└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # πŸŽ“ Generative AI Learning Roadmap 🌐 2 | 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! ​ 3 | 4 | > *Curated with contributions from **LinkedIn**, **Twitter**, and other social media sources.* 5 | 6 | --- 7 | 8 | ## 🏁 Introduction 9 | 10 | Welcome to the **Generative AI Learning Roadmap**! πŸŽ‰ 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. 11 | 12 | ### πŸ”— Credits 13 | 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. 14 | 15 | --- 16 | 17 | ## πŸ“š Table of Contents 18 | - [Beginner Level](#beginner-level) 19 | - [Intermediate Level](#intermediate-level) 20 | - [Advanced Level](#advanced-level) 21 | - [Specialized Generative AI Courses](#specialized-generative-ai-courses) 22 | - [LangChain and Prompt Engineering](#langchain-and-prompt-engineering) 23 | - [Advanced Reading & Research](#advanced-reading--research) 24 | - [Additional Resources](#additional-resources) 25 | - [A-Z of Machine Learning](#a-z-of-machine-learning) 26 | - [Courses from DeepLearning.AI](#courses-from-deeplearningai) 27 | - [Extra Resources](#extra-resources) 28 | - [Books πŸ“–](#books-πŸ“–) 29 | - [Articles πŸ“](#articles-πŸ“) 30 | - [Contributing to this Guide](#contributing-to-this-guide) 31 | - [Closing Notes](#closing-notes) 32 | 33 | --- 34 | 35 | ## πŸ§‘β€πŸ« Beginner Level 36 | 37 | ### Courses 38 | - **Python for Data Science, AI & Development – IBM** 39 | πŸ”— [Course Link](https://www.coursera.org/learn/python-for-applied-data-science-ai) 40 | **Description:** Learn Python basics, data types, and functions for Data Science. 41 | 42 | - **Machine Learning Fundamentals – Stanford University** 43 | πŸ”— [Course Link](https://www.coursera.org/specializations/machine-learning-introduction) 44 | **Description:** Covers ML basics like linear regression, decision trees, and model evaluation. 45 | 46 | - **AI for Everyone – DeepLearning.AI** 47 | πŸ”— [Course Link](https://www.coursera.org/learn/ai-for-everyone) 48 | **Description:** An introduction to AI concepts, ethics, and applications, perfect for non-technical learners. 49 | 50 | - **Introduction to AI with Python – Harvard University** 51 | πŸ”— [Course Link](https://lnkd.in/g4Sbb3nQ) 52 | **Description:** A 7-week course covering AI technologies and machine learning basics. 53 | 54 | ### Videos 55 | - **Mathematics for ML** 56 | 🎬 [Watch Video](https://youtu.be/oMY2uKjx_Zc) 57 | **Topics Covered:** Linear algebra, calculus, and foundational math for ML. 58 | 59 | - **Data Science Basics** 60 | 🎬 [Watch Video](https://youtu.be/maxyUZGB3QY) 61 | **Topics Covered:** Core concepts in data science and ML fundamentals. 62 | 63 | ### Books πŸ“– 64 | - **"Python Crash Course" by Eric Matthes** 65 | **Description:** A beginner-friendly introduction to Python, suitable for data science and AI applications. 66 | 67 | --- 68 | 69 | ## πŸ§‘β€πŸ’» Intermediate Level 70 | 71 | ### Courses 72 | - **Neural Networks & Deep Learning – DeepLearning.AI** 73 | πŸ”— [Course Link](https://www.coursera.org/learn/neural-networks-deep-learning) 74 | **Description:** Understand core architectures of neural networks and deep learning models. 75 | 76 | - **Data Science & ML – Harvard University** 77 | πŸ”— [Course Link](https://pll.harvard.edu/course/data-science-machine-learning) 78 | **Description:** Covers intermediate machine learning concepts, probability, and statistics. 79 | 80 | - **Generative AI with Large Language Models – AWS** 81 | πŸ”— [Course Link](https://www.coursera.org/learn/generative-ai-with-llms) 82 | **Description:** Build and deploy large language models (LLMs) with AWS resources. 