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Supervised Machine Learning: Regression and Classification | Coursera
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DeepLearning.AI

Supervised Machine Learning: Regression and Classification

DeepLearning.AI

# Supervised Machine Learning: Regression and Classification

Taught in English

21 languages available

Some content may not be translated

Andrew Ng

Aarti Bagul

Geoff Ladwig

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## Instructors

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

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5.0(8,339 ratings)

Andrew Ng

Top Instructor

DeepLearning.AI

45 Courses•7,620,936 learners

Aarti Bagul

Top Instructor

3 Courses•762,714 learners

Geoff Ladwig

Top Instructor

DeepLearning.AI

3 Courses•762,714 learners

OK

Top Instructor

Enroll for Free

Starts Sep 13

Financial aid available

731,053 already enrolled

## Course

Gain insight into a topic and learn the fundamentals

4.9

(22,170 reviews)

|

98%

Beginner level

Recommended experience

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## Recommended experience

Beginner level

Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)

Other math concepts will be explained

OK

33 hours (approximately)

Flexible schedule

Learn at your own pace

## What you'll learn

- Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn

- Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

## Skills you'll gain

## Details to know



Shareable certificate

Add to your LinkedIn profile

Assessments

9 quizzes

## Course

Gain insight into a topic and learn the fundamentals

4.9

(22,170 reviews)

|

98%

Beginner level

Recommended experience

Close

## Recommended experience

Beginner level

Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)

Other math concepts will be explained

OK

33 hours (approximately)

Flexible schedule

Learn at your own pace

# See how employees at top companies are mastering in-demand skills

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## Build your subject-matter expertise

When you enroll in this course, you'll also be enrolled in this Specialization.

- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate

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## Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

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## There are 3 modules in this course

In the first course of the Machine Learning Specialization, you will:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

### Week 1: Introduction to Machine Learning

Module 1•7 hours to complete

Module details

Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!

#### What's included

20 videos1 reading3 quizzes1 app item4 ungraded labs

Show info about module content

##### 20 videos•Total 146 minutes

- Welcome to machine learning!•2 minutes• Preview module
- Applications of machine learning•4 minutes
- What is machine learning?•5 minutes
- Supervised learning part 1•6 minutes
- Supervised learning part 2•7 minutes
- Unsupervised learning part 1•8 minutes
- Unsupervised learning part 2•3 minutes
- Jupyter Notebooks•4 minutes
- Linear regression model part 1•10 minutes
- Linear regression model part 2•6 minutes
- Cost function formula•9 minutes
- Cost function intuition•15 minutes
- Visualizing the cost function•8 minutes
- Visualization examples•6 minutes
- Gradient descent•8 minutes
- Implementing gradient descent•9 minutes
- Gradient descent intuition•7 minutes
- Learning rate•9 minutes
- Gradient descent for linear regression•6 minutes
- Running gradient descent•5 minutes

##### 1 reading•Total 2 minutes

- [IMPORTANT] Have questions, issues or ideas? Join our Forum!•2 minutes

##### 3 quizzes•Total 35 minutes

- Practice quiz: Supervised vs unsupervised learning•15 minutes
- Practice quiz: Regression•10 minutes
- Practice quiz: Train the model with gradient descent•10 minutes

##### 1 app item•Total 1 minute

- Intake Survey•1 minute

##### 4 ungraded labs•Total 240 minutes

- Python and Jupyter Notebooks•60 minutes
- Optional lab: Model representation•60 minutes
- Optional lab: Cost function•60 minutes
- Optional lab: Gradient descent•60 minutes

### Week 2: Regression with multiple input variables

Module 2•9 hours to complete

Module details

This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.

