“Dataquest actually makes you think and apply your skills. You don’t really need to spend $10,000 dollars on a bootcamp.”

Miguel Couto

Big Data Analyst @Zattoo

Course overview

In this course, you’ll learn the basics of neural networks, including graph representation, activation functions, multiple hidden layers, and image classification.

You’ll learn the different kinds of nonlinear activation functions, such as the ReLU function, hyperbolic tangent function, and others, to discover how they enable neural networks to capture nonlinearity. You’ll also learn how to add hidden layers — and how they can make neural networks more powerful.

At the end of the course, you’ll complete a project in which you will build a neural network to classify images of digits in the MNIST dataset — and tweak your neural networks for better handwriting recognition. This project is a chance for you to combine the skills you learned in this course and practice building neural networks using a typical deep learning workflow. This project can also serve as a portfolio project that you can show to future employers.

Key skills

  • Understanding the representation of neural networks
  • Demonstrating how adding hidden layers can improve model performance
  • Capturing nonlinearity in the data in neural networks

Course outline

Introduction to Deep Learning [4 lessons]

Representing Neural Networks 2h

Lesson Objectives
  • Visually represent neural networks
  • Implement linear and logistic regression as neural networks
  • Identify the differences between the nonlinear activation functions

Nonlinear Activation Functions 1h

Lesson Objectives
  • Define the different types of nonlinear activation functions
  • Improve neural network models with nonlinear activation functions

Hidden Layers 1h

Lesson Objectives
  • Add hidden layers to make neural networks more powerful
  • Train neural networks in scikit-learn

Guided Project: Building A Handwritten Digits Classifier 1h

Lesson Objectives
  • Define the concepts behind image classification
  • Compare different image classification models
  • Improve neural networks' handwriting recognition

Projects in this course

Guided Project: Building A Handwritten Digits Classifier

Learn the basics of image classification to build a handwriting classifier.

The Dataquest guarantee


Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.


We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

Master skills faster with Dataquest

Go from zero to job-ready

Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

Build your project portfolio

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.

Learning resources

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Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”


Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”


Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

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