
Good to know
Teaches the core mathematical concepts in machine learning and data science
Provides innovative pedagogy in mathematics to help learners understand concepts quickly
Uses visualizations to help learners see how the math works in real-world applications
Builds a foundation for learners to understand and apply more complex machine learning algorithms
Taught by Luis Serrano, who is renowned in the field of machine learning
Students are expected to have a high school level understanding of mathematics and a basic familiarity with Python
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