Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/fazmin/generative-ai-essentials

This is a curated collection of valuable Generative AI resources, including free courses, videos, articles, and books. It covers topics from Machine Learning and NLP fundamentals to advanced concepts, sourced from leading AI organizations and educational institutions like OpenAI, Google, Stanford, and more.
https://github.com/fazmin/generative-ai-essentials

anthropic aws generative-ai google ibm learning-resources machine-learning mcmaster microsoft natural-language-processing openai stanford

Last synced: about 1 month ago
JSON representation

This is a curated collection of valuable Generative AI resources, including free courses, videos, articles, and books. It covers topics from Machine Learning and NLP fundamentals to advanced concepts, sourced from leading AI organizations and educational institutions like OpenAI, Google, Stanford, and more.

Awesome Lists containing this project

README 

          
# 🎓 Generative AI Learning Collection



## introduction



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.



---



## 📚 Table of Contents

- [Foundational Concepts](#foundational-concepts)

- [Building Blocks](#building-blocks)

- [Mastering Generative AI](#mastering-generative-ai)

- [Specialized Generative AI Courses](#specialized-generative-ai-courses)

- [Cutting-Edge Research & Literature](#cutting-edge-research--literature)

- [Supplemental Materials](#supplemental-materials)

  - [Machine Learning Essentials](#machine-learning-essentials)

  - [DeepLearning.AI Offerings](#deeplearningai-offerings)

  - [Further Learning](#further-learning)

  - [Key Articles 📝](#key-articles-📝)

  - [Recommended Books 📖](#recommended-books-📖)

- [Get Involved](#get-involved)



---



## 🧑‍🏫 Foundational Concepts



### Courses



- **Machine Learning Fundamentals – Stanford University**  

  🔗 [Course Link](https://www.coursera.org/specializations/machine-learning-introduction)  

  **Description:** Covers ML basics like linear regression, decision trees, and model evaluation.



- **Python for Data Science, AI & Development – IBM**  

  🔗 [Course Link](https://www.coursera.org/learn/python-for-applied-data-science-ai)  

  **Description:** Learn Python basics, data types, and functions for Data Science.



- **AI for Everyone – DeepLearning.AI**  

  🔗 [Course Link](https://www.coursera.org/learn/ai-for-everyone)  

  **Description:** An introduction to AI concepts, ethics, and applications, perfect for non-technical learners.



- **Introduction to AI with Python – Harvard University**  

  🔗 [Course Link](https://lnkd.in/g4Sbb3nQ)  

  **Description:** A 7-week course covering AI technologies and machine learning basics.



### Videos

- **Mathematics for ML**  

  🎬 [Watch Video](https://youtu.be/oMY2uKjx_Zc)  

  **Topics Covered:** Linear algebra, calculus, and foundational math for ML.



- **Data Science Basics**  

  🎬 [Watch Video](https://youtu.be/maxyUZGB3QY)  

  **Topics Covered:** Core concepts in data science and ML fundamentals.



### Books 📖

- **"Python Crash Course" by Eric Matthes**  

  **Description:** A beginner-friendly introduction to Python, suitable for data science and AI applications.



---



## 🧑‍💻 Building Blocks



### Courses

- **Neural Networks & Deep Learning – DeepLearning.AI**  

  🔗 [Course Link](https://www.coursera.org/learn/neural-networks-deep-learning)  

  **Description:** Understand core architectures of neural networks and deep learning models.



- **Data Science & ML – Harvard University**  

  🔗 [Course Link](https://pll.harvard.edu/course/data-science-machine-learning)  

  **Description:** Covers intermediate machine learning concepts, probability, and statistics.



- **Generative AI with Large Language Models – AWS**  

  🔗 [Course Link](https://www.coursera.org/learn/generative-ai-with-llms)  

  **Description:** Build and deploy large language models (LLMs) with AWS resources.



### Videos

- **Training Embeddings for Recommendation Systems**  

  🎬 [Watch Video](https://youtu.be/DN4S96oHRhE)  

  **Topics Covered:** Key concepts in embeddings and their use in recommendation engines.



- **Data Science: Visualization**  

  🎬 [Watch Video](https://youtu.be/Y6PEpkEdXDQ)  

  **Topics Covered:** Visualizing data with Python libraries.



### Books 📖

- **"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.



