top of page

Free AI Courses by Topic JUN 2025

  • Writer: Venkata  Pagadala
    Venkata Pagadala
  • Jun 17
  • 6 min read

Free AI Courses by Topic

A comprehensive collection of free AI courses organized by topics and subtopics.


About This Collection:


Venkata Pagadala has curated this comprehensive collection of free AI courses to help you navigate the rapidly evolving field of artificial intelligence. All links have been verified as active and free to access as of June 2025.


The collection is organized hierarchically from broader concepts to specific subtopics, covering both foundational AI knowledge and the hottest trending topics in 2025. Each course entry includes a description, topics covered, key features, and verification of link status.


Whether you're a beginner looking to start your AI journey or an experienced practitioner wanting to stay updated with the latest trends, this collection provides valuable resources to enhance your skills and knowledge. Last updated: June 2025




Overview of Topics 

  1. Machine Learning: Technology that enables computers to learn patterns from data and make predictions without being explicitly programmed.

  2. Deep Learning: Advanced machine learning using neural networks with multiple layers to process complex patterns in data.

  3. Natural Language Processing (NLP): An AI field focused on enabling computers to understand, interpret, and generate human language.

  4. Computer Vision: AI technology that allows computers to gain a high-level understanding from digital images and videos.

  5. Reinforcement Learning: A Learning approach where AI agents take actions to maximize rewards in a specific environment.

  6. AI Fundamentals: Core concepts and principles that form the foundation of artificial intelligence systems.

  7. Generative AI: AI systems that create new content like images, text, audio, or video based on training data.

  8. AI Agents: Autonomous software entities that perceive their environment and take actions to achieve goals.

  9. Multimodal AI: Systems that process and understand multiple types of data (text, images, audio) simultaneously.

  10. AI-Powered Social Media Tools: Applications using AI to enhance content creation, scheduling, and analytics for social platforms.

  11. Generative AI: AI systems that create new content like images, text, audio, and video 

  12. Augmented Reality + AI: The combination of AR technology with AI to create intelligent, context-aware experiences that understand and respond to the physical world. This enables more natural interactions between digital content and real environments.

  13. Quantum AI: The intersection of quantum computing and artificial intelligence, using quantum algorithms to potentially solve complex problems beyond classical computing capabilities. This emerging field could revolutionize machine learning with exponential speedups.

  14. AI Ethics and Responsible AI: The practice of designing, developing, and deploying AI systems with consideration for fairness, transparency, privacy, and societal impact. This ensures AI benefits humanity while minimizing potential harms.

  15. AI for Sustainability: Applications of AI to address environmental challenges like climate change, resource conservation, and biodiversity loss. These systems optimize energy use, predict environmental changes, and support sustainable decision-making.

  16. Edge AI and Decentralized Intelligence: AI processing that happens directly on local devices rather than in the cloud, enabling faster responses, enhanced privacy, and operation without constant internet connectivity. This approach distributes intelligence across networks of devices.


FOUNDATIONAL AI TOPICS

Machine Learning

Google - Machine Learning Crash Course

  • URL:https://developers.google.com/machine-learning/crash-course 

  • Description: A self-study guide for aspiring machine learning practitioners with video lectures, real-world case studies, and hands-on practice exercises.

  • Topics Covered: ML concepts, TensorFlow, feature engineering, classification, regression, neural networks

  • Features: Interactive visualizations, coding exercises, real-world examples


Harvard - Data Science: Machine Learning

  • URL:https://pll.harvard.edu/course/data-science-machine-learning 

  • Description: Learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

  • Topics Covered: K-nearest neighbors, linear regression, logistic regression, random forests, regularization

  • Features: Free audit option, Harvard faculty instruction, real-world projects



MIT - Introduction to Machine Learning


Deep Learning

Fast.ai - Practical Deep Learning for Coders

  • URL:https://course.fast.ai/ 

  • Description: A course designed for people with coding experience who want to learn how to apply deep learning and machine learning to practical problems.

  • Topics Covered: CNNs, RNNs, computer vision, NLP, tabular data, collaborative filtering

  • Features: Top-down teaching approach, practical coding exercises, state-of-the-art techniques



DeepLearning.AI - Deep Learning Specialization

  • URL:https://www.deeplearning.ai/courses/ 

  • Description: A foundational program that helps you understand deep learning, apply it effectively, and build a career in AI.

  • Topics Covered: Neural networks, optimization algorithms, CNN, RNN, LSTM, transformers

  • Features: Free audit option, taught by Andrew Ng, programming assignments


MIT - Introduction to Deep Learning

  • URL: https://introtodeeplearning.com/ 

  • Description: An introductory course covering the foundations of deep learning methods with applications to computer vision, natural language processing, biology, and more.

  • Topics Covered: Neural networks, CNN, RNN, self-supervised learning, generative models

  • Features: MIT course materials, lecture videos, coding tutorials




Natural Language Processing


Stanford - CS224N: Natural Language Processing with Deep Learning

  • URL:https://web.stanford.edu/class/cs224n/ 

  • Description: A course on the cutting-edge research in deep learning applied to NLP, focusing on neural networks and their applications.

  • Topics Covered: Word vectors, dependency parsing, RNNs, machine translation, transformers, BERT

  • Features: Stanford course materials, lecture videos, assignments


Great Learning - Free NLP Courses

  • URL:https://www.mygreatlearning.com/nlp/free-courses 

  • Description: A collection of free courses covering various aspects of natural language processing.

