A comprehensive roadmap for mastering Generative AI, including free courses, videos, articles, and books. Curated from resources shared by experts across LinkedIn, Twitter, and major AI platforms like Microsoft, Google, OpenAI, IBM, and more. This guide is designed to support learners from beginner to advanced levels. Contributions are welcome!
Curated with contributions from LinkedIn, Twitter, and other social media sources.
Welcome to the Generative AI Learning Roadmap! 🎉 This guide is a comprehensive resource, covering free courses, videos, articles, and books that will take you from the fundamentals of Machine Learning and NLP to the advanced world of Generative AI. Whether you're a beginner or an experienced AI enthusiast, this roadmap provides a structured path for deep learning.
This guide is curated from a collection of resources shared on LinkedIn, Twitter, and other social media channels, as well as suggestions from renowned educational institutions and leading AI organizations including Microsoft, OpenAI, Google, IBM, AWS, Stanford, Harvard, and more.
- Beginner Level
- Intermediate Level
- Advanced Level
- Specialized Generative AI Courses
- LangChain and Prompt Engineering
- Advanced Reading & Research
- Additional Resources
- Contributing to this Guide
- Closing Notes
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Python for Data Science, AI & Development – IBM
🔗 Course Link
Description: Learn Python basics, data types, and functions for Data Science. -
Machine Learning Fundamentals – Stanford University
🔗 Course Link
Description: Covers ML basics like linear regression, decision trees, and model evaluation. -
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.
- 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.
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LangChain Prompt Templates
🔗 Course Link
Description: Building and applying prompt templates in LangChain. -
LangChain ChatBots Memory
🔗 Course Link
Description: Techniques for memory-aware chatbots using LangChain.
This section includes influential research papers and readings recommended by Ilya Sutskever, a pioneer in the AI and machine learning field. These papers are foundational for understanding neural networks, LSTMs, and other advanced AI concepts.
- The First Law of Complexodynamics
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Understanding LSTM Networks
- Recurrent Neural Network Regularization
- Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
- Pointer Networks
- ImageNet Classification with Deep Convolutional Neural Networks
- Order Matters: Sequence to Sequence for Sets
- GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
- Deep Residual Learning for Image Recognition
- Multi-Scale Context Aggregation by Dilated Convolutions
- Neural Message Passing for Quantum Chemistry
- Attention is All You Need
- Neural Machine Translation by Jointly Learning to Align and Translate
- Identity Mappings in Deep Residual Networks
- A Simple Neural Network Module for Relational Reasoning
- Variational Lossy Autoencoder
- Relational Recurrent Neural Networks
- Quantifying the Rise and Fall of Complexity in Closed Systems: the Coffee Automaton
- Neural Turing Machines
- Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
- Scaling Laws for Neural Language Models
- A Tutorial Introduction to the Minimum Description Length Principle
- Machine Super Intelligence
- Kolmogorov Complexity and Algorithmic Randomness
- Stanford’s CS231n Convolutional Neural Networks for Visual Recognition
- Dense Passage Retriever (DPR)
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Zephyr: Direct Distillation of LM Alignment
- Lost in the Middle: How Language Models Use Long Contexts
- 🎯 Mathematics for ML
- 🎯 Linear Regression
- 🎯 Logistic Regression
- 🎯 Data Science Basics, Alternative Link
- 🎯 Isotonic Regression
- 🎯 ML Metrics for Classification
- 🎯 Categorical Variable Encoding Strategies
- 🎯 Naive Bayes Classifier
- 🎯 Dimensionality Reduction (PCA, AutoEncoders)
- 🎯 Entropy, Cross-Entropy, KL-Divergence
- 🎯 Probability, Model Calibration
- 🎯 Data Drift Detection, Model Monitoring
- 🎯 Dynamic Pricing in Ecommerce
- 🎯 Training Embeddings for Recommendation Systems
- 🎯 ANN in Recsys (Annoy)
- 🎯 ANN in Recsys (Product Quantizer)
- 🎯 Model-Based Recommendations @ Twitter
- 🎯 PID Controller for Diversity in Recommender Systems
- 🎯 Instagram’s Recommendation System
- 🎯 Train Neural Networks to Approximate Any Function
- 🎯 BERT for Embeddings
- 🎯 Twitter's Recommendation Algorithm
- 🎯 Model Compression with Knowledge Distillation
- 🎯 Conversational AI (Chat-GPT)
- 🎯 Dual Nature of Conversational LLMs
- 🎯 Enhancing LLMs
- 🎯 Falcon & LLAMA-2, Second Video
- 🎯 Supercharging LLama-2 & Falcon, Alternate Link
- 🎯 SRKGPT AI with Shahrukh Khan's Style
- 🎯 LinkedIn’s CTR Modeling
- 🎯 Meituan’s Two-Tower Recsys Model
- 🎯 Twitter & Instagram Recommender Systems
- 🎯 Scalable Query-Item Two-Tower Model
- 🎯 Overcoming Biases in Recsys
- 🎯 Evolution of Recsys
- 🎯 Multi-Armed Bandit Strategies
- 🎯 Uplift Modeling to Detect Causal Effect
- 🎯 Netflix’s Unified Recommendation ML Model
- 🎯 Netflix’s Calibrated Recommendations
- 🎯 Intro to GANs & Stable Diffusion
- 🎯 PySpark Essentials
- 🎯 LinkedIn's Budget Pacing for Targeted Ads
- 🎯 Detecting Buyer-side Returns Fraud
- 🎯 ML System to Combat Counterfeit Fraud in E-Commerce
- 🎯 Transparent Machine Learning with GenAI
- 🎯 Pinterest Ranking: GBDT to Deep Learning
- 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
- 📚 Stanford CS229: Building Large Language Models
- 🎓 Learn Generative AI in 21 Hours
- 🎥 NVIDIA Online Courses
- 🧠 LLM Evaluation
- 📝 Awesome Generative AI Guide
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"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. -
"Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf
Description: Practical guide to working with transformer-based NLP models. -
"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 |
|---|---|---|---|
| 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 |
|---|---|---|---|
| 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 |
|---|---|---|---|
| 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 roadmap is designed to be a living document. We invite you to contribute by adding new resources, suggesting improvements, or sharing additional insights! Please submit a pull request on GitHub or reach out with your suggestions. Let’s build a comprehensive learning path for everyone interested in Generative AI. 🙌
This roadmap is designed to help learners advance through different levels of understanding in Generative AI. Be consistent in your learning, practice regularly, and make the most of the amazing free resources available. Enjoy your journey toward becoming a Generative AI expert! 😄