📚 Learning Resources for AI Engineers
A curated list of the best resources for your 100-day journey.
Your #1 resource for success is the COMMUNITY.
🎯 Discord Server - MANDATORY
Join NOW: https://discord.gg/9eFXYntYa8
Why this is your most valuable resource:
- ✅ Get unstuck in minutes - Community answers your questions fast
- ✅ Code reviews - Learn from feedback on your projects
- ✅ Accountability - Daily check-ins keep you consistent
- ✅ Motivation - See others crushing it, stay inspired
- ✅ Networking - Connect with AI Engineers and employers
- ✅ Job opportunities - Members share openings
Key Channels:
#100daysofaiengineer - Daily updates and quick questions100daysofaiengineer forum - Project showcases and detailed discussions- Weekly code review sessions
- Monthly graduation ceremonies
Related Guides:
Username on ALL platforms: @CODERCOPS
- 🐦 Twitter/X: https://twitter.com/CODERCOPS
- Daily AI tips, quick wins, community highlights
- Use #100DaysOfAIEngineer in your posts
- 💼 LinkedIn: https://linkedin.com/company/CODERCOPS
- Professional content, career advice, job postings
- Connect with alumni and industry professionals
- 📸 Instagram: https://instagram.com/CODERCOPS
- Visual progress, project showcases, motivation
- Behind-the-scenes of AI Engineering
- 🎥 YouTube: https://youtube.com/@CODERCOPS
- Project walkthroughs, tutorials, interviews
- Deep dives into AI concepts
- 💻 GitHub: https://github.com/CODERCOPS
- Open source projects, code templates
- Example implementations
Engage actively, tag @CODERCOPS, build your professional brand in public!
📝 Blog Articles Collection (NEW!)
🔥 BLOG_ARTICLES.md - 150+ Curated Blog Posts for Each Topic!
We’ve researched and curated 150+ high-quality blog articles specifically for the 100-day curriculum:
- Organized by Phase & Topic: Find articles for exactly what you’re learning
- 2024-2025 Content: Latest tutorials and best practices
- Verified Quality: Tested tutorials from trusted sources
- Code Examples: All articles include practical implementations
- Direct Links: No searching required, just click and learn
Topics Covered:
- NumPy, Pandas, Data Visualization
- Classical ML Algorithms
- Neural Networks & Backpropagation
- PyTorch & Deep Learning
- CNNs, Transfer Learning, YOLO
- NLP, BERT, Transformers
- LLMs, Fine-tuning, LoRA/QLoRA
- RAG, LangChain, Vector Databases
- MLOps, FastAPI, Docker, MLflow
👉 Open BLOG_ARTICLES.md to start learning with curated content!
📖 Books
Machine Learning Fundamentals
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
- Perfect for beginners
- Practical approach with code examples
- Covers classical ML and deep learning
- O’Reilly Link
- “Pattern Recognition and Machine Learning” by Christopher Bishop
- More theoretical approach
- Comprehensive coverage
- Great for understanding fundamentals
- “The Hundred-Page Machine Learning Book” by Andriy Burkov
- Concise overview
- Good for quick reference
- Covers breadth well
Deep Learning
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- The definitive deep learning textbook
- Theoretical foundation
- Free online
- “Deep Learning with PyTorch” by Eli Stevens et al.
- Practical PyTorch guide
- Step-by-step tutorials
- Real-world projects
- “Dive into Deep Learning” by Aston Zhang et al.
- Interactive book with code
- Free online
- Excellent for hands-on learning
Natural Language Processing
- “Natural Language Processing with Transformers” by Lewis Tunstall et al.
