Machine Learning with Neural Networks

Grow your skills with Coursera Plus for $239/year (usually $399). Save now.

Machine Learning with Neural Networks

This course is part of Practical Machine Learning: Foundations to Neural Networks Specialization

Instructor: Peter Chin

Included with

Learn more

7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

How to build and train neural networks with backpropagation and regularization, and model predictive uncertainty using Bayesian neural networks.

Skills you'll gain

Tools you'll learn

Tensorflow

Details to know

Shareable certificate

Add to your LinkedIn profile

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

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Practical Machine Learning: Foundations to Neural Networks Specialization

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

There are 7 modules in this course

This course explores the principles of machine learning through the lens of one of its most powerful and versatile model classes: the artificial neural network. We will cover the fundamental machine learning concepts of modeling, training, and generalization. You will learn how to process the input data with feed-forward operations, how to train a neural network model using gradient-based optimization and the backpropagation algorithm, and how to ensure it performs well on new data using regularization. In the final module, we discuss Bayesian neural networks, learning how to build models that not only make predictions but also quantify their own uncertainty.

What's included

1 video1 reading

What's included

4 videos1 reading4 assignments

4 videos Total 27 minutes

Feed-Forward Network Functions - Part 1 4 minutes
Feed-Forward Network Functions - Part 2 11 minutes
Feed-Forward Network Functions - Part 3 5 minutes
Feed-Forward Network Functions - Part 4 7 minutes

1 reading Total 10 minutes

Module Overview: Feed-Forward Network Functions 10 minutes

4 assignments Total 45 minutes

Feed-Forward Network Functions - Part 1 10 minutes
Feed-Forward Network Functions - Part 2 10 minutes
Feed-Forward Network Functions - Part 3 10 minutes
Feed-Forward Network Functions - Part 4 15 minutes

What's included

5 videos2 readings6 assignments3 ungraded labs

5 videos Total 32 minutes

Error Backpropagation Algorithm - Part 1 13 minutes
Error Backpropagation Algorithm - Part 2 5 minutes
Error Backpropagation Algorithm - Part 3 6 minutes
Error Backpropagation Algorithm - Part 4 6 minutes
Error Backpropagation Algorithm - Part 5 2 minutes

2 readings Total 25 minutes

Module Overview: Error Backpropagation Algorithm 10 minutes
Lab Overview: Feed-Forward Network Functions (Read First!) 15 minutes

6 assignments Total 95 minutes

Error Backpropagation Algorithm - Part 1 10 minutes
Error Backpropagation Algorithm - Part 2 10 minutes
Error Backpropagation Algorithm - Part 3 10 minutes
Error Backpropagation Algorithm - Part 4 15 minutes
Implementing a 2-Layer Neural Network By Hand - Part 2 30 minutes
Feed-Forward Network Functions Lab - Part 2 20 minutes

3 ungraded labs Total 560 minutes

Implementing a 2-Layer Neural Network By Hand - Part 1 240 minutes
Feed-Forward Network Functions Lab - Part 1 300 minutes
Feed-Forward Network Functions Lab - Solution 20 minutes

What's included

6 videos2 readings7 assignments2 ungraded labs

6 videos Total 52 minutes

Regularization in Neural Networks - Part 1A 8 minutes
Regularization in Neural Networks - Part 1B 6 minutes
Regularization in Neural Networks - Part 1C 11 minutes
Regularization in Neural Networks - Part 2A 4 minutes
Regularization in Neural Networks - Part 2B 6 minutes
Regularization in Neural Networks - Part 3 18 minutes

2 readings Total 25 minutes

Module Overview: Regularization in Neural Networks 10 minutes
Lab Overview: Regularization in Neural Networks (Read First!) 15 minutes

7 assignments Total 80 minutes

Regularization in Neural Networks - Part 1A 10 minutes
Regularization in Neural Networks - Part 1B 10 minutes
Regularization in Neural Networks - Part 1C 10 minutes
Regularization in Neural Networks - Part 2A 10 minutes
Regularization in Neural Networks - Part 2B 10 minutes
Regularization in Neural Networks - Part 3 10 minutes
Regularization in Neural Networks Lab - Part 2 20 minutes

2 ungraded labs Total 320 minutes

Regularization in Neural Networks Lab - Part 1 300 minutes
Regularization in Neural Networks Lab - Solution 20 minutes

What's included

5 videos2 readings6 assignments2 ungraded labs

5 videos Total 35 minutes

Bayesian Neural Network for Regression - Part 1 10 minutes
Bayesian Neural Network for Regression - Part 2 9 minutes
Bayesian Neural Network for Regression - Part 3 6 minutes
Bayesian Neural Network for Regression - Part 4 4 minutes
Bayesian Neural Network for Regression - Part 5 6 minutes

2 readings Total 20 minutes

Module Overview: Bayesian Neural Networks for Regression 10 minutes
Lab Overview: Bayesian Neural Network for Regression (Read First!) 10 minutes

6 assignments Total 70 minutes

Bayesian Neural Network for Regression - Part 1 10 minutes
Bayesian Neural Network for Regression - Part 2 10 minutes
Bayesian Neural Network for Regression - Part 3 10 minutes
Bayesian Neural Network for Regression - Part 4 10 minutes
Bayesian Neural Network for Regression - Part 5 10 minutes
Bayesian Neural Network for Regression Lab - Part 2 20 minutes

2 ungraded labs Total 320 minutes

Bayesian Neural Network for Regression Lab - Part 1 300 minutes
Bayesian Neural Network for Regression Lab - Solution 20 minutes

What's included

1 reading1 assignment3 ungraded labs

1 reading Total 10 minutes

Module Overview: Implementing Neural Networks with TensorFlow 10 minutes

1 assignment Total 30 minutes

Implementing Neural Networks with TensorFlow - Part 3 30 minutes

3 ungraded labs Total 390 minutes

Implementing Neural Networks with TensorFlow - Part 1 60 minutes
Implementing Neural Networks with TensorFlow - Part 2 300 minutes
Implementing Neural Networks with TensorFlow - One Solution 30 minutes

What's included

1 reading1 assignment

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

This course is part of the following degree program(s) offered by Dartmouth College. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Peter Chin

Dartmouth College

4 Courses 1,324 learners

Offered by

Dartmouth College

Explore more from Algorithms

Johns Hopkins University
Mastering Neural Networks and Model Regularization
Course
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
Specialization
Packt
Foundations of Model Optimization and Deep Learning
Course
EDUCBA
Deep Learning: Build & Optimize Neural Networks
Course

Why people choose Coursera for their career

Felipe M.

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."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

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

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

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.

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.