⚡️ Machine Learning Roadmap 2026: a large map for entering ML without fairy tales about "neural networks in a month" 🤖

A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. 🚀

Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. 🧠

The roadmap is divided into 7 tracks: 📊

1. Foundation: Python, mathematics, statistics, tools 🏗️
2. Classic ML: scikit-learn, tabular data, metrics, validation 📈
3. Deep Learning: PyTorch, CNN, RNN, training loop 🧠
4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents 🤖
5. Generative AI: images, videos, audio, multimodality 🎨
6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving ⚙️
7. Specialization: CV, NLP, RecSys, RL, Safety 🎯

The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". 🚫

In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. 🛠️

A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. 🛑

In terms of time, it's no fairy tale either: ⏳

1. 0-3 months: mathematics, classic ML 📚
2. 3-6 months: Deep Learning and PyTorch 🔥
3. 6-12 months: LLM, RAG, fine-tuning, AI agents 🤖
4. 12+ months: MLOps, production, scaling, specialization 🚀

Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! 🎓

If you've long wanted to enter ML systematically, rather than jumping between videos about ChatGPT, Stable Diffusion, and "top-10 libraries", this is a good guide. 🗺️

https://github.com/justxor/MachineLearningRoadmap 🔗

#MachineLearning #AI #DataScience #LLM #MLOps #Python

🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨

We found this interactive website that shows you visually how transformer models work. 🌐📊

Transformer Explainer:
https://poloclub.github.io/transformer-explainer/

#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech

Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚

This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊

It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖

Free public repository on GitHub. 💻✨

https://github.com/dair-ai/Mathematics-for-ML

#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI

Assembling GPT-like LLMs from scratch on PyTorch 🔥

https://github.com/analyticalrohit/llms-from-scratch

📚 10 notebooks. Step-by-step explanation.

🧩 Breaks down the architecture of LLMs into simple parts.

✅ Suitable for beginners.

🛠 Completely hands-on.

#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code

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Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇

AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖

It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨

Key features: 🌟

• TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥
• Books section – lists AI/ML/DL books with short notes on where each one helps 📚
• Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓
• Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️
• Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄

Free public GitHub repo. 🆓

https://github.com/ArturoNereu/AI-Study-Group

#AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning

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🚀 Create an LLM from Scratch!

I came across a great find from Vizuara — a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. 🧠✨

Most people use ChatGPT.
But only a few actually understand how it works under the hood. ⚙️

This playlist step by step breaks down all the key concepts without overloading with complex explanations.

📚 What you will learn:
→ The architecture of Transformer 🏗️
→ The internal structure of GPT
→ Tokenization and BPE 🧩
→ Attention mechanisms 🔍
→ The process of training an LLM 📈
→ Full implementations in Python 🐍

✅ Suitable for:
• ML engineers
• AI enthusiasts
• Developers entering the GenAI field
• Anyone who is tired of explaining AI as a "black box" 🕵️

If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini — this material is worth watching. 👀

🔗 Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu

#LLM #AI #MachineLearning #Python #GenAI #DeepLearning

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🔖 Found a huge database on System Design for GenAI and LLM! 🤖📚

500+ real reviews of GenAI, LLM, and ML systems from OpenAI, Anthropic, Google, Microsoft, Netflix, and dozens of other companies. 🌐🏢

A real find for those who are building AI products or want to understand how market leaders do it. 🚀💡

⛓️ Link to GitHub
https://github.com/themanojdesai/genai-llm-ml-case-studies


#SystemDesign #GenAI #LLM #MachineLearning #AI #Tech

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The only LLM cheat sheet you'll ever need 🚀

Covers the main concepts, architectures, and practical applications.

### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)

### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)

### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)

### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)

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#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech

🔖 The Big Book on Fine-Tuning LLMs

A free 115-page book dedicated to the retraining of large language models. 📚

It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀

#LLM #FineTuning #AI #MachineLearning #DataScience #Tech

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

A new collection of free courses has been added:

🔗 https://github.com/dair-ai/ML-Course-Notes

Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚

Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠

What's inside:

• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
• A table with lectures, descriptions, videos, notes, and authors
• Links to the original lectures and accompanying notes
• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes

The idea was appreciated. 👍

Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️

#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Stop studying LLM from random articles and videos that only explain individual pieces of the puzzle.