83 | 84 | ### Videos 85 | - **Training Embeddings for Recommendation Systems** 86 | 🎬 [Watch Video](https://youtu.be/DN4S96oHRhE) 87 | **Topics Covered:** Key concepts in embeddings and their use in recommendation engines. 88 | 89 | - **Data Science: Visualization** 90 | 🎬 [Watch Video](https://youtu.be/Y6PEpkEdXDQ) 91 | **Topics Covered:** Visualizing data with Python libraries. 92 | 93 | ### Books πŸ“– 94 | - **"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by AurΓ©lien GΓ©ron** 95 | **Description:** A practical guide for machine learning and deep learning with Python libraries. 96 | 97 | --- 98 | 99 | ## πŸ§‘β€πŸ”¬ Advanced Level 100 | 101 | ### Courses 102 | - **Advanced Machine Learning on Google Cloud Specialization – Google** 103 | πŸ”— [Course Link](https://www.coursera.org/specializations/advanced-machine-learning-tensorflow-gcp) 104 | **Description:** Covers advanced ML techniques, including model optimization and hyperparameter tuning. 105 | 106 | - **AI Workflow: Feature Engineering and Bias Detection – IBM** 107 | πŸ”— [Course Link](https://www.coursera.org/learn/ibm-ai-workflow-feature-engineering-bias-detection) 108 | **Description:** Focuses on data preparation, bias detection, and model validation techniques. 109 | 110 | - **Supervised Machine Learning: Regression and Classification** 111 | πŸ”— [Course Link](https://www.coursera.org/learn/machine-learning?id=285&irgwc=1) 112 | **Description:** An in-depth course on supervised ML techniques with applications in regression and classification. 113 | 114 | ### Videos 115 | - **Deep Residual Learning for Image Recognition** 116 | 🎬 [Watch Video](https://youtu.be/WQj8QtjC3gA) 117 | **Topics Covered:** Understanding deep residual networks for image recognition tasks. 118 | 119 | - **Attention Mechanisms and Transformers** 120 | 🎬 [Watch Video](https://youtu.be/v-0J7o-nDBE) 121 | **Topics Covered:** Deep dive into attention mechanisms and transformer models. 122 | 123 | ### Books πŸ“– 124 | - **"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville** 125 | **Description:** A comprehensive resource for deep learning concepts, covering theory and applications. 126 | 127 | --- 128 | 129 | ## 🌟 Specialized Generative AI Courses 130 | 131 | ### Google 132 | - **LLMOps – Google Cloud & DeepLearning.AI** 133 | πŸ”— [Course Link](https://www.deeplearning.ai/short-courses/llmops/) 134 | **Description:** Learn LLM operations, from pre-processing to model deployment. 135 | 136 | ### Microsoft 137 | - **Generative AI for Data Analysis Professional Certificate** 138 | πŸ”— [Course Link](https://microsoft.github.io/AI-For-Beginners/) 139 | **Description:** Covering data analysis and generative AI with real-world applications. 140 | 141 | ### OpenAI 142 | - **ChatGPT Prompt Engineering for Devs** 143 | πŸ”— [Course Link](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) 144 | **Description:** OpenAI's specialized course on prompt engineering for conversational AI models. 145 | 146 | ### Gemini 147 | - **Understanding Responsible AI – Gemini AI Lab** 148 | πŸ”— [Course Link](https://www.cloudskillsboost.google/course_templates/554) 149 | **Description:** Focuses on responsible and ethical AI practices. 150 | 151 | ### GitHub - Awesome Generative AI 152 | - **Awesome Generative AI Guide – Aishwarya Reganti** 153 | πŸ”— [Course Link](https://github.com/aishwaryanr/awesome-generative-ai-guide) 154 | **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. 155 | 156 | ### GitHub - LLM Mastery 157 | - **LLM Mastery In 30 Days – Vasanth51430** 158 | πŸ”— [Course Link](https://github.com/Vasanth51430/LLM_Mastery_In_30_Days) 159 | **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. 