How to Learn Machine Learning and Deep Learning for Free

Data science, machine learning and deep learning are becoming more popular; more and more people are learning these advanced skills, similar to the rush among programmers to learn about video games in the 1980s. Machine learning may be part of the next technical revolution.

What is machine learning?

Machine learning programs use algorithms to parse data, learn from that data, and make informed decisions based on what was learned.

You can take free machine learning and deep learning online courses if you want to learn more.

10 best free machine learning and deep learning online courses

1. What is Machine Learning?

The free Udemy course is an excellent online course to learn essential machine learning concepts, like supervised, unsupervised and reinforcement learning with Python.

You will learn about the process of building supervised predictive models and make several of them using Python, the most widely used programming language for machine learning.

As part of the course, you will receive the thoroughly annotated Jupyter Notebook. The best thing about this course is that concepts are presented with many examples and animations, making learning easy, particularly for beginners.

2. Machine Learning by Andrew Ng

The Coursera course offered by Stanford has taught more than 4 million people machine learning. It provides a broad introduction to machine learning, data mining and statistical pattern recognition.

Andrew Ng is one of the best teachers of machine learning and deep learning; he explains complex concepts in a way you can grasp. You will learn key machine learning concepts of supervised and unsupervised learning, including:

Parametric and non-parametric algorithms
Support vector machines
Kernels
Neural networks
Clustering
Dimensionality reduction
Recommender systems
Deep learning

The course also covers best practices in machine learning, such as bias and variance theory and artificial intelligence.

3. Practical Machine Learning with Scikit-Learn

Scikit is one of the most popular Python machine learning libraries. It was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Since then, it has become the de-facto machine learning library for many software engineers.

Scikit-Learn, also known as a skeleton, is particularly great for beginners. It offers a high-level interface for many tasks, allowing programmers to practice the entire machine learning workflow.

The course teaches machine learning basics, such as a target variable or feature, and how to create an end-to-end model using Scikit-Learn in Python. Some other crucial things in the course:

How to implement regression, classification and boosting algorithms
Which algorithms work best for a given dataset
Data pre-processing

You’ll also get hands-on experience with machine learning from importing, cleaning, training and testing data, plus pre-processing and feature engineering. In short, it’s a perfect course to kick-start your machine learning journey. Once you know Scikit, you can explore more powerful libraries, such as TensorFlow.

4. Introduction to Statistics by Guenther Walther

Stanford’s Introduction to Statistics course will give you a foundation for data and machine learning. In the Coursera online course, you’ll learn how to perform exploratory data analysis, understand critical principles of sampling, and select appropriate tests of significance for multiple contexts. You will also gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

Some additional topics covered by the course include:

Descriptive statistics
Sampling and randomized controlled experiments
Probability
Sampling distributions
Central Limit Theorem
Regression
Common tests of significance
Resampling
Multiple comparisons

5. Deep Learning Prerequisites: The Numpy Stack in Python v2

The free Udemy course about deep learning covers four major Python libraries, which are crucial to deep learning, machine learning and artificial intelligence: Numpy, Scipy, Pandas and Matplotlib. Here’s why they matter:

Numpy provides essential building blocks, like vectors, matrices and operations.
Scipy uses the general building blocks to do specific things.
Panda’s strength lies in loading data, particularly from the database.
Matplotlib helps you look at data using standard plots like the line chart, scatter plot and histogram.

In less than two hours, you’ll learn all these libraries and how to supervise machine learning (classification and regression) with real-world examples using Scikit-Learn. Here are the main concepts covered in this course:

Basic operations in Numpy, Scipy, Pandas and Matplotlib
Vector, Matrix, and Tensor manipulation
Visualizing data
Reading, writing and manipulating DataFrames

You will also learn to use Numpy, Scipy, Matplotlib and Pandas to implement numerical algorithms. Most importantly, you will learn the pros and cons of various machine learning models, including deep learning decisions trees, random forest, linear regression, boosting and more.

6. Basics of Deep Learning

The one-and-a-half-hour Udemy online course teaches the fundamentals of deep learning, including neural networks. The course is suitable for beginners who want to start from zero. Along the way, you will learn about the evolution of deep neural networks and their application in areas like image recognition and natural language processing.

7. FreeCodeCamp.org: Machine Learning Course for Beginners

FreeCodeCamp.org put a comprehensive nine-hour-long machine learning course on YouTube. This course will teach you the theory and practical application of machine learning concepts from scratch.

The course contents include:

Fundamentals of machine learning
Supervised learning
Unsupervised learning
Linear regression
Logistic regression
Regularization
Support vector machines
Principal component analysis
Learning theory
Decision trees
Ensemble learning
Boosting
Stacking ensemble learning
K-Means
Hierarchical clustering

8. DataCamp: Introduction to Data Visualization with Seaborn

Seaborn is a visualization library often used for statistical visualization and customization. Many data analysts use Seaborn.

The DataCamp online course teaches data skills, including machine learning. You’ll learn how to create and customize plots using Seaborn and how to visualize two quantitative and categorical variables.

9. Learn Keras: Build 4 Deep Learning Applications

The free Udemy online course teaches Keras. Keras is a powerful and easy-to-use Python library for developing and evaluating deep learning models. It wraps the efficient numerical computation libraries like Theano and TensorFlow. The library also allows you to define and train neural network models in a few short lines of code.

After the deep-learning course, you’ll know how to build an end-to-end Python machine-learning project using Keras and tune a deep learning model and neural network. The best part of the course: the instructor walks through every line of code, so you’ll be able to understand the model and the process.

10. FreeCodeCamp.org: Deep Learning Crash Course for Beginners

FreeCodeCamp.org teaches deep learning, a sub-branch of machine learning, in one-and-a-half hours on YouTube. The course is designed for absolute beginners with no experience in programming. You’ll learn the key ideas behind deep learning without any code.

The crash course covers:

Neural networks
Neural network architectures
Supervised learning
Unsupervised learning

The bottom line

Machine learning is a great career path if you’re interested in data, automation and algorithms. Your day will be filled with analyzing large amounts of data and implementing and automating data, and you can earn an excellent salary as well.

This article was written by Javin Paul for HackerNoon and was lightly edited and republished with permission.