📚 LLM from Scratch — this is a practical course on PyTorch for those who want to understand the entire path of modern LLMs: from the first Transformer block to RLHF.

Instead of endless theory, here we gather a complete model training chain:

🔹 Pretraining → Finetuning → Alignment in one course
🔹 Transformer from scratch: positional embeddings, self-attention, multi-head attention, MLP, residual connections, LayerNorm, and full Transformer blocks
🔹 Own training loop without Trainer magic: tokenization, batches, cross-entropy, validation loss, text generation
🔹 Modern architecture improvements: RMSNorm, RoPE, SwiGLU, KV Cache, sliding-window attention, and streaming cache
🔹 Full section on alignment: SFT, reward models, PPO-style RLHF, and GRPO with an analysis of how it looks in the training loop in practice

https://github.com/vivekkalyanarangan30/llm_from_scratch

#LLM #PyTorch #MachineLearning #DeepLearning #AI #Transformer

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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🔑 Use code: PRESALE-BOOK-WAVE-2GFG
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Google has published a free guide on scaling AI models and working with GPUs. 🚀

📘 How to Scale Your Model
https://jax-ml.github.io/scaling-book/

📘 How to Think About GPUs
https://jax-ml.github.io/scaling-book/gpus/

The materials discuss the principles of model scaling, the structure of GPUs, computational limitations, memory bandwidth, parallelism, and other topics that are useful when training and running modern AI models. 💡

It's completely free and available online. 🌐

#AI #MachineLearning #GPU #Scaling #DeepLearning #Tech

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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A large collection of materials on LLM Systems,

• model training (pre-training, RLHF, fault tolerance, stragglers)
• inference and serving
• agent systems
• edge deployment
• multimodal models
• technical reports from major laboratories
• reviews, benchmarks, and leaderboards
• courses on MLSys and collections of articles from conferences

https://github.com/AmberLJC/LLMSys-PaperList

#LLMSys #LLM #MachineLearning #AIResearch #DeepLearning #TechReports

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

10 GitHub repositories that are worth checking out for an AI engineer 🤖

1. Hands-On AI Engineering 🛠️

A collection of AI applications and agent systems with practical use cases of LLM.

👉 https://github.com/Sumanth077/Hands-On-AI-Engineering

2. Hands-On Large Language Models 📘

Full code from the book Hands-On Large Language Models: from basics to fine-tuning.

👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

3. AI Agents for Beginners 🎓

A free course from Microsoft with 11 lessons on creating AI agents.

👉 https://github.com/microsoft/ai-agents-for-beginners

4. GenAI Agents 🤖

A large collection of tutorials and implementations of agent systems.

👉 https://github.com/NirDiamant/GenAI_Agents

5. Made With ML 🚀

About the development, deployment, and support of production-ready ML systems.

👉 https://github.com/GokuMohandas/Made-With-ML

6. Learn Harness Engineering ⚙️

A practical course on Harness Engineering for AI agents.

👉 https://github.com/walkinglabs/learn-harness-engineering

7. AutoResearch 🔬

Autonomous cycles of ML experiments from Andrej Karpathy.

👉 https://github.com/karpathy/autoresearch

8. Designing Machine Learning Systems 📚

Notes and materials from Chip Huyen's book.

👉 https://github.com/chiphuyen/dmls-book

9. Awesome LLM Inference ⚡

A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.

👉 https://github.com/xlite-dev/Awesome-LLM-Inference

10. LLM Course 🗺️

A practical course on LLM with a roadmap and Colab notebooks.

👉 https://github.com/mlabonne/llm-course

#AI #MachineLearning #LLM #DataScience #Tech #GitHub

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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🎓 A Free AI Course for Beginners by Microsoft

For those just getting into artificial intelligence, Microsoft offers a free course.

It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.

The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.

Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.

➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md

What's your AI level right now?

❤️ — Advanced user
🔥 — Almost zero

#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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