You don't need to go to a university to learn machine learning - you can do it from your living room, for completely free.

Here is an extensive list of curated free courses and tutorials, from beginner to advanced. ↓

(Trust me, you want to bookmark this tweet.)
This is how I'll group the courses.

Machine learning
├── Getting started
├── Computer vision
├── NLP
├── Reinforcement learning
└── Applications

Coding
├── Python
├── R
├── Javascript
└── Machine learning frameworks

Let's start!
Machine learning
└── Getting started

1. Neural networks (by @3blue1brown)

youtube.com/playlist?list=…
2. Deep Learning from the Foundations (by @fastdotai)

course19.fast.ai/part2
3. NYU Deep Learning Spring 2021 (by @alfcnz and @ylecun)

youtube.com/playlist?list=…
4. MIT RES.LL-005 Mathematics of Big Data and Machine Learning (by Jeremy Kepner and Vijay Gadepally)

youtube.com/playlist?list=…
5. Stanford CS229: Machine Learning (by @AndrewYNg)

youtube.com/playlist?list=…
6. Stanford CS230: Deep Learning (by Andrew Ng)

youtube.com/playlist?list=…
7. Machine Learning Course for Beginners (by Ayush Singh and @freecodecamp)

Machine learning
└── Computer vision

1. Stanford cs231n Convolutional Neural Networks for Visual Recognition (by @karpathy)

(This is my all-time favorite machine learning course.)

2. Convolutional Neural Networks (by Andrew Ng)

youtube.com/playlist?list=…
3. Advanced Computer Vision with Python (by Murtaza Hassan and freecodecamp)

4. OpenCV Python Course - Learn Computer Vision and AI (by @LearnOpenCV and freecodecamp)

5. Tensorflow Object Detection in 5 Hours with Python (by @nicholasrenotte)

Machine learning
└── Natural language processing

1. Stanford CS224N: NLP with Deep Learning (by @chrmanning)

youtube.com/playlist?list=…
2. Natural Language Processing with TensorFlow 2 - Beginner's Course (by Phil Tabor and freecodecamp)

3. Intro to NLP with spaCy (by @explosion_ai)

youtube.com/playlist?list=…
Machine learning
└── Reinforcement learning

1. Stanford CS234: Reinforcement Learning (by @EmmaBrunskill)

youtube.com/playlist?list=…
2. Reinforcement Learning Course - Full Machine Learning Tutorial (by Phil Tabor and freecodecamp)

3. Deep Reinforcement Learning in Python Tutorial - A Course on How to Implement Deep Learning Papers (by Phil Tabor and freecodecamp)

4. Reinforcement Learning in 3 Hours (by @nicholasrenotte)

Machine learning
└── Applications

1. MIT 6.S897 Machine Learning for Healthcare (by @david_sontag and Peter Szolovits)

youtube.com/playlist?list=…
2. Applied Deep Learning with PyTorch (by Fawaz Sammani and freecodecamp)

3. Algorithmic Trading Using Python - Full Course (by @NickJMcCullum and freecodecamp)

4. Machine Learning for Trading at Georgia Tech (by @tuckerbalch)

youtube.com/playlist?list=…
5. MIT Machine Learning in Genomics (by @manoliskellis)

youtube.com/playlist?list=…
6. Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis (by @thedataprof and freecodecamp)

Coding
└── Python

1. MIT 6.0001 Introduction to Computer Science and Programming in Python (by @anabellphd)

youtube.com/playlist?list=…
2. Learn Python - Full Course for Beginners (by @mike_dane and freecodecamp)

3. Python for Data Science - Course for Beginners (by freecodecamp and Maxwell Armi)

Coding
└── R

1. R Programming Tutorial - Learn the Basics of Statistical Computing (by @datalabcc and freecodecamp)

2. Learn R Programming with Johns Hopkins University (by @rdpeng)

youtube.com/playlist?list=…
Coding
└── JavaScript

(Yes, you can do machine learning in JavaScript.)