  • Topics Covered: Text preprocessing, sentiment analysis, language models, chatbots, transformers

  • Features: Free certification, hands-on projects, beginner-friendly



Computer Vision


OpenCV - Free Computer Vision Courses

  • URL:https://opencv.org/university/free-courses/ 

  • Description: A collection of free courses covering computer vision fundamentals and applications using OpenCV.

  • Topics Covered: Image processing, object detection, feature extraction, tracking, OpenCV programming

  • Features: Hands-on tutorials, practical examples, industry-standard library


Hugging Face - Computer Vision Course



Reinforcement Learning


Hugging Face - Deep Reinforcement Learning Course



Stanford - CS234: Reinforcement Learning

  • URL:https://web.stanford.edu/class/cs234/ 

  • Description: A course covering the fundamentals of reinforcement learning and its applications.

  • Topics Covered: MDPs, dynamic programming, Monte Carlo methods, TD learning, function approximation

  • Features: Stanford course materials, lecture videos, assignments



AI Fundamentals

Elements of AI - University of Helsinki 

  • URL:https://www.elementsofai.com/ 

  • Description: A series of free online courses created by Reaktor and the University of Helsinki to demystify AI.

  • Topics Covered: AI basics, problem solving, real-world applications, machine learning, neural networks

  • Features: No coding required, interactive exercises, university certificate



Microsoft - AI for Beginners

  • URL:https://microsoft.github.io/AI-For-Beginners/ 

  • Description: A 12-week curriculum about artificial intelligence basics.

  • Topics Covered: Symbolic AI, neural networks, computer vision, NLP, reinforcement learning

  • Features: Lesson slides, code examples, quizzes, assignments



TRENDING AI TOPICS 2025


Generative AI

Google Cloud - Introduction to Generative AI

  • URL:https://www.cloudskillsboost.google/course_templates/536 

  • Description: An introductory level microlearning course explaining what Generative AI is, how it's used, and how it differs from traditional machine learning methods.

  • Topics Covered: Generative AI fundamentals, use cases, Google tools for Gen AI app development

  • Duration: 45 minutes

  • Features: Completion badge, introductory level content



Microsoft - Generative AI for Beginners

  • URL:https://microsoft.github.io/generative-ai-for-beginners/ 

  • Description: A comprehensive 21-lesson course teaching everything needed to start building Generative AI applications.

  • Topics Covered: LLMs, prompt engineering, text generation, chat applications, image generation, RAG, AI agents, fine-tuning

  • Features: Video lessons, code samples in Python and TypeScript, multi-language support



AI Agents and Autonomous Systems

  • Hugging Face - AI Agents Course

  • URL:https://huggingface.co/learn/agents-course/en/unit0/introduction 

  • Description: A comprehensive free course taking you from beginner to expert in understanding, using, and building AI agents.

  • Topics Covered: Agent fundamentals, frameworks (smolagents, LangGraph, LlamaIndex), use cases, observability and evaluation

  • Features: Hands-on exercises, certification option, community challenges, Discord study groups



Multimodal AI


Simplilearn - Free Multimodal RAG Course

  • URL:https://www.simplilearn.com/free-multimodal-rag-course-skillup 

  • Description: An introduction to integrating text, images, and audio data into retrieval-augmented generation (RAG) systems.

  • Topics Covered: Designing multimodal AI models, data integration, building and training RAG models, evaluation techniques

  • Duration: 2 hours of self-paced video lessons

  • Features: Completion certificate, 90 days of access, beginner-friendly content



AI-Powered Social Media Tools


Great Learning - Basics of Social Media Automation



Augmented Reality + AI


Unity - Mobile AR Development

  • URL:https://learn.unity.com/pathway/mobile-ar-development 

  • Description: A comprehensive learning pathway to create augmented reality applications compatible with iOS and Android devices.

  • Topics Covered: AR development fundamentals, visual scripting, Unity's AR tools, mobile deployment

  • Duration: Approximately 1h 45m

  • Features: Free access, hands-on tutorials, project-based learning



Quantum AI


openHPI - Quantum Machine Learning with IBM Quantum Research



AI Ethics and Responsible AI


University of Helsinki - Ethics of AI

  • URL:https://ethics-of-ai.mooc.fi/ 

  • Description: A comprehensive free course exploring the ethical aspects of artificial intelligence for anyone interested in AI ethics.

  • Topics Covered: Ethical frameworks, AI transparency, fairness, privacy, accountability, sustainable AI development

  • Features: Self-paced learning, accessible to all backgrounds, practical ethical thinking skills



AI for Sustainability


LinkedIn Learning - An Introduction to AI and Sustainability

  • URL:https://www.linkedin.com/learning/an-introduction-to-ai-and-sustainability 

  • Description: A course exploring how AI can help achieve sustainability targets like net zero emissions while protecting and restoring nature.

  • Topics Covered: AI for carbon emission reduction, renewable energy access, waste reduction, biodiversity protection

  • Duration: 41 minutes

  • Features: Free course content, exercise files, completion certificate option




Edge AI and Decentralized Intelligence


Intel - Edge AI Fundamentals with OpenVINO




Comments


bottom of page