160 | 161 | --- 162 | 163 | ## πŸ’‘ LangChain and Prompt Engineering 164 | 165 | - **LangChain Prompt Templates** 166 | πŸ”— [Course Link](https://lnkd.in/dVkuiizQ) 167 | **Description:** Building and applying prompt templates in LangChain. 168 | 169 | - **LangChain ChatBots Memory** 170 | πŸ”— [Course Link](https://www.kaggle.com/code/youssef19/langchain-chatbots-memory) 171 | **Description:** Techniques for memory-aware chatbots using LangChain. 172 | 173 | --- 174 | 175 | 176 | ## πŸ“š Advanced Reading & Research 177 | 178 | ### Ilya Sutskever's Top 30 Reading List 179 | 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. 180 | 181 | 1. The First Law of Complexodynamics 182 | 2. The Unreasonable Effectiveness of Recurrent Neural Networks 183 | 3. Understanding LSTM Networks 184 | 4. Recurrent Neural Network Regularization 185 | 5. Keeping Neural Networks Simple by Minimizing the Description Length of the Weights 186 | 6. Pointer Networks 187 | 7. ImageNet Classification with Deep Convolutional Neural Networks 188 | 8. Order Matters: Sequence to Sequence for Sets 189 | 9. GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism 190 | 10. Deep Residual Learning for Image Recognition 191 | 11. Multi-Scale Context Aggregation by Dilated Convolutions 192 | 12. Neural Message Passing for Quantum Chemistry 193 | 13. Attention is All You Need 194 | 14. Neural Machine Translation by Jointly Learning to Align and Translate 195 | 15. Identity Mappings in Deep Residual Networks 196 | 16. A Simple Neural Network Module for Relational Reasoning 197 | 17. Variational Lossy Autoencoder 198 | 18. Relational Recurrent Neural Networks 199 | 19. Quantifying the Rise and Fall of Complexity in Closed Systems: the Coffee Automaton 200 | 20. Neural Turing Machines 201 | 21. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin 202 | 22. Scaling Laws for Neural Language Models 203 | 23. A Tutorial Introduction to the Minimum Description Length Principle 204 | 24. Machine Super Intelligence 205 | 25. Kolmogorov Complexity and Algorithmic Randomness 206 | 26. Stanford’s CS231n Convolutional Neural Networks for Visual Recognition 207 | 27. Dense Passage Retriever (DPR) 208 | 28. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks 209 | 29. Zephyr: Direct Distillation of LM Alignment 210 | 30. Lost in the Middle: How Language Models Use Long Contexts 211 | 212 | --- 213 | 214 | ## πŸ“˜ Additional Resources 215 | 216 | ### πŸ”Ή A-Z of Machine Learning 217 | 218 | - 🎯 [Mathematics for ML](https://youtu.be/oMY2uKjx_Zc) 219 | - 🎯 [Linear Regression](https://www.youtube.com/watch?v=-OVHiTZofN0&list=PL89V0TQq5GLpnZlZMeUa8EAmM-v9QqXqQ) 220 | - 🎯 [Logistic Regression](https://www.youtube.com/watch?v=0zlDF9A4UiY&list=PL89V0TQq5GLrK1_bXqci8lkaikCliD4I6) 221 | - 🎯 [Data Science Basics](https://youtu.be/maxyUZGB3QY), [Alternative Link](https://youtu.be/Y6PEpkEdXDQ) 222 | - 🎯 [Isotonic Regression](https://youtu.be/lo3rUyk9qi0) 223 | - 🎯 [ML Metrics for Classification](https://youtu.be/E2HRSJKU-_4) 224 | - 🎯 [Categorical Variable Encoding Strategies](https://youtu.be/MKuAQv6ybc8) 225 | - 🎯 [Naive Bayes Classifier](https://youtu.be/IvTCdrx1SHQ) 226 | - 🎯 [Dimensionality Reduction (PCA, AutoEncoders)](https://www.youtube.com/watch?v=bjaTz-7BnMY) 227 | - 🎯 [Entropy, Cross-Entropy, KL-Divergence](https://www.youtube.com/watch?v=hJ8-NauTj2s) 228 | - 🎯 [Probability, Model Calibration](https://youtu.be/rG2EfFOXyg0) 229 | - 🎯 [Data Drift Detection, Model Monitoring](https://youtu.be/tQjRQWfYQ10) 230 | - 🎯 [Dynamic Pricing in Ecommerce](https://youtu.be/a_CXpnsvPa0) 231 | - 🎯 [Training Embeddings for Recommendation