---



## 🧑‍🔬 Mastering Generative AI - Advanced



### Courses

- **Advanced Machine Learning on Google Cloud Specialization – Google**  

  🔗 [Course Link](https://www.coursera.org/specializations/advanced-machine-learning-tensorflow-gcp)  

  **Description:** Covers advanced ML techniques, including model optimization and hyperparameter tuning.



- **AI Workflow: Feature Engineering and Bias Detection – IBM**  

  🔗 [Course Link](https://www.coursera.org/learn/ibm-ai-workflow-feature-engineering-bias-detection)  

  **Description:** Focuses on data preparation, bias detection, and model validation techniques.



- **Supervised Machine Learning: Regression and Classification**  

  🔗 [Course Link](https://www.coursera.org/learn/machine-learning?id=285&irgwc=1)  

  **Description:** An in-depth course on supervised ML techniques with applications in regression and classification.



### Videos

- **Deep Residual Learning for Image Recognition**  

  🎬 [Watch Video](https://youtu.be/WQj8QtjC3gA)  

  **Topics Covered:** Understanding deep residual networks for image recognition tasks.



- **Attention Mechanisms and Transformers**  

  🎬 [Watch Video](https://youtu.be/v-0J7o-nDBE)  

  **Topics Covered:** Deep dive into attention mechanisms and transformer models.



### Books 📖

- **"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville**  

  **Description:** A comprehensive resource for deep learning concepts, covering theory and applications.



---



## 🌟 Specialized Generative AI Courses



### Anthropic

- **Introduction to AI Fluency**

  🔗 [Course Link](https://www.anthropic.com/ai-fluency/introduction-to-ai-fluency)

  **Description:** Learn to collaborate with AI systems effectively, efficiently, ethically, and safely.



### Google

- **LLMOps – Google Cloud & DeepLearning.AI**  

  🔗 [Course Link](https://www.deeplearning.ai/short-courses/llmops/)  

  **Description:** Learn LLM operations, from pre-processing to model deployment.



### Microsoft

- **Generative AI for Data Analysis Professional Certificate**  

  🔗 [Course Link](https://microsoft.github.io/AI-For-Beginners/)  

  **Description:** Covering data analysis and generative AI with real-world applications.



### OpenAI

- **ChatGPT Prompt Engineering for Devs**  

  🔗 [Course Link](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)  

  **Description:** OpenAI's specialized course on prompt engineering for conversational AI models.



### Gemini

- **Understanding Responsible AI – Gemini AI Lab**  

  🔗 [Course Link](https://www.cloudskillsboost.google/course_templates/554)  

  **Description:** Focuses on responsible and ethical AI practices.



### GitHub - Awesome Generative AI

- **Awesome Generative AI Guide – Aishwarya Reganti**  

  🔗 [Course Link](https://github.com/aishwaryanr/awesome-generative-ai-guide)  

  **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.



### GitHub - LLM Mastery

- **LLM Mastery In 30 Days – Vasanth51430**  

  🔗 [Course Link](https://github.com/Vasanth51430/LLM_Mastery_In_30_Days)  

  **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.

  



---



## 📘 Other Resources



### AI Basics

 * [Navigating GenAI: An Introduction for Researchers](https://cris.utoronto.ca/event/navigating-genai-an-introduction-for-researchers-sept-19-2024/)

     * **Includes:** Key AI concepts, applications of GenAI, challenges and risks.

 * [MS Co-pilot – A Protected Alternative to ChatGPT](https://easi.its.utoronto.ca/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](https://promptengineering.org/a-guide-to-conversational-and-structured-prompting/)

     * **Includes:** The difference between conversational versus structured prompting techniques, how to optimize interactions with AI chatbots.



 ### Ethical and Safe Use of AI Tools

 * [Use Artificial Intelligence Intelligently](https://security.utoronto.ca/governance/guidelines/use-ai-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](https://utoronto.sharepoint.com/sites/its-easi/SharedServices/_layouts/15/stream.aspx?id=%2Fsites%2Fits%2Deasi%2FSharedServices%2FEngagement%20Coordinator%2FConnect%2BLearn%2FConnect%2BLearn%20Recordings%2FConnect%2BLearn%20%2D%20AI%20hallucinations%20in%20practice%5F%20Tools%20and%20techniques%20for%20reliable%20generation%2D20250522%5F110039%2DMeeting%20Recording%2Emp4&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0&ga=1&referrer=StreamWebApp%2EWeb&referrerScenario=AddressBarCopied%2Eview%2E6b869116%2D40df%2D419a%2D8975%2D5ae1456cfc8a)

     * **Includes:** Strategies for mitigating hallucinations or incorrect responses from chatbots, covering administrative, research, and coding tasks.

