Free Courses That Are Actually Free: AI & ML Edition

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

1. Google Cybersecurity Certificate – Get on the fast track to a career in cybersecurity.

2. Natural Language Processing in TensorFlow – Build NLP systems

3. Python for Everybody – Develop programs to gather, clean, analyze, and visualize data

4. Google IT Support Professional Certificate

5. AWS Cloud Solutions Architect – Professional Certificate

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

 

AI for Everyone

 
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 use cases and applications of AI. You will also be exposed to concerns surrounding AI, including ethics, bias, jobs and the impacts on society.

You will take a glimpse of the future with AI, get advice for starting an AI-related career, and wrap up 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 at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing 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 topics in artificial intelligence as they incorporate them into their own Python programs.

By the end of the course, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

 

Google AI for Anyone

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

As its name suggests, this course is for anybody — you don’t need a computer science, mathematics or AI background to understand it. No programming skills or prior knowledge are needed.

It will take you through, from first principles what the fuss is all about, and you’ll get hands-on in 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 etc.) you will also begin to build an understanding of Neural networks and the types of machine learning including supervised, unsupervised, reinforcement etc. You will also see (and experience) what programming 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 basis for your learning and understanding of machine learning: decision trees. Developing your core skills in machine learning will create the foundation for expanding your knowledge into bagging and random forests, and from there into more complex algorithms like gradient boosting.

Using real-world cases and sample data sets, you will examine processes, chart your expectations, review the results, and measure the effectiveness of the machine’s techniques. Throughout the course, you will witness the evolution of the machine learning models, incorporating additional data and criteria – testing your predictions and analyzing the results along the way to avoid overtraining your data, mitigating overfitting and preventing 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 about the fundamentals and evolution of generative AI. You will explore the capabilities of generative AI in 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 an opportunity to explore the use cases of generative AI through IBM Generative AI Classroom and popular tools like ChatGPT. You will also hear from the 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, 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 set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid 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 for turning training data into effective automated predictions. You will learn about representation, over-fitting, regularization, generalization, and VC dimension. As well as clustering, classification, recommender problems, probabilistic modelling, and reinforcement learning. Last but not least, you will dive into on-line 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 find out about the types of problems that machine learning can help to solve, discuss how AI is changing the world, and think about 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 will start with some services where Amazon handles the training model and raw inference for you. It will 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 of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work 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 aims to educate, inspire, and enable you to rapidly create your next ML-powered idea in this rapidly emerging industry while providing you with a solid foundation to understand the field and the confidence to explore the industry further.

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

 

Wrapping up

 

The best thing you can do when you’re looking to enter a new career or upskill is soak up all the free knowledge available. In this blog, I have listed 10 different free courses that you can make use of and gain foundational knowledge and experience without having to spend a penny.

 
 

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