Systems](https://youtu.be/DN4S96oHRhE) 232 | - 🎯 [ANN in Recsys (Annoy)](https://youtu.be/DSQOrBTqmYA) 233 | - 🎯 [ANN in Recsys (Product Quantizer)](https://youtu.be/50PNumB7s3U) 234 | - 🎯 [Model-Based Recommendations @ Twitter](https://youtu.be/Xqo8fwgjxW4) 235 | - 🎯 [PID Controller for Diversity in Recommender Systems](https://youtu.be/laTxgnzjfR0) 236 | - 🎯 [Instagram’s Recommendation System](https://youtu.be/Myna6rnmCG8) 237 | - 🎯 [Train Neural Networks to Approximate Any Function](https://youtu.be/4PvGKuqRQTE) 238 | - 🎯 [BERT for Embeddings](https://youtu.be/v-0J7o-nDBE) 239 | - 🎯 [Twitter's Recommendation Algorithm](https://youtu.be/IhGq9jgcxFM) 240 | - 🎯 [Model Compression with Knowledge Distillation](https://youtu.be/1N_EBJUOjVU) 241 | - 🎯 [Conversational AI (Chat-GPT)](https://youtu.be/JKoJ5YIr2O4) 242 | - 🎯 [Dual Nature of Conversational LLMs](https://youtu.be/MHfzoHC4kek) 243 | - 🎯 [Enhancing LLMs](https://youtu.be/mF7OM_XU2S4) 244 | - 🎯 [Falcon & LLAMA-2](https://youtu.be/CxqZ5j3xlt0), [Second Video](https://youtu.be/8cc4bJtycOA) 245 | - 🎯 [Supercharging LLama-2 & Falcon](https://youtu.be/paGr-t1wSOQ), [Alternate Link](https://youtu.be/lo11Iczb0Vc) 246 | - 🎯 [SRKGPT AI with Shahrukh Khan's Style](https://youtu.be/gYPwx0DR7zc) 247 | - 🎯 [LinkedIn’s CTR Modeling](https://youtu.be/7l0HLYVFEuU) 248 | - 🎯 [Meituan’s Two-Tower Recsys Model](https://youtu.be/UhpbTSbi3lI) 249 | - 🎯 [Twitter & Instagram Recommender Systems](https://youtu.be/PaDsiJCPCXQ) 250 | - 🎯 [Scalable Query-Item Two-Tower Model](https://youtu.be/o-pZk5R0TZg) 251 | - 🎯 [Overcoming Biases in Recsys](https://youtu.be/oGb_mIdO0tA) 252 | - 🎯 [Evolution of Recsys](https://youtu.be/lgoyJn7MsH8) 253 | - 🎯 [Multi-Armed Bandit Strategies](https://youtu.be/2A5f3GrX0dA) 254 | - 🎯 [Uplift Modeling to Detect Causal Effect](https://youtu.be/rKzG0Ct_ReA) 255 | - 🎯 [Netflix’s Unified Recommendation ML Model](https://youtu.be/OKmv9sUrvk8) 256 | - 🎯 [Netflix’s Calibrated Recommendations](https://youtu.be/DOWXNrBpO4w) 257 | - 🎯 [Intro to GANs & Stable Diffusion](https://youtu.be/KUeq-wszG80) 258 | - 🎯 [PySpark Essentials](https://youtu.be/aruptWppgSs) 259 | - 🎯 [LinkedIn's Budget Pacing for Targeted Ads](https://youtu.be/R4EZ92VJvSI) 260 | - 🎯 [Detecting Buyer-side Returns Fraud](https://youtu.be/as4i1tUo0EA) 261 | - 🎯 [ML System to Combat Counterfeit Fraud in E-Commerce](https://youtu.be/YQZBgvLB_EQ) 262 | - 🎯 [Transparent Machine Learning with GenAI](https://youtu.be/PPl0MRuCKLo) 263 | - 🎯 [Pinterest Ranking: GBDT to Deep Learning](https://youtu.be/WQj8QtjC3gA) 264 | 265 | ### πŸ”Ή Courses from DeepLearning.AI 266 | 267 | - [AI for Everyone](https://www.deeplearning.ai/ai-for-everyone/) 268 | - [Generative AI with Large Language Models](https://www.deeplearning.ai/generative-ai-with-llms/) 269 | - [Neural Networks and Deep Learning](https://www.deeplearning.ai/neural-networks-and-deep-learning/) 270 | - [Structuring Machine Learning Projects](https://www.deeplearning.ai/structuring-ml-projects/) 271 | - [Improving Deep Neural Networks](https://www.deeplearning.ai/improving-deep-neural-networks/) 272 | - [AI for Medicine](https://www.deeplearning.ai/ai-for-medicine/) 273 | - [Natural Language Processing Specialization](https://www.deeplearning.ai/nlp-specialization/) 274 | - [Generative Adversarial Networks](https://www.deeplearning.ai/generative-adversarial-networks/) 275 | - [AI Ethics](https://www.deeplearning.ai/ai-ethics/) 276 | 277 | ### πŸ”Ή Extra Resources 278 | 279 | - πŸ“š [Stanford CS229: Building Large Language Models](https://t.co/Eh0IYhHY0g) 280 | - πŸŽ“ [Learn Generative AI in 21 Hours](https://www.freecodecamp.org/news/learn-generative-ai-for-developers/) 281 | - πŸŽ₯ [NVIDIA Online Courses](https://t.co/orcYDKCs4v) 282 | - 🧠 [LLM Evaluation](https://t.co/xsYgjw0x5g) 283 | - πŸ“ [Awesome Generative AI Guide](https://github.com/aishwaryanr/awesome-generative-ai-guide) 284 | --- 285 | 286 | ## πŸ“– Books πŸ“– 287 | 288 | 1. **"Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play" by David Foster** 289 | **Description:** A guide to generative models and their applications in creative fields. 