Episode 5: Math for Machine Learning

Hello! And welcome to a new edition of the Data Science Now newsletter. In this session, I talked about the math you need to know to understand and do machine learning. You can hear the podcast version here:

And if you prefer you can watch the video recording here (sadly we had issues with the video, so it's just a Youtube video with the audio):

Remember that we will be live every Wednesday here at Linkedin, 8 PM CST :).

Here's a short recap of what I covered in the session:

In order to really feel the power that machine learning gives us, we need to know how the most important models work inside. It is not necessary a PhD in mathematics (although excellent and recommended if possible) to know these aspects.

I talked about the importance of mathematics in the world of data science, machine learning and friends.

The three pillars of mathematics that we should know: Algebra (mainly linear), Calculus (more than all the differential part and a bit the integral part) and statistics (all that is possible because it is fundamental). There are more things that can come with the study of all of that, but those are the most important things you have to know.

There are multiple ways of getting all this knowledge. You can do a career in science, engineering, something like that, or you can enroll in GOOD data science and machine learning courses. Apart from that, here are my recommendations to learn math:

General Books (All free):

• Elements of Statistical Learning
• Mathematics for Machine Learning
• Bayesian Reasoning and Machine Learning
• The Hundred-Page Machine Learning Book
• Mathematics for Machine Learning (Notes)
• Foundations of Machine Learning

Algebra (Free courses and books)

• Linear Algebra - Foundations to Frontiers (edX)
• Advanced Linear Algebra: Foundations to Frontiers
• Mathematics for Machine Learning: Linear Algebra (Coursera)
• Gilbert Strang lectures on Linear Algebra (MIT)
• Linear Algebra (Khan Academy)
• Linear Algebra Done Right (Book)
• Numerical Linear Algebra (Fast.ai)
• Coding the Matrix (Course)

My sister (Héizel Vázquez) and I reproduced the diagram (in a prettier format) for Algebra from the book Mathematics of Machine Learning in English and Spanish, so here they are:

Calculus (Free courses and books)

• Mathematics for Machine Learning: Multivariate Calculus (Coursera)
• Essence of calculus (3Blue1Brown)
• Introduction to Mathematical Thinking (Coursera)
• Calculus I (Professor Leonard)
• Calculus II (Professor Leonard)
• Calculus III (Professor Leonard)
• Single Variable Calculus (MIT)
• Calculus (Khan Academy)
• Introduction to Calculus I and II (Books)
• Advanced Calculus (Book)
• Calculus (Companion book to Gilbert Strang course)

Again my sister and I reproduced the diagram for Calculus from the book Mathematics of Machine Learning in English and Spanish, so here they are:

Statistics and Probability (Free courses and books)

• Probability and Statistics in Data Science using Python (edX)
• Business Statistics and Analysis Specialization (Coursera)
• Business Analytics: Decision Making using Data (Emeritus)
• Data Visualization: Tools and Techniques (Emeritus)
• Statistics and Probability (Khan Academy)
• All of Statistics: A Concise Course in Statistical Inference (Book)
• Intro to Statistics (Udacity)
• Statistics 110: Probability (Harvard)
• Crash Course Statistics (Youtube course)
• Statistics (Professor Leonard)
• Statistics for Data Science (Stanford)
• Probability and Statistics (Book)
• Think Stats (Book)

As you can imagine my sister and I reproduced the diagram for Statistics and Probability from the book Mathematics of Machine Learning in English and Spanish, so here they are:

Hopefully this helps you find a good path to study and learn what you need to know to rule the world of mathematics. Always Remember:

There's no easy path, you have to practice, study, and if you want to know where you're going, you need to understand where you come from.

Thanks for reading this, please subscribe share this with your network, it would help us a lot :)

With love by the Closter Team:

Gabriel Erives, Héizel Vázquez, Eilén Vázquez, Favio Vázquez.