10 Free AI and Machine Learning Courses by Google in 2026

Artificial intelligence (AI) and machine learning (ML) are revolutionizing industries and transforming the employment landscape. As more companies adopt AI technologies, the demand for skilled AI practitioners is skyrocketing. In fact, the World Economic Forum predicts that 97 million new jobs may emerge that are more adapted to the new division of labor between humans, machines and algorithms by 2025.[^1] [^1]: The Future of Jobs Report 2020, World Economic Forum

Now is the ideal time to start learning AI and position yourself for the jobs of the future. However, you don‘t need to spend a fortune on education or have a PhD in computer science to break into AI. Thanks to Google, a pioneer in AI innovation, anyone can now learn AI and ML skills for free online.

Google offers an impressive catalog of free AI courses designed for various skill levels and backgrounds. Whether you‘re a complete beginner looking to understand key AI concepts or an experienced developer seeking to master the latest ML techniques, Google has you covered with high-quality, hands-on courses.

In this article, we‘ve curated the top 10 free AI and machine learning courses from Google that you should consider in 2024. We‘ll dive into the details of each course, including the topics covered, expected time commitment, and learning outcomes. By the end, you‘ll see how these free courses provide an incredible opportunity to gain in-demand AI skills from one of the world‘s top technology companies.

1. Machine Learning Crash Course

Google‘s Machine Learning Crash Course is the perfect launchpad for your AI learning journey. Designed for beginners, this 15-hour course teaches the fundamentals of machine learning using TensorFlow, Google‘s open-source ML library.

Through a series of accessible lessons and interactive exercises, you‘ll learn key ML concepts and techniques such as:

• Linear regression
• Classification
• Clustering
• Hidden Markov models
• Reinforcement learning
• Neural networks

You‘ll also gain hands-on experience with practical ML topics like data preprocessing, feature engineering, model evaluation, and hyperparameter tuning. The course includes a variety of real-world case studies and coding challenges in TensorFlow to solidify your understanding.

According to Cassie Kozyrkov, Head of Decision Intelligence at Google, the goal of the Machine Learning Crash Course is to "help everyone understand how machine learning works, guide them through building real ML systems hands-on, and empower them to apply what they‘ve learned to their own projects or their job."[^2] [^2]: About the Machine Learning Crash Course, Google Developers

By the end of the course, you‘ll have a strong grasp of core ML concepts and be able to build your own basic ML models using TensorFlow. You‘ll also receive a certificate of completion to showcase your new skills. No prior ML experience is required, but comfort with programming and basic algebra is recommended.

2. Intro to TensorFlow for Deep Learning

Deep learning, a subfield of machine learning that uses neural networks with many layers, powers some of the most impressive AI breakthroughs like natural language processing and computer vision. If you‘re eager to harness deep learning, check out Google‘s Intro to TensorFlow for Deep Learning course on Udacity.

In this free course, you‘ll learn how to build and train neural networks in TensorFlow to tackle real-world challenges such as:

• Image classification
• Language translation
• Text generation
• Sentiment analysis

Engaging video lessons break down deep learning theory, while interactive notebooks allow you to experiment with code and visualize results. You‘ll build your skills through hands-on projects like training an image classifier and generating TV scripts with recurrent neural networks.

The course spans about 2 months, with an expected commitment of 5-10 hours per week. While some ML familiarity is helpful, it‘s not required. If you have basic Python skills, you can dive right in and learn TensorFlow syntax along the way.

Deep learning skills are in high demand as industries apply the technology to automate complex tasks and uncover new insights. The average annual salary for a deep learning engineer in the U.S. is $117,510.[^3] This course will give you a solid foundation to pursue further study and projects in this exciting field.

[^3]: Deep Learning Engineer Salary in United States, Glassdoor

3. Google Cloud AI Fundamentals

As companies shift their data and applications to the cloud, the ability to apply AI and ML in cloud environments is becoming increasingly valuable. Google Cloud AI Fundamentals, a free 4-week course, teaches you how to utilize Google Cloud‘s powerful suite of AI tools and services.

Each week focuses on a different fundamental aspect of AI on Google Cloud:

1. Overview: Types of problems AI can solve and key Google Cloud AI products
2. ML APIs: Using pre-trained models for vision, language, speech, and structured data
3. Custom Models: Building and training your own models with AutoML and BigQuery ML
4. ML in Production: Deploying models to production and monitoring them on Vertex AI

Hands-on labs based on real business scenarios give you practical experience building end-to-end ML workflows on Google Cloud. For example, you might use AutoML Vision to train a model to detect defective products on an assembly line, or use the Natural Language API to analyze customer sentiment in support tickets.

By the end of the course, you‘ll understand the landscape of Google Cloud AI offerings and be able to apply them to your own projects. You‘ll also be prepared for the Google Cloud Professional Machine Learning Engineer certification.

4. AI for Social Good

AI has immense potential to help solve pressing global issues and improve people‘s lives. AI for Social Good, a joint program from Google and Udacity, explores how AI can be used to support crisis response, disease prevention, conservation, accessibility, and more.