290 | 291 | 2. **"Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf** 292 | **Description:** Practical guide to working with transformer-based NLP models. 293 | 294 | 3. **"The Hundred-Page Machine Learning Book" by Andriy Burkov** 295 | **Description:** A concise yet comprehensive overview of machine learning concepts. 296 | 297 | 4. **"Machine Learning Yearning" by Andrew Ng** 298 | **Description:** Free book offering insights into how to structure ML projects effectively. 299 | 300 | --- 301 | 302 | ## πŸ“ Articles πŸ“ 303 | 304 | - **"Attention is All You Need"** 305 | πŸ“„ [Read Article](https://arxiv.org/abs/1706.03762) 306 | **Description:** Foundational paper on the Transformer model, revolutionizing NLP. 307 | 308 | - **"Understanding LSTMs" by Christopher Olah** 309 | πŸ“„ [Read Article](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) 310 | **Description:** An illustrated guide to Long Short-Term Memory (LSTM) networks. 311 | 312 | - **"Scaling Laws for Neural Language Models"** 313 | πŸ“„ [Read Article](https://example.com) 314 | **Description:** Research on scaling language models and their impacts on performance. 315 | 316 | --- 317 | 318 | ## πŸ“Š Categorized Resources 319 | 320 | ### Machine Learning 321 | 322 | | **Category** | **Topic** | **Resource Type** | **Link** | 323 | |--------------------|------------------------------------------------|-----------------------|----------| 324 | | Machine Learning | Mathematics for ML | Video | [Watch](https://youtu.be/oMY2uKjx_Zc) | 325 | | Machine Learning | Linear Regression | Course | [Link](https://lnkd.in/gdRsMHbn) | 326 | | Machine Learning | Logistic Regression | Course | [Link](https://lnkd.in/gtPfmQUv) | 327 | | Machine Learning | Naive Bayes Classifier | Video | [Watch](https://youtu.be/IvTCdrx1SHQ) | 328 | | Machine Learning | Dimensionality Reduction (PCA, AutoEncoders) | Course | [Link](https://lnkd.in/gC6XQfez) | 329 | | Machine Learning | Data Science: Machine Learning (Harvard) | Course | [Link](https://lnkd.in/eBPDfkqd) | 330 | | Machine Learning | Machine Learning Crash Course | Course (Google) | [Link](https://developers.google.com/machine-learning/crash-course) | 331 | | Machine Learning | Data Science: Linear Regression (Harvard) | Course | [Link](https://pll.harvard.edu/course/data-science-linear-regression/2023-10) | 332 | 333 | ### Statistics 334 | 335 | | **Category** | **Topic** | **Resource Type** | **Link** | 336 | |--------------------|------------------------------------------------|-----------------------|----------| 337 | | Statistics | Statistics Fundamentals | Playlist | [Link](https://lnkd.in/gCNme3W9) | 338 | | Statistics | Data Science: Probability (Harvard) | Course | [Link](https://lnkd.in/ecEFv-hE) | 339 | | Statistics | Probability | Course | [Link](https://pll.harvard.edu/course/data-science-probability) | 340 | | Statistics | Data Science: Probability (Great Learning) | Course | [Link](https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science) | 341 | | Statistics | Statistics and R (Harvard) | Course | [Link](https://edx.org/learn/r-programming/harvard-university-statistics-and-r) | 342 | | Statistics | Data