Author's image | Canva
One of the most annoying things that can happen is that you come across a course that says it is free. 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 review a list of courses that are actually free, specifically on artificial intelligence and machine learning.
ai for all
Link: IBM: ai for Everyone: Master 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 transforming 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 concerns around ai, such as ethics, biases, jobs, and impacts on society.
You'll get a glimpse into the future with ai, receive tips for starting an ai-related career, and finish the course by demonstrating ai in action with a mini-project.
Introduction to artificial intelligence with Python in CS50
Link: Introduction to artificial intelligence with Python in CS50
Duration: 7 weeks, 10–30 hours per week
This course explores the concepts and algorithms that underlie modern artificial intelligence, delving into the ideas that give rise to technologies such as game engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other ai topics as they incorporate them into their own Python programs.
Upon completion of the course, students will gain experience with machine learning libraries as well as knowledge of ai principles that will enable them to design their own intelligent systems.
Google artificial intelligence for everyone
Link: Google artificial intelligence for everyone
Duration: 4 weeks, 2-3 hours per week
As the name suggests, this course is for everyone: you don't need to have any knowledge of computer science, mathematics or artificial intelligence to understand it. No programming skills or prior knowledge are required.
It will walk you through the basic principles and where all the fuss comes from, and let you play around with data to teach a computer to recognize images, sounds, and more.
As you explore how ai is used in the real world (recommendation systems, computer vision, autonomous driving, etc.), you'll also begin to understand neural networks and types of machine learning, including supervised, unsupervised, reinforcement, etc. You'll also see (and experience) what ai programming looks like and how it's applied.
HarvardX: Machine Learning and artificial intelligence with Python
Link: HarvardX: Machine Learning and artificial intelligence 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 learning and understanding of machine learning: decision trees. Developing your basic skills in machine learning will create the foundation for expanding your knowledge into bagging and random forests, and from there to more complex algorithms like gradient boosting.
Using real-world cases and sample data sets, you will examine processes, graph your expectations, review results, and measure the effectiveness of machine learning techniques. Throughout the course, you will witness the evolution of machine learning models, incorporating additional data and criteria, testing their predictions, and analyzing results along the way to avoid overtraining your data, mitigate overfitting, and prevent biased results.
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 about the fundamentals and evolution of generative ai. You will explore the capabilities of generative ai across different domains including text, image, audio, video, virtual worlds, code, and data. You will understand the applications of generative ai across different sectors and industries. You will learn about the capabilities and features of common generative ai models and tools such as GPT, DALL-E, Stable Diffusion, and Synthesia.
Hands-on labs, included in the course, provide the opportunity to explore generative ai use cases through the IBM Generative ai Classroom and popular tools like ChatGPT. You’ll also hear from practitioners discussing generative ai capabilities, applications, and tools.
HarvardX: Data Science: Machine Learning
Link: HarvardX: Data Science: Machine Learning
Duration: 8 weeks, 2–4 hours per week
In this course, part of the Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
You will learn about training data and how to use a dataset to discover potentially predictive relationships. As you develop the movie recommendation system, you will learn how to train algorithms using training data so that you can predict the outcome of 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 the principles and algorithms for turning training data into effective automated predictions. You will learn about representation, overfitting, regularization, generalization, and VC dimension. Also, about clustering, classification, recommendation problems, probabilistic modeling, and reinforcement learning. Last but not least, you will dive into online algorithms, support vector machines, and neural networks/deep learning.
Introduction to Machine Learning and artificial intelligence
Link: RaspberryPiFoundation: Introduction to Machine Learning and artificial intelligence
Duration: 4 weeks, 2–4 hours per week
In this four-week course from the Raspberry Pi Foundation, you'll learn about the different types of machine learning and use online tools to train your own ai models. You'll discover the types of problems that machine learning can help solve, discuss how ai is changing the world, and reflect on 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 few services where amazon takes care of the training model and raw inference for you. You'll cover 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 with your current applications to improve the user experience or business needs of your application.
artificial intelligence 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 aims to educate, inspire and enable you to quickly create your next ML-powered idea in this rapidly emerging industry, whilst giving you a solid foundation to understand the field and the confidence to explore the industry further.
No prior knowledge of ML is required to take the course. A basic, working knowledge of web technologies such as HTML, CSS, and JavaScript is highly recommended.
Ending up
The best thing you can do when you are looking to start a new career or improve your skills is to take advantage of all the free knowledge available. In this blog, I have listed 10 different free courses that you can use to gain basic knowledge and experience without having to spend a dime.
Nisha Arya Nisha is a data scientist, freelance technical writer, and KDnuggets community editor and manager. She is especially interested in providing career advice on data science or tutorials and theoretical knowledge on data science. Nisha covers a wide range of topics and wishes to explore the different ways in which artificial intelligence can benefit the longevity of human life. Nisha is an enthusiastic learner and is looking to expand her technological knowledge and writing skills while helping to mentor others.
