Machine Learning with Python

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🤖Still Scared AI Is Coming For Your Job? Here's 20 Repos To Actually Do Something About It 1. Prompt-Engineering-Guide Explains how to actually get useful, reliable output from AI models instead of guessing and hoping. 2. AI-For-Beginners Covers the broader AI landscape, not just LLMs, so you're not caught off guard by the next big shift either. 3. generative-ai-for-beginners A calm, lesson-by-lesson intro to building with generative AI, no ML background assumed. 4. anthropic-cookbook Real code examples for building things with Claude, straight from the people who make it. 5. openai-cookbook The same idea for OpenAI's models, practical patterns instead of theory. 6. ML-For-Beginners Twelve weeks of classic machine learning basics, useful groundwork before jumping into AI specifically. 7. llama_index Handles connecting AI models to your own data and documents, a skill that comes up constantly in real jobs now. 8. ollama Lets you run AI models on your own machine, good for understanding them without relying on an API key. 9. llama.cpp Shows what's actually happening when a model runs, right down to the hardware level. 10. transformers The library behind most modern AI models, worth exploring even just to see how they're built. 11. awesome-ai-agents A running list of AI agent projects and frameworks, handy for keeping track of a space that moves fast. 12. nanoGPT A small, readable implementation of GPT, built by Andrej Karpathy so you can see the whole thing without getting lost. 13. AutoGPT One of the earliest autonomous AI agent projects, still useful for understanding how agents plan and act. 14. gpt-engineer An AI agent that writes entire codebases from a prompt, worth trying just to see what it's actually capable of. 15. OpenHands An open source AI coding agent, a good look at where AI-assisted development is heading. 16. awesome-machine-learning A broad, well-organized list of ML resources and libraries across every language, a solid reference to keep bookmarked. 17. langchain The most widely used framework for wiring AI models into actual applications, worth knowing even if you end up not using it. 18. Awesome-LLM A curated list of papers, models, and tools, good for going deeper once the basics feel comfortable. 19. applied-ml Real-world write-ups from companies on how they actually use ML and AI in production, not just in demos. 20. llm-course A structured path from "what is an LLM" to actually fine-tuning and deploying one yourself.

Updated CS 8803 "Large Language Model" course at Georgia Tech for 2026. The list of materials covers pre-training, Mixture of Experts (MoE), reasoning, reinforcement learning and self-play, agents, long context, scaling during inference, diffusion language models, safety, interpretability, and much more. - https://cocoxu.github.io/CS8803-LLM-spring2026/ - https://docs.google.com/spreadsheets/d/1Oisf4imoNL3fs4UWGYAUlMCuYfCACMHCDb0iqEYU8wc/edit?usp=sharing

📝 Huge cheat sheet for plotting in Matplotlib with code examples 📊 Matplotlib is a powerful plotting library in Python used for creating static, animated, and interactive visualizations. Main features of Matplotlib: 💬 Versatility: can generate a wide range of plots including line plots, scatter plots, bar charts, histograms, and pie charts. 💬 Customization: offers extensive options to control every aspect of the plot such as line styles, colors, markers, labe 🏷 Category: Cheat Sheet 💾 Size: 3.1 MB 📄 Pages: 99 🆓 100% free — public domain / open-source resource.

📚 "Natural Language Processing and Large Language Models" is a new open-access book from Springer, written by Chengqing Zong, Yang Zhao, and Yanjun Ma. It's almost 400 pages long and provides an introduction to modern natural language processing and large language models. Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF. In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨ https://link.springer.com/book/10.1007/978-981-92-0682-7 #NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮 The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈 A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠 The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊 In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨ https://arxiv.org/pdf/2409.02668 #MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A