 * [Using Generative AI Ethically at Work](https://www.linkedin.com/learning/using-generative-ai-ethically-at-work/recognize-ethical-choices-with-ai?autoSkip=true&resume=false&u=76812730)

     * **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](https://www.cyber.gc.ca/en/education-community/learning-hub/courses/cyber-security-users-generative-artificial-intelligence)

     * **Includes:** Guidance and safe use of GenAI at work, ethical concerns, risks, and limitations of GenAI, practical use of GenAI tools.



 ### Literature Exploration & Summarization

 * [Introduction to Scopus AI and Web of Science Research Assistant to Explore Literature](https://cris.utoronto.ca/event/introduction-to-scopus-ai-and-web-of-science-research-assistant-to-explore-literature-apr-9-2025/)

     * **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](https://www.youtube.com/watch?v=5LAbLZUy_SU)

      * **Includes:** Overview of GenAI tools for literature discovery, summarization, and synthesis.



 ### Working with Data

 * [Data Analysis: Quantitative Data](https://www.youtube.com/watch?v=DJYTtpeUGbA)

     * **Includes:** Generating code and graphics using GenAI chatbots, exploring data and generalizing code for various models.

 * [GenAI Tools for Data Visualization and Presenting Information](https://www.youtube.com/watch?v=RnWNTnhrHZo)

     * **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](https://youtu.be/58ISUGR2ey8?si=5B5P2vSPWXWAtznP)

     * **Includes:** Overview of AI technology in qualitative data analysis and software platforms, rigor and ethical implications of using AI in qualitative research.



 ### Writing & Creating

 * [Techniques for Supercharging Academic Writing with Generative AI](https://www-nature-com.myaccess.library.utoronto.ca/articles/s41551-024-01185-8)

     * **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](https://utoronto.sharepoint.com/sites/ctsi-PublicFiles/_layouts/15/stream.aspx?id=%2Fsites%2Fctsi%2DPublicFiles%2FShared%20Documents%2FCTSI%20Workshops%2F2025%20Programming%2FCTSI%20Workshop%5F%20Creative%20and%20Critical%20Thinking%20with%20Generative%20AI%2D20250130%5F130446%2DMeeting%20Recording%2Emp4&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0&ga=1&referrer=StreamWebApp%2EWeb&referrerScenario=AddressBarCopied%2Eview%2E07b48955%2D4e82%2D442e%2D9de6%2D10b58ba0b30b)

     * **Includes:** Implications of generative AI on creative and critical thinking, models and strategies for integrating AI-assisted creative and critical thinking in curriculum design.



 ### Project & Time Management

 * [Reclaiming Our Time: AI and Academic Productivity](https://www.ncfdd.org/webinars/aiacademy)

     * **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](https://www.linkedin.com/learning-login/share?account=76812730&forceAccount=false&redirect=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fhow-to-boost-your-productivity-with-ai-tools%3Ftrk%3Dshare_ent_url%26shareId%3DO1lG08XQR424abD%252Fq0dKuA%253D%253D)

     * **Includes:** Framework for incorporating AI into everyday tasks and useful prompts to streamline tasks and working more efficiently.

 



### Machine Learning Essentials



- [Reinforcement Learning](https://github.com/victorfiz/ucl_ml/blob/main/reinforcement_learning/Learning_Guide_RL.pdf): a guide by Nishant Aklecha

- [LLM Visualisation](https://bbycroft.net/llm)

- [Chip Huyen Blog](https://huyenchip.com/blog/)

- [Lil'Log Blog](https://lilianweng.github.io/)

- [All of Deep Learning in 1 hour](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

- [Simons Institute](https://www.youtube.com/@SimonsInstituteTOC/streams)

- [Phil Wang Github – Architecture Implementations](https://github.com/lucidrains)

- [Gabriel Mongaras](https://www.youtube.com/@gabrielmongaras/videos)

- [Information Theory, Inference, and Learning Algorithms – David MacKay](https://www.inference.org.uk/itprnn/book.pdf)



# Top 15 YouTube Channels to Level Up Your Machine Learning Skills



### YouTube Channels



- [**Two Minute Papers**](https://www.youtube.com/@TwoMinutePapers)

  * **Description:** Hosted by Konrad Kording, this channel summarizes the latest research papers in short videos, ideal for staying updated on new developments.