Science: Probability (Harvard) | Course | [Link](https://lnkd.in/ecEFv-hE) | 343 | 344 | ### Generative AI 345 | 346 | | **Category** | **Topic** | **Resource Type** | **Link** | 347 | |--------------------|------------------------------------------------|-----------------------|----------| 348 | | Generative AI | ChatGPT Prompt Engineering for Devs | Course (OpenAI) | [Link](https://lnkd.in/gtGc5Znp) | 349 | | Generative AI | LLMOps (Google Cloud & DeepLearning.AI) | Course | [Link](https://lnkd.in/gMXDr7MJ) | 350 | | Generative AI | Generative AI for Data Analysis (Microsoft) | Professional Certificate | [Link](https://lnkd.in/eKJ9qmEQ) | 351 | | Generative AI | AI for Everyone (DeepLearning.AI) | Course | [Link](https://www.deeplearning.ai/ai-for-everyone/) | 352 | | Generative AI | Generative AI with Large Language Models (AWS) | Course | [Link](https://lnkd.in/dSNEtsDz) | 353 | | Generative AI | Generative Deep Learning by David Foster | Book | - | 354 | 355 | ### Programming 356 | 357 | | **Category** | **Topic** | **Resource Type** | **Link** | 358 | |--------------------|------------------------------------------------|-----------------------|----------| 359 | | Programming | Python for Data Science, AI & Development (IBM)| Course | [Link](https://lnkd.in/dAzq8jCr) | 360 | | Programming | R Programming Fundamentals | Course (Stanford) | [Link](https://lnkd.in/eYrsBwAH) | 361 | | Programming | SQL for Data Science | Course | [Link](https://lnkd.in/eSUCR9jB) | 362 | | Programming | MongoDB Basics | Course | [Link](https://lnkd.in/es6miJmh) | 363 | | Programming | Python for Data Science (Playlist) | Playlist | [Link](https://lnkd.in/gzD7cy6R) | 364 | 365 | ### LangChain and Prompt Engineering 366 | 367 | | **Category** | **Topic** | **Resource Type** | **Link** | 368 | |------------------------------------|--------------------------------------|-----------------------|----------| 369 | | LangChain and Prompt Engineering | LangChain Prompt Templates | Course | [Link](https://lnkd.in/dVkuiizQ) | 370 | | LangChain and Prompt Engineering | Building LLM Agents Using LangChain | Course | [Link](https://lnkd.in/dmTgfzYV) | 371 | | LangChain and Prompt Engineering | LangChain Output Parsing | Course | [Link](https://lnkd.in/dYvjufGD) | 372 | | LangChain and Prompt Engineering | Understanding LangChain Chains | Course | [Link](https://lnkd.in/deE4HYpu) | 373 | 374 | ### Other Specialized Topics 375 | 376 | | **Category** | **Topic** | **Resource Type** | **Link** | 377 | |----------------------------|-----------------------------------------|-----------------------|----------| 378 | | Other Specialized Topics | Dynamic Pricing in Ecommerce | Video | [Watch](https://youtu.be/a_CXpnsvPa0) | 379 | | Other Specialized Topics | Transparent Machine Learning with GenAI | Video | [Watch](https://youtu.be/PPl0MRuCKLo) | 380 | | Other Specialized Topics | RAG from Scratch | Course | [Link](https://lnkd.in/gKBqvbF3) | 381 | | Other Specialized Topics | Detecting Buyer-side Returns Fraud | Video | [Watch](https://youtu.be/as4i1tUo0EA) | 382 | | Other Specialized Topics | LinkedIn’s CTR Modeling | Video | [Watch](https://youtu.be/7l0HLYVFEuU) | 383 | | Other Specialized Topics | Building Large Language Models (Stanford CS229) | Course | [Link](https://t.co/Eh0IYhHY0g) | 384 | 385 | --- 386 | 387 | ## πŸ“’ Contributing to this Guide 388 | 389 | 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. πŸ™Œ 390 | 391 | --- 392 | 393 | ## πŸ“ˆ Closing Notes 394 | 395 | 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! πŸ˜„ 396 | 397 | 398 | --------------------------------------------------------------------------------