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Linear Regression with NumPy and Python

Snehan Kekre

Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed.

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What's inside

Syllabus

Project: Linear Regression with NumPy and Python

Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various algorithms yourself, so you have a deeper understanding of the fundamentals.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops deep learning concepts and fundamentals for those without a strong math and programming background

Lays a solid foundation in linear regression with NumPy and Python, suitable for beginners

Teaches essential machine learning concepts without relying on popular libraries, fostering a deep understanding of algorithms

Requires learners to come in with some programming experience and basic Python knowledge to grasp the presented concepts

Uses pre-configured cloud desktops, providing a seamless hands-on learning experience

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Reviews summary

Linear regression made practical

Students largely agree that this highly rated course is a good way to learn the basics of linear regression in Python and NumPy. Engaging guided projects provide learners with an opportunity to practice what they learn and apply it to real-world problems. The instructor is knowledgeable and presents the material in a clear and concise manner. However, some learners have complained about the slow speed of the course platform and the lack of detailed explanations in some areas.

The instructor presents the content in a clear and concise way.

"The instructor is very well equipped with the knowledge and guides us through the project very well."

"Simple and efficient."

"Being able to code along with the video was so helpful, too."

Hands-on guided projects help students understand the concepts better.

"Guided projects are just amazing while working around Hands-on side by side which gives quite Good Understanding."

"This project was just the right one to get me started on my path to machine learning."

"I have read many articles and enrolled in several courses attempting to teach linear regression from scratch. This course provides the best balance of sufficient math to enable a deeper understanding and the practicality of seeing a simple implementation of the algorithm actually working in numpy."

Some learners have complained about the lack of detail in some of the explanations.

"The rhyme was very slow.....and in poor quality...video quality was intermediate but software quality is poor."

"The instructor is very well equipped with the knowledge and guides us through the project very well, its only the slow loading of the instructors video that made the learning experience bad."

"It was helpful as recently I had seen a linear regression problem which was too complicated. But this project helped me understand the basics properly to continue my interest in Python language. It was interesting. Thank You"

The course platform can be slow.

"The server is too slow."

"The important points should be discussed thoroughly, The instructor was not able to make the user understand why he was using this and that part, he lacked some teaching skills."

"The course would have been better if the project was a little bigger and not just the coding of gradient descent."

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 Regression with NumPy and Python with these activities:

Review your previous knowledge of algebra

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Refresh your knowledge of algebra to fill in any gaps in your understanding before the course begins.

Browse courses on Algebra

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Go over your notes from previous algebra courses.
Take practice problems to test your understanding.

Review the course textbook

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Get a head start on the course material by reviewing the course textbook before the class starts.

View Introduction to Linear Algebra (Gilbert Strang) on Amazon

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Organize your notes and course materials

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Stay organized by compiling your notes, assignments, and other course materials into a central location for easy reference and review.

Browse courses on Linear Algebra

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Create a dedicated folder or notebook for the course.
Regularly add and organize your notes, assignments, and any relevant resources.
Review your organized materials periodically to reinforce your understanding.

Five other activities

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Watch video tutorials on linear algebra concepts

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Supplement your learning by watching video tutorials that provide clear explanations and visual demonstrations of linear algebra concepts.

Browse courses on Linear Algebra

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Search for video tutorials on YouTube or other online platforms.
Choose tutorials that align with the course topics and your learning style.
Take notes and pause the videos when needed to fully understand the concepts.

Practice solving linear equations and systems of equations

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Gain proficiency in solving linear equations and systems of equations, which are fundamental concepts in linear algebra.

Browse courses on Linear Equations

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Find practice problems online or in textbooks.
Set aside time each week to practice solving problems.
Check your solutions against answer keys or online resources.

Develop a cheat sheet of formulas and concepts

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Create a personalized resource summarizing key formulas and concepts, which can aid in understanding and retention.

Browse courses on Linear Algebra

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Gather formulas and concepts from course materials.
Organize and condense the information into a concise cheat sheet.
Review the cheat sheet regularly to reinforce your understanding.

Join a study group or participate in online forums

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Engage with peers to discuss course material, ask questions, and clarify concepts, fostering a deeper understanding.

Browse courses on Linear Algebra

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Find a study group or join online forums related to linear algebra.
Actively participate in discussions and ask thoughtful questions.
Collaborate with others to solve problems and exchange ideas.

Contribute to open-source linear algebra projects

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Gain hands-on experience by contributing to open-source projects related to linear algebra, deepening your understanding and practical skills.

Browse courses on Linear Algebra

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Identify open-source linear algebra projects on platforms like GitHub.
Choose a project that aligns with your interests and skill level.
Read the project documentation and contribute code or documentation improvements.

Career center

Learners who complete Linear Regression with NumPy and Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their strong background in mathematics and statistics to extract knowledge from data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models is a must-have for Data Scientists. This course will help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use their strong background in mathematics and statistics to develop and implement financial models. They work closely with other teams to communicate the meaning of these models. The ability to implement linear regression models is a must-have for Quantitative Analysts. This course will help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians use their strong background in mathematics and statistics to collect, analyze, and interpret data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models is a must-have for Statisticians. This course will help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers create the models that are used to make predictions. They use their strong background in computer science and statistics to find patterns in data. The ability to implement linear regression models is a must-have for Machine Learning Engineers. This course will help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts use their strong background in mathematics to clean up and interpret raw data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models will greatly assist in performing these tasks. This course will help you build a foundation in linear regression, so you may be more effective at performing these tasks.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain computer software. They use their strong background in computer science to create software applications that meet the needs of users. The ability to implement linear regression models may be helpful in various software engineering roles. This course may help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Software Engineer

Financial Analyst

Financial Analysts use their strong background in finance and economics to analyze financial data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models may be helpful for Financial Analysts. This course may help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use their strong background in mathematics and statistics to assess risk. They work closely with other teams to communicate the meaning of this risk. The ability to implement linear regression models may be helpful for Actuaries. This course may help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Actuary

Operations Research Analyst

Operations Research Analysts use their strong background in mathematics and statistics to solve problems. They work closely with other teams to communicate the meaning of these solutions. The ability to implement linear regression models may be helpful for Operations Research Analysts. This course may help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

Business Analysts use their strong background in business and technology to solve problems. They work closely with other teams to communicate the meaning of these solutions. The ability to implement linear regression models may be helpful for Business Analysts. This course may help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers use their strong background in marketing and research to collect and analyze data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models may be helpful for Market Researchers. This course may help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Market Researcher

Economist

Economists use their strong background in economics to analyze data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models may be helpful for Economists. This course may help you build a foundation in linear regression, so you may be more effective in this role.

See salaries and explore the career path for Economist

Reading list

We've selected 15 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 Regression with NumPy and Python.

The Elements of Statistical Learning

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More advanced treatment of statistical learning than the previous book. It covers a wider range of topics, including linear and nonlinear regression, classification, and clustering.

Linear Regression with NumPy and Python

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Linear regression made practical

Activities

Career center

Reading list

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