This is a Generative AI Learning Collection featuring resources I've personally found incredibly valuable. It includes free courses, videos, articles, and books that cover everything from the basics of Machine Learning and NLP to the more advanced concepts in Generative AI. This guide is curated from a collection of resources shared on LinkedIn, X (formerly Twitter), and other social media channels, as well as suggestions from renowned educational institutions and leading AI organizations including OpenAI, Microsoft, Anthropic, Google, IBM, AWS, Stanford, Harvard, and more. I wanted to share these structured learning materials with you, whether you're just starting out or already have some AI experience.
- Foundational Concepts
- Building Blocks
- Mastering Generative AI
- Specialized Generative AI Courses
- Cutting-Edge Research & Literature
- Supplemental Materials
- Get Involved
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Machine Learning Fundamentals – Stanford University
🔗 Course Link
Description: Covers ML basics like linear regression, decision trees, and model evaluation. -
Python for Data Science, AI & Development – IBM
🔗 Course Link
Description: Learn Python basics, data types, and functions for Data Science. -
AI for Everyone – DeepLearning.AI
🔗 Course Link
Description: An introduction to AI concepts, ethics, and applications, perfect for non-technical learners. -
Introduction to AI with Python – Harvard University
🔗 Course Link
Description: A 7-week course covering AI technologies and machine learning basics.
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Mathematics for ML
🎬 Watch Video
Topics Covered: Linear algebra, calculus, and foundational math for ML. -
Data Science Basics
🎬 Watch Video
Topics Covered: Core concepts in data science and ML fundamentals.
- "Python Crash Course" by Eric Matthes
Description: A beginner-friendly introduction to Python, suitable for data science and AI applications.
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Neural Networks & Deep Learning – DeepLearning.AI
🔗 Course Link
Description: Understand core architectures of neural networks and deep learning models. -
Data Science & ML – Harvard University
🔗 Course Link
Description: Covers intermediate machine learning concepts, probability, and statistics. -
Generative AI with Large Language Models – AWS
🔗 Course Link
Description: Build and deploy large language models (LLMs) with AWS resources.
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Training Embeddings for Recommendation Systems
🎬 Watch Video
Topics Covered: Key concepts in embeddings and their use in recommendation engines. -
Data Science: Visualization
🎬 Watch Video
Topics Covered: Visualizing data with Python libraries.
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
Description: A practical guide for machine learning and deep learning with Python libraries.
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Advanced Machine Learning on Google Cloud Specialization – Google
🔗 Course Link
Description: Covers advanced ML techniques, including model optimization and hyperparameter tuning. -
AI Workflow: Feature Engineering and Bias Detection – IBM
🔗 Course Link
Description: Focuses on data preparation, bias detection, and model validation techniques. -
Supervised Machine Learning: Regression and Classification
🔗 Course Link
Description: An in-depth course on supervised ML techniques with applications in regression and classification.
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Deep Residual Learning for Image Recognition
🎬 Watch Video
Topics Covered: Understanding deep residual networks for image recognition tasks. -
Attention Mechanisms and Transformers
🎬 Watch Video
Topics Covered: Deep dive into attention mechanisms and transformer models.
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Description: A comprehensive resource for deep learning concepts, covering theory and applications.
- Introduction to AI Fluency 🔗 Course Link Description: Learn to collaborate with AI systems effectively, efficiently, ethically, and safely.
- LLMOps – Google Cloud & DeepLearning.AI
🔗 Course Link
Description: Learn LLM operations, from pre-processing to model deployment.
- Generative AI for Data Analysis Professional Certificate
🔗 Course Link
Description: Covering data analysis and generative AI with real-world applications.
- ChatGPT Prompt Engineering for Devs
🔗 Course Link
Description: OpenAI's specialized course on prompt engineering for conversational AI models.
- Understanding Responsible AI – Gemini AI Lab
🔗 Course Link
Description: Focuses on responsible and ethical AI practices.
- Awesome Generative AI Guide – Aishwarya Reganti
🔗 Course Link
Description: A curated list of resources, tools, papers, and tutorials on generative AI. This guide covers topics like large language models (LLMs), prompt engineering, diffusion models, and more. Perfect for learners at all levels seeking structured and high-quality AI content.
