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One of the most annoying things that can happen is that you come across this course and it says it’s free, and as you sign up and go through the steps, you start to realize that only the first module or even the first lesson is free.

In this blog I will discuss a list of courses that are actually free, specifically for artificial intelligence and machine learning.

AI for everyone

Link: IBM: AI for Everyone: Learn the Basics
Duration: 4 weeks, 1-2 hours per week.

In this course, you will learn what AI is and understand its applications and use cases and how it is changing our lives. You will explore basic AI concepts including machine learning, deep learning, and neural networks, as well as AI use cases and applications. You will also be exposed to AI concerns including ethics, bias, jobs, and impact on society.

You will get a glimpse into the future of AI, receive advice on starting an AI-related career, and conclude the course by demonstrating AI in action with a mini project.

CS50’s Introduction to Artificial Intelligence with Python

Link: CS50’s Introduction to Artificial Intelligence with Python
Duration: 7 weeks, 10–30 hours per week

This course explores the concepts and algorithms underlying modern artificial intelligence, delving into the ideas that fuel technologies such as game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students will gain insight into the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence, while integrating them into their own Python programs.

By the end of the course, students will have experience with machine learning libraries and knowledge of the principles of artificial intelligence, enabling them to design their own intelligent systems.

Google AI for everyone

Link: Google AI for Everyone
Duration: 4 weeks, 2–3 hours per week

As the name suggests, this course is for everyone — you don’t need a computer science, math, or AI background to understand it. No programming skills or prior knowledge are required.

You will learn from the basics what it all involves, then get to work playing with data to teach a computer how to recognize images, sounds and more.

As you explore how AI is used in the real world (recommender systems, computer vision, self-driving cars, etc.), you will also begin to understand neural networks and the types of machine learning including supervised, unsupervised, reinforcement, etc. You will also see (and experience) what programming with AI looks like and how it is applied.

HarvardX: Machine Learning and AI with Python

Link: HarvardX: Machine Learning and AI with Python
Duration: 6 weeks, 4–5 hours per week

In Machine Learning and AI with Python, you will explore the most basic algorithm as a foundation for your knowledge and understanding of machine learning: decision trees. Developing your core machine learning skills will lay the foundation for expanding your knowledge to bagging and random forests, and from there to more complex algorithms such as gradient boosting.

Using real cases and sample datasets, you will explore processes, map your expectations, observe results, and measure the effectiveness of the machine’s techniques. Throughout the course, you will witness the evolution of machine learning models, incorporating additional data and criteria. You will test your predictions and analyze results along the way to avoid overtraining your data, limit overfitting, and prevent biased outcomes.

IBM: Introduction to Generative AI

Link: IBM: Introduction to Generative AI
Duration: 3 weeks, 1–3 hours per week

In this course, you will learn the fundamentals and evolution of generative AI. You will explore the possibilities of generative AI in various domains, including text, image, audio, video, virtual worlds, code, and data. You will understand the applications of generative AI in various sectors and industries. You will learn about the capabilities and characteristics of common generative AI models and tools, such as GPT, DALL-E, Stable Diffusion, and Synthesia.

Hands-on labs, included in the course, provide an opportunity to explore the use cases of Generative AI through IBM Generative AI Classroom and popular tools such as ChatGPT. You will also hear from practitioners about the capabilities, applications, and tools of Generative AI.

HarvardX: Data Science: Machine Learning

Link: HarvardX: Data Science: Machine Learning
Duration: 8 weeks, 2–4 hours per week

In this course, which is part of the Professional Certificate Program in Data Science, you will learn about popular machine learning algorithms, principal component analysis, and regularization by building a movie recommender system.

You will learn about training data and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms with training data so that you can predict the outcome for future datasets. You will also learn about overtraining and techniques to prevent it, such as cross-validation. All of these skills are fundamental to machine learning.

Machine Learning with Python: From Linear Models to Deep Learning

Link: MITx: Machine Learning with Python: From Linear Models to Deep Learning
Duration: 15 weeks, 10–14 hours per week

In this course, students will learn about principles and algorithms to transform training data into effective automated predictions. You will learn about representation, overfitting, regularization, generalization, and VC dimension. And about clustering, classification, recommendation problems, probabilistic modeling, and reinforcement learning. And last but not least, you will dive into online algorithms, support vector machines, and neural networks/deep learning.

Introduction to Machine Learning and AI

Link: RaspberryPiFoundation: Introduction to Machine Learning and AI
Duration: 4 weeks, 2–4 hours per week

In this four-week course from the Raspberry Pi Foundation, you’ll learn about different types of machine learning and use online tools to train your own AI models. You’ll learn about the types of problems machine learning can help solve, discuss how AI is changing the world, and consider the ethics of collecting data to train a machine learning model.

Introduction to Machine Learning on AWS

Link: AWS: Introduction to Machine Learning on AWS
Duration: 2 weeks, 2–4 hours per week

In this course, you’ll start with a set of services where Amazon handles the training model and rough inference for you. It covers services that do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training, and virtual agents. You’ll think about your current solutions and see where you can improve them using AI, ML, or Deep Learning. All of these solutions can work together with your current applications to improve your user experience or the business needs of your application.

AI for JavaScript Developers with TensorFlow.js

Link: Google AI for JavaScript Developers with TensorFlow.js
Duration: 7 weeks, 3–4 hours per week

This course is designed to educate, inspire and empower you to quickly create your next ML idea in this rapidly emerging sector, while giving you a solid foundation to understand the field and the confidence to explore the sector further.

No background in ML is required to take the course. A basic understanding of web technologies such as HTML, CSS, and JavaScript is highly recommended.

Complete

The best thing you can do if you want to start a new career or improve your skills is to get all the free knowledge that is available. In this blog I have listed 10 different free courses that you can use and that will give you fundamental knowledge and experience without having to spend a cent.

Nisha Arya is a data scientist, freelance technical writer and editor, and community manager for KDnuggets. She is particularly interested in providing career advice or tutorials on data science and theory-based knowledge about data science. Nisha covers a wide range of topics and wants to explore the different ways in which artificial intelligence can improve the longevity of human life. Nisha is an avid learner and wants to broaden her technical knowledge and writing skills while helping others.