- [**Sentdex**](https://www.youtube.com/@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**](https://www.youtube.com/@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**](https://www.youtube.com/@ArtificialIntelligenceAllinOne)

  * **Description:** A comprehensive resource for AI fundamentals, machine learning, deep learning, computer vision, and NLP, accessible to all skill levels.

- [**Kaggle**](https://www.youtube.com/@KaggleCommunity)

  * **Description:** Covers tutorials for various skill levels, features interviews with industry gurus, and shares winning solutions from Kaggle competitions.

- [**Siraj Raval**](https://www.youtube.com/@sirajraval)

  * **Description:** Explores topics in machine learning, deep learning, computer vision, and NLP with a fun and engaging teaching style.

- [**Jeremy Howard**](https://www.youtube.com/@jeremyphoward)

  * **Description:** Co-founder of fast.ai, his channel aims to make AI accessible to everyone with easy-to-understand video lectures.

- [**Applied AI Course**](https://www.youtube.com/@AppliedAICourse)

  * **Description:** Focuses on practical machine learning knowledge, teaching core ideas through real-world case studies to build AI solutions.

- [**Krish Naik**](https://www.youtube.com/@krishnkhd)

  * **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**](https://www.youtube.com/@StatQuest)

  * **Description:** Provides educational content on statistics, data science, and machine learning, breaking down complex concepts and their mathematical underpinnings.

- [**Daniel Bourke**](https://www.youtube.com/@mrdbourke)

  * **Description:** A self-taught machine learning engineer who guides viewers from beginner to master in ML, including PyTorch.

- [**Data School**](https://www.youtube.com/@dataschool)

  * **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**](https://www.youtube.com/@3blue1brown)

  * **Description:** Grant Sanderson explains complex mathematical and machine learning concepts through appealing and intuitive animations for a broad audience.

- [**Jeff Heaton**](https://www.youtube.com/@jeffheaton)

  * **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**](https://www.youtube.com/@MachineLearningStreetTalk)

  * **Description:** Managed by Tim Scarfe, this channel covers the latest developments in AI and ML with in-depth analysis and interviews with leading thinkers.



### Courses from DeepLearning.AI



- [AI for Everyone](https://www.deeplearning.ai/ai-for-everyone/)

- [Generative AI with Large Language Models](https://www.deeplearning.ai/generative-ai-with-llms/)

- [Neural Networks and Deep Learning](https://www.deeplearning.ai/neural-networks-and-deep-learning/)

- [Structuring Machine Learning Projects](https://www.deeplearning.ai/structuring-ml-projects/)

- [Improving Deep Neural Networks](https://www.deeplearning.ai/improving-deep-neural-networks/)

- [AI for Medicine](https://www.deeplearning.ai/ai-for-medicine/)

- [Natural Language Processing Specialization](https://www.deeplearning.ai/nlp-specialization/)

- [Generative Adversarial Networks](https://www.deeplearning.ai/generative-adversarial-networks/)

- [AI Ethics](https://www.deeplearning.ai/ai-ethics/)



# UCL Machine Learning MSc

This is an archive of lectures, resources, and coursework from my time at UCL.



### Maybe if you are a student

- [Probabilistic and Unsupervised Learning](https://github.com/victorfiz/ucl_ml/blob/main/unsupervised_learning/Probabilistic%20and%20Unsupervised%20Learning.pdf)

- [Approximate Inference](https://github.com/victorfiz/ucl_ml/blob/main/approximate_inference/Approximate_Inference.pdf)

- [Advanced](https://github.com/victorfiz/ucl_ml/blob/main/advanced_topics/kernels.pdf) [Topics in Machine Learning](https://github.com/victorfiz/ucl_ml/blob/main/advanced_topics/convex-optimisation.pdf)

- [Supervised Learning](https://github.com/victorfiz/ucl_ml/blob/main/supervised_learning/Supervised_Learning_Cheat_Sheet.pdf)

- [Bayesian Deep Learning](https://github.com/victorfiz/ucl_ml/tree/main/bayesian_deep_learning/Bayesian%20Deep%20Learning)

- [Reinforcement Learning](https://github.com/victorfiz/ucl_ml/tree/main/reinforcement_learning)