- LLM Mastery In 30 Days – Vasanth51430
🔗 Course Link
Description: A comprehensive 30-day roadmap to master Large Language Models (LLMs). This resource guides learners through NLP fundamentals, transformer models, fine-tuning, and deploying LLMs in real-world applications. Perfect for those looking for structured learning on LLMs and prompt engineering.
- Navigating GenAI: An Introduction for Researchers
- Includes: Key AI concepts, applications of GenAI, challenges and risks.
- MS Co-pilot – A Protected Alternative to ChatGPT
- Includes: How to access and use Microsoft Copilot, information security considerations, main uses.
- Conversational vs. Structured Prompting
- Includes: The difference between conversational versus structured prompting techniques, how to optimize interactions with AI chatbots.
- Use Artificial Intelligence Intelligently
- Includes: Guidance for everyone on recognizing privacy and information security risks and for people building or training systems with AI components.
- AI Hallucinations in Practice: Tools and Techniques for Reliable Generation
- Includes: Strategies for mitigating hallucinations or incorrect responses from chatbots, covering administrative, research, and coding tasks.
- Using Generative AI Ethically at Work
- Includes: Impact of legal and ethical considerations on GenAI use, adherence to AI regulations, identify and make ethical choices when using AI.
- Cyber Security for Users of Generative Artificial Intelligence
- Includes: Guidance and safe use of GenAI at work, ethical concerns, risks, and limitations of GenAI, practical use of GenAI tools.
- Introduction to Scopus AI and Web of Science Research Assistant to Explore Literature
- Includes: Differences between AI search engines and AI chatbots, benefits and pitfalls of using AI tools in literature reviews.
- From Search to Synthesis: AI Tools for Literature Discovery and Summarization
- Includes: Overview of GenAI tools for literature discovery, summarization, and synthesis.
- Data Analysis: Quantitative Data
- Includes: Generating code and graphics using GenAI chatbots, exploring data and generalizing code for various models.
- GenAI Tools for Data Visualization and Presenting Information
- Includes: Practical examples of leveraging LLM tools in data visualization workflows, overview of data visualization principles and discussion of GPT models for writing code and data exploration.
- Qualitative Data Analysis and Artificial Intelligence in Research: Introspection in an Evolving Era
- Includes: Overview of AI technology in qualitative data analysis and software platforms, rigor and ethical implications of using AI in qualitative research.
- Techniques for Supercharging Academic Writing with Generative AI
- Includes: Benefits and challenges of using GenAI as a writing assistant, framework for effective AI engagement, considerations of AI ethics and policy in academic writing.
- Creative and Critical Thinking with Generative AI
- Includes: Implications of generative AI on creative and critical thinking, models and strategies for integrating AI-assisted creative and critical thinking in curriculum design.
- Reclaiming Our Time: AI and Academic Productivity
- Includes: Challenges faced by faculty of colour, especially women, due to service duties and strategies for reclaiming time using AI.
- How to Boost Your Productivity with AI Tools
- Includes: Framework for incorporating AI into everyday tasks and useful prompts to streamline tasks and working more efficiently.
- Reinforcement Learning: a guide by Nishant Aklecha
- LLM Visualisation
- Chip Huyen Blog
- Lil'Log Blog
- All of Deep Learning in 1 hour
- Simons Institute
- Phil Wang Github – Architecture Implementations
- Gabriel Mongaras
- Information Theory, Inference, and Learning Algorithms – David MacKay
- Two Minute Papers
- Description: Hosted by Konrad Kording, this channel summarizes the latest research papers in short videos, ideal for staying updated on new developments.
- Sentdex
- Description: Offers a wide range of programming tutorials on machine learning, Python, finance, data analysis, robotics, and more, aimed at beginners to intermediate programmers.
- DeepLearningAI
- Description: Founded by Andrew Ng, this channel provides educational content including lectures, tutorials, and expert interviews, covering the latest trends in ML and DL.
- Artificial Intelligence — All in One
- Description: A comprehensive resource for AI fundamentals, machine learning, deep learning, computer vision, and NLP, accessible to all skill levels.
- Kaggle
- Description: Covers tutorials for various skill levels, features interviews with industry gurus, and shares winning solutions from Kaggle competitions.
- Siraj Raval
- Description: Explores topics in machine learning, deep learning, computer vision, and NLP with a fun and engaging teaching style.
- Jeremy Howard
- Description: Co-founder of fast.ai, his channel aims to make AI accessible to everyone with easy-to-understand video lectures.