1. Learn TensorFlow.js - Deep Learning and Neural Networks with JavaScript (by @deeplizard and freecodecamp)

2. Neural Networks with JavaScript - Full Course using Brain.js (by @robertlplummer and freecodecamp)

Coding
└── Machine learning frameworks

1. TensorFlow 2.0 Complete Course (by @TechWithTimm and freecodecamp)

2. Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners (by @deeplizard and freecodecamp)

3. PyTorch for Deep Learning (by @jovianhq and freecodecamp)

4. Scikit-learn Crash Course - Machine Learning Library for Python (by @fishnets88 and freecodecamp)

If you are still here, and perhaps finished some courses after coming back to this list, congratulations! You are off to a great start in machine learning.

Now go, and build something awesome!
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More from @TivadarDanka

20 Sep
What do you get when you let a monkey randomly smash the buttons on a typewriter?

Hamlet from Shakespeare, of course. And Romeo and Juliet. And every other finite string that is possible.

Don't believe me? Keep reading. ↓
Let's start at the very beginning!

Suppose that I have a coin that, when tossed, has a 1/2 probability of coming up heads and a 1/2 probability of coming up tails.

If I start tossing the coin and tracking the result, what is the probability of 𝑛𝑒𝑣𝑒𝑟 having heads?
To answer this, first, we calculate the probability of no heads in 𝑛 tosses. (That is, the probability of 𝑛 tails.)

Since tosses are independent of each other, we can just multiply the probabilities for each toss together.
Read 14 tweets
16 Sep
Data similarity has such a simple visual interpretation that it will light all the bulbs in your head.

The mathematical magic tells you that similarity is given by the inner product. Have you thought about why?

This is how elementary geometry explains it all.

↓ A thread. ↓
Let's start in the beginning!

In machine learning, data is represented by vectors. So, instead of observations and features, we talk about tuples of (real) numbers.
Vectors have two special functions defined on them: their norms and inner products. Norms simply describe their magnitude, while inner products describe
.
.
.
well, a 𝐥𝐨𝐭 of things.

Let's start with the fundamentals!
Read 11 tweets
14 Sep
You are (probably) wrong about probability.

If I toss a fair coin ten times and it all comes up heads, what is the chance that the 11th toss will also be heads? Many think that it'll be highly unlikely. However, this is incorrect.

Here is why!

↓ A thread. ↓
In probability theory and statistics, we often study events in the context of other events.

This is captured by conditional probabilities, answering a simple question: "what is the probability of A if we know that B has occurred?".
Without any additional information, the probability that eleven coin tosses result in eleven heads in a row is extremely small.

However, notice that it was not our case. The original question was to find the probability of the 11th toss, given the result of the previous ten.
Read 10 tweets
1 Sep
The early access of my Mathematics of Machine Learning book is launching today!

One chapter per week, we go from basics to the internals of neural networks. We are starting with vector spaces, the scene where machine learning happens.

Here is why they are so important!

🧵 👇🏽
As you probably know, data is represented by vectors.

Data points are just tuples of measurements. In their raw form, they are hardly useful for us. They are just blips in space.
Without operations and transformations, it is difficult to predict class labels or do anything else.

Vector spaces provide a mathematical structure where operations naturally arise.

Instead of a blip, just imagine an arrow pointing to the data point from a fixed origin.
Read 11 tweets
27 Aug
The Mathematics of Machine Learning book early release is launching in September 1st! Exciting times are ahead :)

If you are interested in understanding the mathematics of machine learning, this is the book for you.

tivadar.gumroad.com/l/mathematics-…
In the early access program, I'll release the sections of this book as I write them.

During our time together, my goal is to guide you through the inner workings of machine learning, from high school mathematics to backpropagation.
This is the release calendar for 2021.

Part 1: Linear algebra

1. Vector spaces (September 1st)
2. Normed spaces (September 8th)
3. Inner product spaces (September 15th)
4. Linear transformations (September 22th)
Read 7 tweets
23 Aug
Machine learning is more than function fitting.

Even though most of us are introduced to the subject through this example, fitting functions to a training dataset seemingly doesn't give us any deep insight about the data.

This is what's working behind the scenes!

🧵 👇🏽
Consider a simple example: predicting the value 𝑦 from the observation 𝑥; for instance 𝑦-s are real estate prices based on the square footage 𝑥.

If you are a visual person, this is how you can imagine such dataset.
The first thing one would do is to fit a linear function 𝑓(𝑥) = 𝑎𝑥 + 𝑏 on the data.

By looking at the result, we can see that something is not right. Sure, it might capture the mean value for a given observation, but the variance and the noise in the data is not explained.
Read 9 tweets

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