- [ML Seminar: GPs, Belief Prop, Norm Flows, Meta Learning](https://github.com/victorfiz/ucl_ml/blob/main/ml-seminar/machine-learning-seminar.pdf)



### Extra Resources



- [NVIDIA Online Courses](https://t.co/orcYDKCs4v)

- [Stanford CS229: Building Large Language Models](https://t.co/Eh0IYhHY0g)

- [Learn Generative AI in 21 Hours](https://www.freecodecamp.org/news/learn-generative-ai-for-developers/)

- [LLM Evaluation](https://t.co/xsYgjw0x5g)

- [Awesome Generative AI Guide](https://github.com/aishwaryanr/awesome-generative-ai-guide)

---



## Books



1. **"Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf**  

   **Description:** Practical guide to working with transformer-based NLP models.



2. **"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.



3. **"The Hundred-Page Machine Learning Book" by Andriy Burkov**  

   **Description:** A concise yet comprehensive overview of machine learning concepts.



4. **"Machine Learning Yearning" by Andrew Ng**  

   **Description:** Free book offering insights into how to structure ML projects effectively.



---



## Articles



- **"Attention is All You Need"**  

  📄 [Read Article](https://arxiv.org/abs/1706.03762)  

  **Description:** Foundational paper on the Transformer model, revolutionizing NLP.



- **"Understanding LSTMs" by Christopher Olah**  

  📄 [Read Article](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)  

  **Description:** An illustrated guide to Long Short-Term Memory (LSTM) networks.



- **"Scaling Laws for Neural Language Models"**  

  📄 [Read Article](https://example.com)  

  **Description:** Research on scaling language models and their impacts on performance.



---



## 📊 Categorized Resources



### Machine Learning



| **Category**       | **Topic**                                      | **Resource Type**     | **Link** |

|--------------------|------------------------------------------------|-----------------------|----------|

| Machine Learning   | Mathematics for ML                             | Video                 | [Watch](https://youtu.be/oMY2uKjx_Zc) |

| Machine Learning   | Linear Regression                              | Course                | [Link](https://lnkd.in/gdRsMHbn) |

| Machine Learning   | Logistic Regression                            | Course                | [Link](https://lnkd.in/gtPfmQUv) |

| Machine Learning   | Naive Bayes Classifier                         | Video                 | [Watch](https://youtu.be/IvTCdrx1SHQ) |

| Machine Learning   | Dimensionality Reduction (PCA, AutoEncoders)   | Course                | [Link](https://lnkd.in/gC6XQfez) |

| Machine Learning   | Data Science: Machine Learning (Harvard)       | Course                | [Link](https://lnkd.in/eBPDfkqd) |

| Machine Learning   | Machine Learning Crash Course                  | Course (Google)       | [Link](https://developers.google.com/machine-learning/crash-course) |

| Machine Learning   | Data Science: Linear Regression (Harvard)      | Course                | [Link](https://pll.harvard.edu/course/data-science-linear-regression/2023-10) |



### Generative AI



| **Category**       | **Topic**                                      | **Resource Type**     | **Link** |

|--------------------|------------------------------------------------|-----------------------|----------|

| Generative AI      | ChatGPT Prompt Engineering for Devs            | Course (OpenAI)       | [Link](https://lnkd.in/gtGc5Znp) |

| Generative AI      | LLMOps (Google Cloud & DeepLearning.AI)        | Course                | [Link](https://lnkd.in/gMXDr7MJ) |

| Generative AI      | Generative AI for Data Analysis (Microsoft)    | Professional Certificate | [Link](https://lnkd.in/eKJ9qmEQ) |

| Generative AI      | AI for Everyone (DeepLearning.AI)              | Course                | [Link](https://www.deeplearning.ai/ai-for-everyone/) |

| Generative AI      | Generative AI with Large Language Models (AWS) | Course                | [Link](https://lnkd.in/dSNEtsDz) |

| Generative AI      | Generative Deep Learning by David Foster       | Book                  | - |



