Machine Learning: Build neural networks in 77 lines of code
4.5 (167 ratings)
582 students enrolled

# Machine Learning: Build neural networks in 77 lines of code

Machine Learning and Artificial Intelligence for beginners. How to build a neural network in 77 lines of Python code.
4.5 (167 ratings)
582 students enrolled
Last updated 1/2019
English
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Current price: \$16.99 Original price: \$24.99 Discount: 32% off
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This course includes
• 1 hour on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Neural Networks
• Machine Learning
• Artificial Intelligence
• Supervised Learning
Course content
Expand all 18 lectures 56:20
+ Theory
9 lectures 16:56

In this section, I briefly outline the structure of the course.

Preview 01:04

In this section, I'll explain how a biological neural network works, which in turn has inspired artificial neural networks which run on computers.

Preview 01:18

In this section, I will show you the problem our neural network is going to solve.

Preview 01:42

In this section, I will show you how to design the architecture of a neural network to fit the problem.

Preview 00:28

In this section, I will introduce you to the concepts of weights. Each input to a neuron has a weight, which indicates how much the signal should be amplified or reduced. The weights act as memory.

Preview 01:27

In this section, I will introduce you to the activation function. What is an activation function? An activation function is a mathematical formula which describes how the output of a neuron varies as its input changes. There are many different formulas which could be used. In this video, we will use the sigmoid formula. Watch the video to learn more.

Preview 03:46

In this section, I will show you how the neural network uses the training set to learn.

Training process
01:42

In this section, I will introduce you to the Error Cost Function. This describes the accuracy of the neural network. Although we don't use an Error Cost Function in our Python code directly, it gives you the foundational knowledge to understand the next section.

Error Cost Function
01:22

In this section, I explain gradient descent and we derive the the critical formula for adjusting the weights. This formula is the final piece in the puzzle, that will allow us to build our neural network.

04:07
+ Putting it into practise
9 lectures 39:24

In this section, I'll show you how to install Python and Sublime, so you have the software you need to start coding.

Development Environment
02:16

In this section we will start writing our first Python code.

Coding Part 1
08:34
Coding Part 2
04:27
Coding Part 3
08:53
Coding Part 4
05:22

In this section I will explain what the Terminal is and how to use it. If you already know how to use the Terminal, feel free to skip this section.

Using the Terminal
03:10

In this section, you will run the code you've written and see the neural network in action!

Preview 02:03

In this section, I will teach you the skills you need to decipher any Python error message and fix your code yourself.

Debugging
03:03

I'll summarise everything you learned in the course. And congratulate you for completing it! If you enjoyed it, please leave me a review.

Conclusion
01:36
Requirements
• Basic Python knowledge
Description

From Google Translate to Netflix recommendations, neural networks are increasingly being used in our everyday lives. One day neural networks may operate self driving cars or even reach the level of artificial consciousness. As the machine learning revolution grows, demand for machine learning engineers grows with it. Machine learning is a lucrative field to develop your career.

In this course, I will teach you how to build a neural network from scratch in 77 lines of Python code. Unlike other courses, we won't be using machine learning libraries, which means you will gain a unique level of insight into how neural networks actually work. This course is designed for beginners. I don't use complex mathematics and I explain the Python code line by line, so the concepts are explained clearly and simply.

This is the expanded and improved video version of my blog post "How to build a neural network in 9 lines of Python code" which has been read by over 500,0000 students.

Enroll today to start building your neural network.

Who this course is for:
• Anyone interested in machine learning