Milo Spencer-Harper
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.
Instructor Details
Courses : 2
Specification: Machine Learning: Build neural networks in 77 lines of code
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23 reviews for Machine Learning: Build neural networks in 77 lines of code
Add a review
| Price | $14.99 |
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| Provider | |
| Duration | 1 hour |
| Year | 2019 |
| Level | Beginner |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
$14.99
Ray Zhu –
Very clear and well considered and prepared contents. A very good teacher. I should remember this name: Milo Spencer Harper. You should teach more and many people will benefit!
Patrick Hamilton –
Pretty much straight to the point and of thorough explanation of the basics of neural networks and of the lines of code. Definitely recommend this for those first starting into neural networks with Python. I had always thought there was more too it, that is, more complicity with neural networking and machine learning. But with this this fairly quick explanation and hands on coding, I now understand the basics and of how this can be applied to other projects.
Craig Endert –
Fantastic overview and mathematical proof of concept. Not too long or complex for a learner. Well done.
Dhruv Gupta –
Focussed.
Edgar Brazda –
Nowhere near detailed enough, and not much beyond the Medium post that probably brought you here. Not worth it.
Damodar Mahanta –
Very nicely explained through a short example about how neural network functions. I realy enjoyed this tutorial and got an idea about neural network.
Michel Landry –
A bit deep but begs for a re viewing of the material.
Orland Malphrus –
the great intro would love it if you did a course on the math behind it!
Aditya Iyengar –
Great course, decently well explained. I was able to understand and follow despite knowing extremely basic Python.
Ray Siltala –
very micro, I’m enjoying the to the point info.
Armandt van Zyl –
I already knew all this, but he really explained it well
Peter Gruessing –
It’s s great starter course but I think maybe go a bit further and add another section on how to move forward with it and/or tie it to some practical uses etc. Maybe a a section on how this compares to tensorflow and other products out there for AI.
Ian Woodbridge –
Very good course. I would have liked to program a more complex example.
Jeffrey Welsh –
This was great; Fast, to the point. More classes in AI by this author please!
Jean Oberson –
I recoded the entire example of the course in Procedural Python without using OOP. I got a shorter and more understandable code. For a beginner’s course, we should avoid OOP which complicates the understanding of the algorithm. I have a little doubt about the code: in the theoretical part, we talk about squaring the error and dividing by 2. We also talk about summing up the errors. In the code there is no squaring or summing of errors? Why these 2 contradictions?
Keerati Julsophon –
great course
Ken B –
Amazing! The best course I have taken on machine learning.
Ajay Radhakrishnan –
Great start and a great ML course. Although i disagree that this is a beginner level course, it does require understanding of coding, OOP etc.
Pratik Kumar Basu –
Clearly explained right from the basics!!! Loved it.
Brian Culver –
Very good explanation from beginning to end on Neural Network theory and its components (and formulas). Milo then walked us through coding the neural network while reintroducing each formula for each piece of code was great. It very much helped bridge how to go from theory to code. Very good course. Also, it works great on Mac and Windows. I used Spyder in Windows to build my code. Worked great.
Mark Tellez –
for 8 bucks it was alright
Luke Murray –
Great Introduction to Neural Networks!
Austin Holloway –
I took a computation intelligence class in college and we befeily discussed neural networks but I still confused. This is help connect the pieces where i was confused on