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Amit Yadav
In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.
Read more
In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.
Read more
In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.
Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/cp6zld/linear
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What's inside
Syllabus
Linear Regression with Python
In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process. Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches linear regression with Python and Numpy, which are essential skills for understanding machine learning and deep learning
Assumes a theoretical understanding of linear regression and gradient descent
Provides a practical, project-based approach to implementing linear regression
Suitable for learners based in the North America region only
Save this course
Save Linear Regression with Python to your list so you can find it easily later:
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Reviews summary
Beginner-friendly linear regression in python
Learners say this course is a solid choice for beginner learners seeking an introduction to linear regression in Python. It's highly engaging as it provides hands-on projects with clear explanations. Students particularly appreciate the well-structured and practical content.
Organized and intuitive presentation of material
"The project is well-structured and practical."
"It introduces the fundamentals of linear regression and its implementation using Python in a very intuitive way."
Concepts well-explained, making complex ideas accessible
"The instructor's clear explanations make complex concepts accessible."
"I appreciate that the instructor explains with enough detail the steps to be followed. Even as a rookie in Python, understanding how to create the model was easy thanks to him."
Suitable for beginners with little to no knowledge
"This course is a solid choice for beginner learners seeking an introduction to linear regression in Python."
"If you aren't familiar with the mechanics of linear regression and the gradient descent algorithm, take a look before you do the project."
"Though some parts could have been explained in a more detailed manner, those who have some basic knowledge of Python and higher-level mathematics can easily do this course."
Engaging with hands-on projects and real-world experience
"It's highly engaging as it provides hands-on projects with clear explanations."
"The hands-on projects provide valuable real-world experience."
"I have followed some courses related to linear regression already and being able to put the theory to practice with this project was really good."
Occasional lags and glitches experienced with the platform
"The VM got a little glitchy a couple of times, but nothing too bad."
"I found that rhyme only started up about 2/3's of the time, I had to restart it."
"Rhyme platform lags a bit."
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 Python with these
activities:
Connect with experienced professionals
Show steps
Build connections and gain valuable insights from experts in the field.
Browse courses on
Linear Regression
Show steps
-
Identify potential mentors on LinkedIn or industry-specific platforms
-
Reach out to them and introduce yourself
-
Set up meetings or video calls to connect with them
Review materials from MIT OpenCourseWare
Show steps
Help strengthen your foundational understanding of the skills you will use throughout this class.
Browse courses on
Gradient Descent
Show steps
-
Review Differential Calculus
-
Review Matrix Multiplication
-
Review Optimization
Review material from prerequisite courses
Show steps
Ensure that you have a solid understanding of the concepts covered in this course.
Show steps
-
Review notes from Calculus I focusing on derivatives and integrals
-
Review matrix operations from Linear Algebra
-
Review Python basics and data structures
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete exercises on Coursera
Show steps
Gain hands-on experience with the concepts and tools you will learn in this course.
Browse courses on
Linear Regression
Show steps
-
Complete all of the exercises in the Coursera course
Solve practice problems on Leetcode
Show steps
Deepen your understanding of implementing Linear Regression with Gradient Descent using Python.
Browse courses on
Linear Regression
Show steps
-
Solve 10 easy difficulty Leetcode problems on linear regression
-
Solve 5 medium difficulty Leetcode problems on linear regression
Write a blog post about your learning experience
Show steps
Reflect on what you have learned and share your insights with others.
Browse courses on
Linear Regression
Show steps
-
Choose a topic to write about
-
Write a blog post about your experience learning about linear regression
-
Publish your blog post and share it with others
Build a linear regression model from scratch
Show steps
Demonstrate your mastery of implementing linear regression from scratch.
Browse courses on
Linear Regression
Show steps
-
Collect a dataset for your model
-
Implement linear regression using Python and Numpy
-
Evaluate your model's performance
-
Write a report summarizing your work
Connect with experienced professionals
Show steps
Build connections and gain valuable insights from experts in the field.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify potential mentors on LinkedIn or industry-specific platforms
- Reach out to them and introduce yourself
- Set up meetings or video calls to connect with them
Review materials from MIT OpenCourseWare
Show steps
Help strengthen your foundational understanding of the skills you will use throughout this class.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Review Differential Calculus
- Review Matrix Multiplication
- Review Optimization
Review material from prerequisite courses
Show steps
Ensure that you have a solid understanding of the concepts covered in this course.
