5 Free Courses to Master Machine Learning - KDnuggets

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5 Free Courses to Master Machine Learning

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Machine learning is becoming increasingly popular in the data space. But there’s often a notion that to become a machine learning engineer you need to have an advanced degree. This, however, is not completely true. Because skills and experience trump degrees, always.

If you're reading this, you’re probably new to the data field and want to start out as a machine learning engineer. Perhaps, you already work in data as a data analyst or a BI analyst and would like to switch to a machine learning role.

Whatever your career goals are, we’ve curated a list of machine learning courses—that are completely free—to help you gain proficiency in machine learning. We’ve included courses that’ll help you understand both the theory and building machine learning models.

Let’s begin!

If you’re looking for a machine learning course that is accessible, Machine Learning for Everybody is for you.

Taught by Kylie Ying, this course takes a code first approach building simple and interesting machine learning models in Google Colab. Spinning up your own notebooks and building models while learning just enough theory is a great way to familiarize yourself with machine learning.

This course makes machine learning concepts accessible and covers the following topics:

Introduction to machine learning
K-Nearest Neighbors
Naive Bayes
Logistic regression
Linear regression
K-Means clustering
Principal Component Analysis (PCA)

Course link: Machine Learning for Everybody

Kaggle is a great platform to take part in real-world data challenges, build your data science portfolio, and hone your model building skills. In addition, Kaggle team also has a series of micro courses to get you up to speed on the fundamentals of machine learning.

You can check out the following (micro) courses. Each course will typically take a few hours to complete and work through the exercises:

Intro to Machine Learning
Intermediate Machine Learning
Feature engineering

The Intro to Machine Learning course covers the following topics:

How ML models work
Data exploration
Model validation
Underfitting and overfitting
Random forests

In the Intermediate Machine Learning course, you’ll learn:

Handling missing values
Working with categorical variables
ML pipelines
Cross-validation
XGBoost
Data leakage

The Feature Engineering course covers:

Mutual information
Creating features
K-Means clustering
Principal Component Analysis
Target encoding

It's recommended to take the courses in the above order so that you have the prerequisites covered when you move from one course to the next.

Courses link:

Machine Learning in Python with Scikit-Learn on the FUN MOOC platform is a free self-paced course created by the developers on the scikit-learn core team.

It covers a wide breadth of topics to help you learn building machine learning models with scikit-learn. Each module contains video tutorials and accompanying Jupyter notebooks. You need to have some familiarity with Python programming and Python data science libraries to make the most of the course.

The course contents include:

Predictive modeling pipeline
Evaluating model performance
Hyperparameter tuning
Selecting the best model
Linear models
Decision tree models
Ensemble of models

Course Link: Machine Learning in Python with Scikit-Learn

Machine Learning Crash Course from Google is another good resource to learn machine learning. From the basics of building a model to feature engineering and more, this course will teach you how to build machine learning models using the TensorFlow framework.

This course is split into three main sections, with a majority of the course’s contents in the ML concepts section:

ML Concepts
ML Engineering
ML Systems in the Real World

To take this course, you need to be familiar with high school math, Python programming, and the command line.

The ML concepts section includes the following:

ML foundations
Introduction to TensorFlow
Feature engineering
Logistic regression
Regularization
Neural networks

The ML Engineering section covers:

Static vs. dynamic training
Static vs. dynamic inference
Data dependencies
Fairness

And ML Systems in the Real World is a set of case studies to understand how machine learning is done in the real world.

Course link: Machine Learning Crash Course

So far, we’ve seen courses that give you a flavor of theoretical concepts while focusing on building models.

While this is a good start, you will have to understand the workings of machine learning algorithms in greater detail. This is important for cracking technical interviews, growing in your career, and getting into ML research.

CS229: Machine Learning at Stanford university is one of the most popular and highly recommended ML courses. This course will give you the same technical depth as a semester-long university course.

You can access the lectures and lecture notes online. This course covers the following broad topics:

Supervised learning
Unsupervised learning
Deep learning
Generalization and regularization
Reinforcement learning and control

Course Link: CS229: Machine Learning

I hope you found helpful resources to help you in your machine learning journey! These courses will help you get a good balance of theoretical concepts and practical model building.

If you’re already familiar with machine learning and are limited by time, I recommend checking out Machine Learning in Python with scikit-learn for a scikit-learn deep dive and CS229 for essential theoretical foundations. Happy learning!

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she's working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more.

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