How to Start with Machine Learning for Free
By: Roman Orac
Tags: Data Science, Artificial Intelligence, Learning, Machine Learning, Course

How to Start with Machine Learning for Free

The field offers competitive salaries, it’s challenging and fun. The best thing about it is that you can learn it for FREE. Roman Orac Mar 30·6 min read

Photo by Roméo A. on Unsplash

There is an increasing interest in the field of Machine Learning. This doesn’t surprise me with all the hype around Deep Learning and Neural Networks. Besides that, the field offers competitive salaries, it’s challenging and fun.

I’m assuming you’re new to the field and don’t know where to start.

The question I get asked the most often is: How to start with Machine Learning? My answer is always the same, hence I wrote this article to guide you in your journey.

My suggestion is: start with free content as there are many great resources available online. Even Ivy League Universities offer their courses online for free.

By studying these resources you’re going to learn if Machine Learning is right for you

Don’t start spending money on expensive courses right away. Think of paid courses as a specialization. Luckily, you can learn strong Machine Learning foundations for free.

By reading this article, you’ll learn, which are great eBooks and courses to learn:

Programming with Python

Data analysis with pandas

Start with Data Science and Machine Learning

I also share a few tips on how to efficiently learn Machine Learning.

Intro

Photo by Tim Mossholder on Unsplash

I would like to give you a few tips before we start.

Don’t go too deep into any book or course. When starting with a new field, it is important that you learn the breadth of the field.

Learn from multiple books and courses at the same time. This way you’ll hear explanations of complex concepts from different sides.

Practice is also important. Don’t go too deep into the theory without any practical experience. Titanic — Machine Learning from Disaster is a great place to start. Take a look at the code and discussion sections.

What’s the point of learning if you don’t memorize the concepts? One great way to memorize is to use Anki — an app that uses spaced repetition.

Programming eBooks

Python Notes for Professionals

Authors: People at Stack Overflow

Reading Python Notes for Professionals (image made by author)

This Python book is one of the most complete guides for Python I’ve ever seen. It is compiled from Stack Overflow Documentation and the content is written by people at Stack Overflow.

The book extensively covers every area of Python that you will (most probably) ever need. I suggest you don’t read it from start to finish, but use it as a go-to reference for a certain topic you would like to learn more about.

If you aren’t well versed in Python, I recommend diving deep into this book. With 816 pages this book offers more than enough information to make you proficient in Python.

Why do I recommend this book? Because you can always revisit it. It can serve as your reference on your journey.

Learn Pandas

Author: Hernan Rojas

Photo by Stan Y on Unsplash

Pandas became the defacto standard for Data Science in Python. Learn Pandas book is written in Jupyter Notebooks with accompanying text.

It starts with basic lessons on how to create a DataFrame and it ends with creating Excel reports. It is aimed at Junior Data Scientists that are still getting familiar with pandas.

To learn more advanced pandas tricks, I would recommend you to check my pandas series for Data Analysis:

Machine Learning eBooks

Python Data Science Handbook

Author: Jake VanderPlas

Photo by Viktor Forgacs on Unsplash

Python Data Science Handbook is aimed at junior Data Scientists. It shows how to use the most important tools, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and many others. This book is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models.

Interpretable Machine Learning

Subtitle: A Guide for Making Black Box Models Explainable

Author: Christoph Molnar

Photo by Lucas Benjamin on Unsplash

This book uses a “Pay what you want pricing strategy” so it is technically not free.

Interpretable Machine Learning focuses on ML models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks.

Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable. It details how to select and apply the best interpretation methods for a machine learning project.

Free Online Courses

Machine Learning Course

University: Columbia

Instructor: John W. Paisley, Ph.D.

Photo by h heyerlein on Unsplash

Machine Learning is the basis for the most exciting careers in Data Analysis today. You’ll learn the models and methods and apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams and plotting the path of movie zombies.

Major perspectives covered include:

probabilistic versus non-probabilistic modeling

supervised versus unsupervised learning

Topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.

Methods include linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

Artificial Intelligence Course

University: ColumbiaX

Instructor: Ansaf Salleb-Aouissi, Ph.D.

Photo by Alex Knight on Unsplash

What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?

They are all complex real-world problems being solved with applications of intelligence (AI).

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.

You will gain hands-on experience by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to Machine Learning includes work on linear regression.

Before you go

Some of the links above are affiliate links and if you go through them to make a purchase I’ll earn a commission. Keep in mind that I link courses because of their quality and not because of the commission I receive from your purchases.

Follow me on Twitter, where I regularly tweet about Data Science and Machine Learning.