- Applied AI Course
- Description: Focuses on practical machine learning knowledge, teaching core ideas through real-world case studies to build AI solutions.
- Krish Naik
- Description: An experienced educator who explains various ML, DL, and AI topics with real-world problem scenarios, making AI familiar to everyone.
- StatQuest with Josh Starmer
- Description: Provides educational content on statistics, data science, and machine learning, breaking down complex concepts and their mathematical underpinnings.
- Daniel Bourke
- Description: A self-taught machine learning engineer who guides viewers from beginner to master in ML, including PyTorch.
- Data School
- Description: Kevin Markham's channel offers in-depth tutorials and webinars with clear, concise, and step-by-step explanations of complex data science concepts.
- 3Blue1Brown
- Description: Grant Sanderson explains complex mathematical and machine learning concepts through appealing and intuitive animations for a broad audience.
- Jeff Heaton
- Description: Uses real-world examples to explain machine learning, deep learning, and AI concepts, serving as a great primer for beginners.
- Machine Learning Street Talk
- Description: Managed by Tim Scarfe, this channel covers the latest developments in AI and ML with in-depth analysis and interviews with leading thinkers.
- AI for Everyone
- Generative AI with Large Language Models
- Neural Networks and Deep Learning
- Structuring Machine Learning Projects
- Improving Deep Neural Networks
- AI for Medicine
- Natural Language Processing Specialization
- Generative Adversarial Networks
- AI Ethics
This is an archive of lectures, resources, and coursework from my time at UCL.
- Probabilistic and Unsupervised Learning
- Approximate Inference
- Advanced Topics in Machine Learning
- Supervised Learning
- Bayesian Deep Learning
- Reinforcement Learning
- ML Seminar: GPs, Belief Prop, Norm Flows, Meta Learning
- NVIDIA Online Courses
- Stanford CS229: Building Large Language Models
- Learn Generative AI in 21 Hours
- LLM Evaluation
- Awesome Generative AI Guide
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"Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf
Description: Practical guide to working with transformer-based NLP models. -
"Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play" by David Foster
Description: A guide to generative models and their applications in creative fields. -
"The Hundred-Page Machine Learning Book" by Andriy Burkov
Description: A concise yet comprehensive overview of machine learning concepts. -
"Machine Learning Yearning" by Andrew Ng
Description: Free book offering insights into how to structure ML projects effectively.
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"Attention is All You Need"
📄 Read Article
Description: Foundational paper on the Transformer model, revolutionizing NLP. -
"Understanding LSTMs" by Christopher Olah
📄 Read Article
Description: An illustrated guide to Long Short-Term Memory (LSTM) networks. -
"Scaling Laws for Neural Language Models"
📄 Read Article
Description: Research on scaling language models and their impacts on performance.
| Category | Topic | Resource Type | Link |
|---|---|---|---|
| Machine Learning | Mathematics for ML | Video | Watch |
| Machine Learning | Linear Regression | Course | Link |
| Machine Learning | Logistic Regression | Course | Link |
| Machine Learning | Naive Bayes Classifier | Video | Watch |
| Machine Learning | Dimensionality Reduction (PCA, AutoEncoders) | Course | Link |
| Machine Learning | Data Science: Machine Learning (Harvard) | Course | Link |
| Machine Learning | Machine Learning Crash Course | Course (Google) | Link |
| Machine Learning | Data Science: Linear Regression (Harvard) | Course | Link |
| Category | Topic | Resource Type | Link |
|---|---|---|---|
| Generative AI | ChatGPT Prompt Engineering for Devs | Course (OpenAI) | Link |
| Generative AI | LLMOps (Google Cloud & DeepLearning.AI) | Course | Link |
| Generative AI | Generative AI for Data Analysis (Microsoft) | Professional Certificate | Link |
| Generative AI | AI for Everyone (DeepLearning.AI) | Course | Link |
| Generative AI | Generative AI with Large Language Models (AWS) | Course | Link |
| Generative AI | Generative Deep Learning by David Foster | Book | - |
| Category | Topic | Resource Type | Link |
|---|---|---|---|
| Statistics | Statistics Fundamentals | Playlist | Link |
| Statistics | Data Science: Probability (Harvard) | Course | Link |
| Statistics | Probability | Course | Link |
| Statistics | Data Science: Probability (Great Learning) | Course | Link |
| Statistics | Statistics and R (Harvard) | Course | Link |
| Statistics | Data Science: Probability (Harvard) | Course | Link |
| Category | Topic | Resource Type | Link |
|---|---|---|---|