This free 4-week program consists of video lessons, case studies, and a hands-on project. Each week covers a different theme:

1. Crisis Response: Using AI to predict natural disasters, coordinate aid, and locate missing persons
2. Disease Prevention: Applying ML to detect disease outbreaks, develop vaccines, and improve diagnosis
3. Environmental Conservation: Leveraging AI to combat climate change, protect habitats, and reduce pollution
4. Accessibility: Building AI tools to assist people with disabilities and promote inclusion

You‘ll learn from experts at Google, Waymo, World Bank, and various nonprofits who have applied AI for positive impact. Compelling examples include fighting illegal fishing with ML-equipped cameras and using AI to provide mental health support for youth.

For the course project, you‘ll propose your own AI solution to a social issue you care about. Top proposals can be submitted to Google‘s AI for Social Good program for a chance to receive mentoring and support from Google.

No prior AI experience is required for this inspirational yet practical course. By the end, you‘ll understand AI‘s vast potential for social impact and have a road map to develop your world-changing idea.

5. Exploring Fairness in Machine Learning

As AI systems are deployed in high-stakes domains like hiring, lending, and criminal justice, ensuring they are fair and unbiased is critical. Google‘s Exploring Fairness in Machine Learning course equips you with tools and techniques to detect and mitigate unfair bias in ML models.

In this 15-hour intermediate-level course, you‘ll grapple with key questions such as:

• What does it mean for an ML model to be "fair"?
• How can we mathematically define and measure fairness in ML?
• What are common sources of unfair bias in ML and how can we mitigate them?
• How do we balance competing notions of fairness and performance?

Through a mix of lectures, readings, and coding exercises in TensorFlow, you‘ll learn to assess ML models for statistical fairness criteria and visualize how models impact different subgroups. You‘ll get hands-on experience using Google‘s What-If Tool to probe your models for concerning disparities and experiment with remediation strategies.

The course concludes with a capstone project where you‘ll conduct a fairness audit of a real-world ML system and present your findings. This project is excellent preparation for the kind of work ML fairness teams do at major tech companies to help uphold AI ethics principles.

While the course does not require ML expertise, familiarity with training models in TensorFlow is strongly recommended to complete the exercises. By the end, you‘ll be equipped with a framework to identify and address fairness issues as you develop ML systems—an increasingly essential skill as AI is applied in ever more consequential domains.

Why Learn AI with Google?

Google‘s free AI courses provide a unique opportunity to learn from a world leader in AI innovation. Google‘s courses stand out for several reasons:

1. Expertise: The courses are developed by Google‘s own AI experts who are pushing the boundaries of what‘s possible with ML. You‘ll learn best practices directly from the pros.

2. Practical focus: Google‘s courses emphasize hands-on TensorFlow exercises to build your practical ML skills. You‘ll gain experience with the same tools and techniques used by professional ML engineers.

3. Recognition: A certificate of completion from Google carries weight in the industry. Listing your Google AI coursework on your resume and LinkedIn can make you stand out to employers.

4. Flexibility: The self-paced, modular format of Google‘s courses allows you to learn on your own schedule from anywhere. You can focus on the topics most relevant to your goals.

5. Supportive community: Connect with thousands of peers around the world in the course forums to get help, share ideas, and showcase your projects. Learning with others keeps you motivated.

Google‘s investment in making AI education accessible to all is part of the company‘s larger mission to use AI to benefit society. As Jeff Dean, Google Senior Fellow and SVP of Google Research and Health, explains:

We believe that the potential of AI and machine learning is so vast that every technical person should learn ML fundamentals. We want to help everyone understand how this technology works, learn how to build AI systems themselves, and think critically about how AI can be applied to solve big problems in the world.[^4]

[^4]: Introduction to Machine Learning Crash Course, TensorFlow YouTube Channel

Start Your Free Google AI Education Today

In a world where AI is becoming ubiquitous, there has never been a better time to build your AI skills. Google‘s free courses make it easy for anyone to get started with practical, job-relevant AI education from a trusted source.

We‘ve highlighted just a few of the many free AI and ML courses Google offers. Additional self-study resources include:

• Intro to ML on Google Cloud: Learn how to train, evaluate, and deploy ML models using Google Cloud tools

• Machine Learning Guides: In-depth tutorials on ML topics like classification, recommendation, sequence models, and more

• Machine Learning Practica: Monthly challenges to grow your applied ML skills in areas like computer vision, reinforcement learning, ML fairness, etc.

Whether you‘re a student exploring career paths, a software engineer looking to expand your skill set, or a domain expert eager to apply AI to your field, Google‘s free courses can put you on the path to achieving your learning goals. The interactive, hands-on format makes it engaging to pick up practical AI skills on your own schedule.

Are you ready to future-proof your skill set and open up exciting new career possibilities in the age of AI? Dive into Google‘s Machine Learning Crash Course and start your AI learning journey today. With Google as your guide, you‘ll be on your way to building the breakthrough intelligent applications of tomorrow.