Machine Learning: Build neural networks in 77 lines of code (Udemy.com)

This is the bite-sized lecture I like. This course doesn't teach the history and the future of neural network. The code doesn't solve any real world problem. The explanation of how it works is just a minimum. However we can memorize ALL of the contents in this lecture for the rest of our carriers. I strongly recommend learners to have some basic knowledge of neural network before taking this course. Very basic knowledge of Python coding is a must. There're hundreds of such lengthy courses explaining how it kinda works. Then you'll have a click. Lecturer will just start coding along side with learners. I love the feeling of having a program when every single line of code is under my command.

This course does a great job explaining the neural network from a math standpoint and starting it from scratch. However, the example used was fairly simple and it would be interesting to see how it could be used in other scenarios, more complex scenarios, or to solve issues.

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.

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?

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.

I like how straight forward and simple this course is. Tells you exactly the components of a Neural Network without over explaining it. Perfect starter course. I've personally only used libraries so once I had a technical interview and they asked how to build a NN from scratch and I was unprepared. This course is helping fill in that void! I ask the creator of the course to create more courses as I very much his style of teaching.

Great review of basics of Artificial Intelligence. Should have spent more time on each individual subject or recommended extended reading for them. Overall a great choice if you want a quick way to understand how a basic AI works.

The course isn't about Neural Networks but about the description of a single neuron. The announced subject isn't fully covered yet. The author has announced that an additionnal part will be avalaible soon. To wait you can have a look on (teaser): https://github.com/miloharper/multi-layer-neural-network

At first I thought that this course might not have enough depth, and it is true, that it is very basic. However, if you are new to neural networks like me, and you have read some books but not seen code in action, this course is perfect. A very easy viewing experience, short and to the point. At the conclusion, you may, like me, say, "Oh, now I get it. I feel much more comfortable continuing my education on neural networks."

It's the first course I've ever taken on neural networks and Milo's teaching makes it really accessible, and easy to grasp the core concepts. I'm not particularly mathsy, and yet I still felt like I was able to get to grips with the basics. Milo is really a fantastic teacher. He explains the concepts incredibly clearly. He applies the theory you learn in the first half of the course in the second half of the course, taking you step by step through what each line of code is doing and how that relates to the theory. It's exciting when you finally get to run your code and when you realise that you've created something that is 'thinking' on its own. Thanks so much Milo!

I'm an app developer who's been thinking of learning more about AI and machine learning for a while now. I couldn't resist trying to "build a neural network in 77 lines of codde", and I'm glad to have taken the course. It's a great introduction for anybody new to the subject like me, and Milo is a fantastic teacher. 5 stars!

Excellent course. Recommended.