Fundamental Linear Algebra Concepts with Python from Coursera

What's inside

Syllabus

Introduction to Finding Inverses

In module 1, you’ll learn how to define linear equations, how to use Python to find the determinant of matrices and how to perform different commands using Python. We will cover the following learning objectives.

Introduction to Matrix Algebra with Python

Let’s recap! In module 1, you learned how to define linear equations, how to use Python to find the determinant of matrices and how to perform different commands using Python. In module 2, you’ll learn how to explain different matrix algebra functions, perform matrix algebra on large data sets using Python. We will cover the following learning objectives.

Solving Systems of Linear Equations

Let’s recap! In module 2, you learned how to explain different matrix algebra functions and perform matrix algebra on large data sets using Python. In module 3, you will learn how to solve systems of linear equations using several methods. We will cover the following learning objectives.

Eigenvalues and Eigenvectors

Welcome to the final module of this course! Over the past 3 modules, you have been introduced to and gained knowledge on the following topics: determinants, inverses, matrix algebra with Python, row reduction and, systems of linear equations. In the final module of the course, you’ll apply what you’ve learned to concrete, real-world examples. You’ll practice using linear transformation, Eigenvalues and Eigenvectors, and solving applications. We will cover the following learning objectives.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a solid mathematical foundation in linear algebra, an essential area of knowledge in engineering, computer science, and data science

Develops skills in manipulating, representing, and solving linear equations and matrices, which are widely applicable in various domains

Introduces the concept of linear transformations and their applications in real-world scenarios, enhancing the understanding of vector spaces

Teaches the computation of determinants and inverses of matrices, which are essential concepts for solving systems of linear equations

Provides hands-on practice in applying matrix algebra and linear transformations using Python, which enhances the practical understanding of the concepts

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 Fundamental Linear Algebra Concepts with Python with these activities:

Review Python basics

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Familiarize yourself with the basics of Python to ensure a stronger conceptual foundation and smoother sailing through the course.

Browse courses on Python

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Revisit tutorials on Python syntax and data types
Solve simple coding problems using Python
Review documentation for commonly used Python libraries

Participate in study groups

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Engage in peer-led study sessions to discuss concepts, solve problems collaboratively, and reinforce your understanding of the course material.

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Form or join a study group with peers
Establish a regular meeting schedule
Take turns presenting concepts and leading discussions

Explore Python libraries for matrix operations

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Gain familiarity with Python libraries that streamline matrix operations, complementing your understanding of matrix algebra techniques.

Browse courses on Python Libraries

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Identify suitable Python libraries for matrix manipulation
Follow tutorials on implementing matrix operations using these libraries
Experiment with the libraries to solve practical problems

Four other activities

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Solve linear equation exercises

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Practice solving linear equations to enhance your understanding of the concepts and techniques covered in the course.

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Attempt practice problems on finding inverses
Solve linear equation systems using row reduction
Apply your knowledge to real-world scenarios

Develop a cheat sheet for linear transformations

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Create a concise cheat sheet summarizing key concepts and formulas related to linear transformations, aiding in quick reference and knowledge retention.

Browse courses on Linear Transformations

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Gather relevant information on linear transformations
Organize and present the information in a clear and concise manner
Review and refine the cheat sheet for effectiveness

Participate in online coding challenges

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Test and refine your Python skills and problem-solving abilities by participating in coding challenges related to linear algebra and matrix operations.

Browse courses on Coding Challenges

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Identify online platforms that host coding challenges
Select challenges that align with the course topics
Attempt to solve the challenges within the specified time limit

Assist fellow students as a tutor

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Sharpen your understanding of the course concepts by explaining them to others, reinforcing your knowledge and fostering a collaborative learning environment.

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Identify opportunities to provide tutoring or assistance
Prepare and plan your tutoring sessions
Provide clear and patient explanations to your tutees

Career center

Learners who complete Fundamental Linear Algebra Concepts with Python will develop knowledge and skills that may be useful to these careers:

Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. This course may be useful for a Machine Learning Researcher because it provides a foundation in linear algebra, which is used in many machine learning algorithms.

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Machine Learning Engineer

Machine Learning Engineers use machine learning algorithms to solve problems and make predictions. This course may be useful for a Machine Learning Engineer because it provides a foundation in linear algebra, which is used in many machine learning algorithms.

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Data Scientist

Data Scientists use data to solve problems and make better decisions. This course may be useful for a Data Scientist because it provides a foundation in linear algebra, which is used in many data science techniques.

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Data Analyst

Data Analysts use data to solve problems and make better decisions. This course may be useful for a Data Analyst because it provides a foundation in linear algebra, which is used in many data analysis techniques.

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Statistician

Statisticians use statistical methods to collect, analyze, and interpret data. This course may be useful for a Statistician because it provides a foundation in linear algebra, which is used in many statistical techniques.

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Quantitative Researcher

Quantitative Researchers use mathematical and statistical models to make investment decisions. This course may be useful for a Quantitative Researcher because it provides a foundation in linear algebra, which is used in many quantitative finance models.

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Risk Analyst

Risk Analysts use mathematical and statistical techniques to assess risk and make informed decisions. This course may be useful for a Risk Analyst because it provides a foundation in linear algebra, which is used in many risk assessment models.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to make investment decisions. This course may be useful for a Quantitative Analyst because it provides a foundation in linear algebra, which is used in many quantitative finance models.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to improve the efficiency of organizations. This course may be useful for an Operations Research Analyst because it provides a foundation in linear algebra, which is used in many operations research models.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for a Software Engineer because it provides a foundation in linear algebra, which is used in many software applications.

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Financial Analyst

Financial Analysts use financial data to make investment recommendations. This course may be useful for a Financial Analyst because it provides a foundation in linear algebra, which is used in many financial models.

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Financial Engineer

Financial Engineers use mathematical and financial models to develop new financial products and strategies. This course may be useful for a Financial Engineer because it provides a foundation in linear algebra, which is used in many financial engineering models.

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Actuary

Actuaries use mathematical and statistical skills to assess risk and make informed decisions. This course may be useful for an Actuary because it provides a foundation in linear algebra and matrix algebra, both of which are heavily used in the field of actuarial science.

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Teacher

Teachers educate students at all levels. This course may be useful for a Teacher because it provides a foundation in linear algebra, which is taught in many math and science courses.

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Researcher

Researchers use scientific methods to conduct research and discover new knowledge. This course may be useful for a Researcher because it provides a foundation in linear algebra, which is used in many research fields.

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