| Programming | Python for Data Science, AI & Development (IBM) | Course | Link |
| Programming | R Programming Fundamentals | Course (Stanford) | Link |
| Programming | SQL for Data Science | Course | Link |
| Programming | MongoDB Basics | Course | Link |
| Programming | Python for Data Science (Playlist) | Playlist | Link |
| Category | Topic | Resource Type | Link |
|---|---|---|---|
| LangChain and Prompt Engineering | LangChain Prompt Templates | Course | Link |
| LangChain and Prompt Engineering | Building LLM Agents Using LangChain | Course | Link |
| LangChain and Prompt Engineering | LangChain Output Parsing | Course | Link |
| LangChain and Prompt Engineering | Understanding LangChain Chains | Course | Link |
| Category | Topic | Resource Type | Link |
|---|---|---|---|
| Other Specialized Topics | Dynamic Pricing in Ecommerce | Video | Watch |
| Other Specialized Topics | Transparent Machine Learning with GenAI | Video | Watch |
| Other Specialized Topics | RAG from Scratch | Course | Link |
| Other Specialized Topics | Detecting Buyer-side Returns Fraud | Video | Watch |
| Other Specialized Topics | LinkedIn’s CTR Modeling | Video | Watch |
| Other Specialized Topics | Building Large Language Models (Stanford CS229) | Course | Link |
This is a Generative AI Learning Collection featuring resources I've personally found incredibly valuable. It includes free courses, videos, articles, and books that cover everything from the basics of Machine Learning and NLP to the more advanced concepts in Generative AI. This guide is curated from a collection of resources shared on LinkedIn, X (formerly Twitter), and other social media channels, as well as suggestions from renowned educational institutions and leading AI organizations including OpenAI, Microsoft, Anthropic, Google, IBM, AWS, Stanford, Harvard, and more. I wanted to share these structured learning materials with you, whether you're just starting out or already have some AI experience.
Here's a list of top LLMs, each with distinct capabilities:
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OpenAI 🔗 OpenAI Model Release Notes Summary: Features GPT-4.5, excelling in conversational AI, multi-step reasoning, and real-time interactions with multimodal capabilities. Proprietary model, best for businesses with budget flexibility.
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DeepSeek 🔗 DeepSeek Website Summary: DeepSeek-R1 (671B parameters, MoE) is a top open-source LM known for reasoning, long-form content, and efficiency in math/code generation. Ideal for integrating with enterprise data using RAG.
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Qwen 🔗 Qwen LM GitHub Summary: Alibaba's Qwen models (e.g., QwQ-32B, Qwen2.5-Max) excel in mathematical reasoning and coding with less computational resources. Open-sourced under Apache 2.0, suitable for diverse enterprise applications.
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Grok 🔗 xAI Grok Blog Summary: xAI's chatbot integrated with X, Grok 3 offers real-time information, advanced reasoning, and "DeepSearch." Recommended for fast news analysis, coding assistance, and dynamic customer support.
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Llama 🔗 Meta AI Website Summary: Meta's Llama 3.3 features multimodal capabilities, a 128,000-token context window, and outperforms alternatives in multilingual dialogue, reasoning, and coding. Open-source, offering flexibility for customization.
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Claude 🔗 Claude AI by Anthropic Summary: Anthropic's Claude 3.7 Sonnet integrates multiple reasoning approaches with an "extended thinking mode" for accuracy. Strong in coding, web development, summarization, and conversational AI.
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Mistral 🔗 Mistral AI Chat Summary: Mistral Small 3 is a 24-billion-parameter, latency-optimized model (Apache 2.0 license) for high-efficiency tasks, processing 150 tokens/second. Ideal for low-latency AI solutions and limited hardware.
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Gemini 🔗 Google DeepMind Gemini Summary: Google's Gemini 2.5 enhances complex problem-solving and multimodal understanding with a 1 million token context window. Excellent for coding and includes self-fact-checking. Proprietary, consider data privacy.
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Command R 🔗 Cohere Command Models Summary: Cohere's Command R+ specializes in RAG and business intelligence workflows with a 128k-token context window. Features native search query generation, source citation, and multilingual coverage. Cohere also offers the open-source Command A for on-premises deployment.
Please submit a pull request on GitHub or reach out with your suggestions.
Thank you and have a wonderful day!