### Statistics



| **Category**       | **Topic**                                      | **Resource Type**     | **Link** |

|--------------------|------------------------------------------------|-----------------------|----------|

| Statistics         | Statistics Fundamentals                        | Playlist              | [Link](https://lnkd.in/gCNme3W9) |

| Statistics         | Data Science: Probability (Harvard)            | Course                | [Link](https://lnkd.in/ecEFv-hE) |

| Statistics         | Probability                                    | Course                | [Link](https://pll.harvard.edu/course/data-science-probability) |

| Statistics         | Data Science: Probability (Great Learning)     | Course                | [Link](https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science) |

| Statistics         | Statistics and R (Harvard)                     | Course                | [Link](https://edx.org/learn/r-programming/harvard-university-statistics-and-r) |

| Statistics         | Data Science: Probability (Harvard)            | Course                | [Link](https://lnkd.in/ecEFv-hE) |



### Programming



| **Category**       | **Topic**                                      | **Resource Type**     | **Link** |

|--------------------|------------------------------------------------|-----------------------|----------|

| Programming        | Python for Data Science, AI & Development (IBM)| Course                | [Link](https://lnkd.in/dAzq8jCr) |

| Programming        | R Programming Fundamentals                     | Course (Stanford)     | [Link](https://lnkd.in/eYrsBwAH) |

| Programming        | SQL for Data Science                           | Course                | [Link](https://lnkd.in/eSUCR9jB) |

| Programming        | MongoDB Basics                                 | Course                | [Link](https://lnkd.in/es6miJmh) |

| Programming        | Python for Data Science (Playlist)             | Playlist              | [Link](https://lnkd.in/gzD7cy6R) |



### LangChain and Prompt Engineering



| **Category**                       | **Topic**                            | **Resource Type**     | **Link** |

|------------------------------------|--------------------------------------|-----------------------|----------|

| LangChain and Prompt Engineering   | LangChain Prompt Templates           | Course                | [Link](https://lnkd.in/dVkuiizQ) |

| LangChain and Prompt Engineering   | Building LLM Agents Using LangChain  | Course                | [Link](https://lnkd.in/dmTgfzYV) |

| LangChain and Prompt Engineering   | LangChain Output Parsing             | Course                | [Link](https://lnkd.in/dYvjufGD) |

| LangChain and Prompt Engineering   | Understanding LangChain Chains       | Course                | [Link](https://lnkd.in/deE4HYpu) |



### Other Specialized Topics



| **Category**               | **Topic**                               | **Resource Type**     | **Link** |

|----------------------------|-----------------------------------------|-----------------------|----------|

| Other Specialized Topics   | Dynamic Pricing in Ecommerce            | Video                 | [Watch](https://youtu.be/a_CXpnsvPa0) |

| Other Specialized Topics   | Transparent Machine Learning with GenAI | Video                 | [Watch](https://youtu.be/PPl0MRuCKLo) |

| Other Specialized Topics   | RAG from Scratch                        | Course                | [Link](https://lnkd.in/gKBqvbF3) |

| Other Specialized Topics   | Detecting Buyer-side Returns Fraud      | Video                 | [Watch](https://youtu.be/as4i1tUo0EA) |

| Other Specialized Topics   | LinkedIn’s CTR Modeling                 | Video                 | [Watch](https://youtu.be/7l0HLYVFEuU) |

| Other Specialized Topics   | Building Large Language Models (Stanford CS229) | Course          | [Link](https://t.co/Eh0IYhHY0g) |



---



### Top 9 LLMs Making Waves in the Industry



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.





I'm always open to adding more, so feel free to share any great resources you've come across!




---



### List of Noteworthy LLMs



Here's a list of top LLMs, each with distinct capabilities:



1.  **OpenAI**

    🔗 [OpenAI Model Release Notes](https://help.openai.com/en/articles/9624314-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.



2.  **DeepSeek**

    🔗 [DeepSeek Website](https://www.deepseek.com/)

    **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.



3.  **Qwen**

    🔗 [Qwen LM GitHub](https://qwenlm.github.io/)

    **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.



4.  **Grok**

    🔗 [xAI Grok Blog](https://x.ai/blog/grok-3)

    **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.



5.  **Llama**

    🔗 [Meta AI Website](https://ai.meta.com/)

    **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.



6.  **Claude**

    🔗 [Claude AI by Anthropic](https://claude.ai/new)

    **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.



7.  **Mistral**

    🔗 [Mistral AI Chat](https://chat.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.



8.  **Gemini**

    🔗 [Google DeepMind Gemini](https://deepmind.google/technologies/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.



9.  **Command R**

    🔗 [Cohere Command Models](https://cohere.com/command)

    **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.

    

---



## You have something to Contribute



Please submit a pull request on GitHub or reach out with your suggestions.



Thank you and have a wonderful day!