#### What's included

10 videos2 quizzes1 programming assignment5 ungraded labs

Show info about module content

##### 10 videos•Total 66 minutes

- Multiple features•9 minutes• Preview module
- Vectorization part 1•6 minutes
- Vectorization part 2•6 minutes
- Gradient descent for multiple linear regression•7 minutes
- Feature scaling part 1•6 minutes
- Feature scaling part 2•7 minutes
- Checking gradient descent for convergence•5 minutes
- Choosing the learning rate•6 minutes
- Feature engineering•3 minutes
- Polynomial regression•5 minutes

##### 2 quizzes•Total 45 minutes

- Practice quiz: Multiple linear regression•15 minutes
- Practice quiz: Gradient descent in practice•30 minutes

##### 1 programming assignment•Total 180 minutes

- Week 2 practice lab: Linear regression•180 minutes

##### 5 ungraded labs•Total 300 minutes

- Optional lab: Python, NumPy and vectorization•60 minutes
- Optional Lab: Multiple linear regression•60 minutes
- Optional Lab: Feature scaling and learning rate•60 minutes
- Optional lab: Feature engineering and Polynomial regression•60 minutes
- Optional lab: Linear regression with scikit-learn•60 minutes

### Week 3: Classification

Module 3•16 hours to complete

Module details

This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!

#### What's included

12 videos2 readings4 quizzes1 programming assignment9 ungraded labs

Show info about module content

##### 12 videos•Total 139 minutes

- Motivations•9 minutes• Preview module
- Logistic regression•9 minutes
- Decision boundary•10 minutes
- Cost function for logistic regression•11 minutes
- Simplified Cost Function for Logistic Regression•5 minutes
- Gradient Descent Implementation•6 minutes
- The problem of overfitting•11 minutes
- Addressing overfitting•8 minutes
- Cost function with regularization•9 minutes
- Regularized linear regression•8 minutes
- Regularized logistic regression•5 minutes
- Andrew Ng and Fei-Fei Li on Human-Centered AI•41 minutes

##### 2 readings•Total 4 minutes

- [IMPORTANT] Reminder about end of access to Lab Notebooks•2 minutes
- Acknowledgments•2 minutes

##### 4 quizzes•Total 120 minutes

- Practice quiz: Classification with logistic regression•30 minutes
- Practice quiz: Cost function for logistic regression•30 minutes
- Practice quiz: Gradient descent for logistic regression•30 minutes
- Practice quiz: The problem of overfitting•30 minutes

##### 1 programming assignment•Total 180 minutes

- Week 3 practice lab: logistic regression•180 minutes

##### 9 ungraded labs•Total 540 minutes

- Optional lab: Classification•60 minutes
- Optional lab: Sigmoid function and logistic regression•60 minutes
- Optional lab: Decision boundary•60 minutes
- Optional lab: Logistic loss•60 minutes
- Optional lab: Cost function for logistic regression•60 minutes
- Optional lab: Gradient descent for logistic regression•60 minutes
- Optional lab: Logistic regression with scikit-learn•60 minutes
- Optional lab: Overfitting•60 minutes
- Optional lab: Regularization•60 minutes

### Instructors

Instructor ratings

Close

## Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

OK

5.0(8,339 ratings)

Andrew Ng

Top Instructor

DeepLearning.AI

45 Courses•7,620,936 learners

View all 3 instructors

Close

## Instructors

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

Close

5.0(8,339 ratings)

Andrew Ng

Top Instructor

DeepLearning.AI

45 Courses•7,620,936 learners

Aarti Bagul

Top Instructor

3 Courses•762,714 learners

Geoff Ladwig

Top Instructor

DeepLearning.AI

3 Courses•762,714 learners

OK

### Offered by



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## Offered by



DeepLearning.AI is an education technology company that develops a global community of AI talent.

DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.

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Close

## Offered by



The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

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### Recommended if you're interested in Machine Learning

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## Why people choose Coursera for their career



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Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."



Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."



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"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

## Learner reviews

Showing 3 of 22170

4.9
22,170 reviews

- 5 stars

91.70%

- 4 stars

7.21%

- 3 stars

0.63%

- 2 stars

0.18%

- 1 star

0.26%

J

JM

5
Reviewed on Sep 21, 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

A

AD

5
Reviewed on Nov 23, 2022

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

W

WB

4
Reviewed on May 23, 2024

amazing course and super easy to follow. my only problem is that it doesn't delve too deeply into the math and science of things and focuses more on practical applications rather than how things work

## New to Machine Learning? Start here.

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## Frequently asked questions

### When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

### What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

### What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policyOpens in a new tab.

### Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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### More questions

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