Show steps
Show steps
Show steps
- Review notes from Calculus I focusing on derivatives and integrals
- Review matrix operations from Linear Algebra
- Review Python basics and data structures
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete exercises on Coursera
Show steps
Gain hands-on experience with the concepts and tools you will learn in this course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Complete all of the exercises in the Coursera course
Solve practice problems on Leetcode
Show steps
Deepen your understanding of implementing Linear Regression with Gradient Descent using Python.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Solve 10 easy difficulty Leetcode problems on linear regression
- Solve 5 medium difficulty Leetcode problems on linear regression
Write a blog post about your learning experience
Show steps
Reflect on what you have learned and share your insights with others.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Choose a topic to write about
- Write a blog post about your experience learning about linear regression
- Publish your blog post and share it with others
Build a linear regression model from scratch
Show steps
Demonstrate your mastery of implementing linear regression from scratch.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Collect a dataset for your model
- Implement linear regression using Python and Numpy
- Evaluate your model's performance
- Write a report summarizing your work
Career center
Learners who complete Linear Regression with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns that can help businesses make better decisions. This course will help you build a foundation in linear regression, which is a fundamental technique for analyzing data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Data Analyst.
Data Scientist
Data Scientist
Data Scientists use their understanding of machine learning and computer science to analyze data and build models that can help businesses make better decisions. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Data Scientist.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve problems in business and industry. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Operations Research Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling financial data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Quantitative Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Machine Learning Engineer.
Business Analyst
Business Analyst
Business Analysts use data to help businesses improve their operations and make better decisions. This course will help you build a foundation in linear regression, which is a fundamental technique for analyzing data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Business Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling financial data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Financial Analyst.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Statistician.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Software Engineer who wants to work on machine learning or data science projects.
Risk Analyst
Risk Analyst
Risk Analysts use mathematical and statistical techniques to assess risk and uncertainty. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Risk Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Actuary.
Investment Analyst
Investment Analyst
Investment Analysts use mathematical and statistical techniques to analyze investment opportunities. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling financial data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Investment Analyst.
Data Scientist Intern
Data Scientist Intern
Data Scientist Interns work on machine learning and data science projects under the supervision of experienced Data Scientists. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Data Scientist Intern.
Data Engineer
Data Engineer
Data Engineers design and build systems for collecting, storing, and processing data. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Data Engineer who wants to work on machine learning or data science projects.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course will help you build a foundation in linear regression, which is a fundamental technique for modeling data. You will also learn how to use Python and Numpy to implement linear regression models. This knowledge will be valuable for any aspiring Machine Learning Researcher.
For more career information including salaries, visit:
OpenCourser.com/course/cp6zld/linear
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 Python.
Classic textbook on machine learning and statistics. It covers a wide range of topics, including linear regression, and is known for its clear and concise explanations.
Gives a very practical and hands-on approach to machine learning using Python. It covers a wide range of topics, including linear regression, which is the focus of the course.
Provides a comprehensive introduction to statistical learning, including linear regression. It is written by leading researchers in the field and is widely used as a textbook for graduate-level courses in machine learning and statistics.
Provides a comprehensive and detailed treatment of statistical methods for machine learning. It covers a wide range of topics, including linear regression, and is suitable for advanced learners and researchers.
Hands-on guide to machine learning using Scikit-Learn and TensorFlow, two of the most popular machine learning libraries. It provides comprehensive coverage of linear regression and other machine learning algorithms.
Save
Comprehensive and theoretical treatment of pattern recognition and machine learning, covering a wide range of topics, including a mathematical background on real analysis and linear algebra for the application of statistical models.
Save
Comprehensive and in-depth treatment of machine learning. It covers a wide range of topics, including linear regression, and is suitable for advanced learners and researchers.
Focuses on the practical aspects of linear regression, such as model selection, inference, and prediction. It is written for applied researchers and provides a good balance of theory and practice.
Gentle introduction to machine learning and its practical applications using Python. It provides examples and exercises to help the reader understand the concepts. However, it is good as a supplementary reading rather than as a stand-alone book.
Save
Provides a comprehensive and practical introduction to data mining and machine learning. It covers a wide range of topics, including linear regression, and is suitable for both beginners and advanced learners.
Save
Provides a practical and hands-on approach to predictive modeling using R. It covers a wide range of topics, including linear regression, and good resource for applied researchers and data scientists.
Comprehensive and theoretical treatment of linear models, including linear regression. It provides a detailed discussion of model selection, inference, and prediction, well-suited for advanced learners and researchers.
Provides a probabilistic approach to machine learning, covering a wide range of topics, including linear regression. It is suitable for advanced learners who have a strong background in probability and statistics.
Comprehensive and detailed treatment of linear regression. It provides a rigorous mathematical foundation for the subject and covers a wide range of topics, including model selection, inference, and prediction. It is suitable for advanced learners who want to deepen their understanding of linear regression.
Save
Comprehensive and in-depth treatment of deep learning. It is suitable for advanced learners and researchers who want to learn about the latest advances in deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cp6zld/linear
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Teaches linear regression with Python and Numpy, which are essential skills for understanding machine learning and deep learning
Assumes a theoretical understanding of linear regression and gradient descent
Provides a practical, project-based approach to implementing linear regression
Suitable for learners based in the North America region only
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