One of the most common questions I get asked is “I want to learn machine learning, where do I start?”.

I’ve written some blog posts about this in the past, detailing how I got started and how I continue to learn.

And when people asked for resources, I’d usually send them a list of resources I’d collected over time.

But as good as the external resources I'd send to others are, I realized it can be hard to stitch together a collection of different sources on your own, especially when first getting started.

So I had a problem.

People were asking me, "how do I get started with machine learning?".

And my only answer was a table of resources all with varying levels of prerequisites.

To solve this problem, I created my own machine learning course. One which has no prerequisites except an openness to learn.

What you'll learn

the zero to mastery machine learning and data science course topics
The topics we cover in the Zero to Mastery Machine Learning Course on Udemy (note: this link is the best deal you can get as of June 2020, see below for more).

There are 2 paths:

  1. If you don’t know Python (a programming language for writing machine learning code).
  2. If you already know some Python (4-6 months experience).

Path 1 takes you from the ground up, teach you how to code Python from the ground up. It’s taught by my friend and number 1 rated Udemy instructor Andrei Neagoie. If you already know some Python, you can skip this part.

Path 2 continues on from Path 1 and gets you hands-on writing machine learning code with Python.

In Path 2, you’ll learn a framework for how to approach machine learning problems. Then you’ll learn the tools you can use for part of the framework, such as, Pandas for data analysis and manipulation, NumPy for numerical calculations (much of machine learning is finding patterns in numbers) and Matplotlib for making visualisations of your findings.

Once you’ve had some practice learning the tools of the trade. You’ll get hands-on with 3 end-to-end projects. In other words, you’ll combine the framework you’ve learned with the tools you’ve learned to see how an actual machine learning engineer might approach a problem.

scroll through of one of the end-to-end projects you'll create in the zero to mastery machine learning course
One of the end-to-end projects you'll build, Dog Vision or in other words, using computer vision to classify different dog breeds, including your own. You don't just get given the code either, we'll go step by step and code it together. But if you want to check out the code, it's available now on Google Colab. Take note of the workflow diagram up the top.

I designed and built the course with one question in mind.

If I was starting to learn machine learning again, what course would I have liked to have taken?

And this is it. The machine learning course I would’ve liked to have had when I started.

Testimonials

Below are some of the comments students have left in the Zero to Mastery Discord server (a space where you can chat with other students about the course and the field of machine learning in general), on my YouTube videos or through other means sharing their love for the course.

Finally, for an even more comprehensive breakdown of what you’ll be learning, check out the Complete Machine Learning and Data Science: Zero to Mastery course page on Udemy.

PS I've made sure the link above is the best deal you can get. And because of how Udemy works, if you purchase the course through that link, Andrei and I will get 98% of the funds you spend (2% goes to Udemy). Where as if you buy it directly from Udemy, Andrei and I split the funds you spend 50/50 (50% to Udemy, 50% to us).

The link is the most up date as of July 2020.

And if you'd like to know more, feel free to email me any questions you have.