- Modern NLP with Transformers
- Hugging Face library
- Practical examples
- “Speech and Language Processing” by Dan Jurafsky and James H. Martin
Computer Vision
- “Deep Learning for Computer Vision” by Rajalingappaa Shanmugamani
- Practical CV guide
- CNN architectures
- Object detection and segmentation
- “Computer Vision: Algorithms and Applications” by Richard Szeliski
MLOps & Production
- “Designing Machine Learning Systems” by Chip Huyen
- Production ML systems
- Real-world considerations
- Best practices
- “Machine Learning Engineering” by Andriy Burkov
- End-to-end ML systems
- Deployment and monitoring
🎓 Online Courses
Foundations
Fast.ai - Practical Deep Learning for Coders
- Free
- Top-down teaching approach
- Great for building intuition
- Course Link
Andrew Ng - Machine Learning (Coursera)
- Classic ML course
- Strong theoretical foundation
- Course Link
Andrew Ng - Deep Learning Specialization
- 5-course series
- Covers neural networks, CNNs, RNNs, Transformers
- Course Link
Advanced
Stanford CS229 - Machine Learning
Stanford CS231n - Convolutional Neural Networks for Visual Recognition
Stanford CS224N - Natural Language Processing with Deep Learning
- Excellent NLP course
- Covers transformers and LLMs
- Course Link
MIT 6.S191 - Introduction to Deep Learning
- Fast-paced introduction
- Covers latest techniques
- Course Link
Specialized
Hugging Face Course - NLP
- Free course on Transformers
- Hands-on with Hugging Face library
- Course Link
Full Stack Deep Learning
DeepLearning.AI - LLMs Courses
- LangChain
- Vector databases
- Fine-tuning LLMs
- Course Link
🎥 YouTube Channels
Theory & Concepts
3Blue1Brown
- Beautiful math visualizations
- Neural networks series
- Linear algebra series
- Channel
StatQuest with Josh Starmer
- Clear explanations of ML concepts
- Statistics fundamentals
- Channel
Andrej Karpathy
- Neural networks from scratch
- GPT explanations
- Channel
Paper Reviews & News
Yannic Kilcher
- Deep dives into papers
- ML news and discussions
- Channel
Two Minute Papers
- Quick paper summaries
- Latest AI research
- Channel
Tutorials & Projects
sentdex
- Python tutorials
- ML/DL projects
- Channel
Nicholas Renotte
- Computer vision projects
- Object detection tutorials
- Channel
Aladdin Persson
- PyTorch tutorials
- Paper implementations
- Channel
📝 Blogs & Websites
Technical Blogs
Distill.pub
- Interactive ML explanations
- High-quality visualizations
- Website
Towards Data Science
- Wide range of ML articles
- Practical tutorials
- Website
Papers with Code
- Latest research papers
- Code implementations
- Benchmarks and leaderboards
- Website
Sebastian Ruder’s Blog
- NLP research insights
- Transfer learning
- Blog
Andrej Karpathy’s Blog
- In-depth ML articles
- “Yes you should understand backprop”
- Blog
Jay Alammar’s Blog
- Visual guides to ML concepts
- Transformers explained
- Blog
Lil’Log by Lilian Weng
- Deep learning research
- Comprehensive guides
- Blog
Company Blogs
OpenAI Blog
- Latest from OpenAI
- GPT, DALL-E updates
- Blog
Google AI Blog
- Research from Google
- New models and techniques
- Blog
Meta AI Blog
- PyTorch updates
- Vision and language research
- Blog
DeepMind Blog
- AlphaFold, AlphaGo
- Reinforcement learning
- Blog
Development Environments
Jupyter Notebook / JupyterLab
- Interactive Python development
- Great for experiments
- Install
Google Colab
- Free GPU access
- No setup required
- Website
Kaggle Notebooks
- Free GPU/TPU
- Access to datasets
- Website
VS Code with Python Extensions
- Professional IDE
- Jupyter integration
- Download
PyCharm
- Python-specific IDE
- Professional features
- Download
Machine Learning Frameworks
PyTorch
- Most popular for research
- Dynamic computational graphs
- Docs
TensorFlow / Keras
- Production-ready
- TensorFlow Lite for mobile
- Docs
scikit-learn
- Classical ML algorithms
- Simple API
- Docs
XGBoost / LightGBM / CatBoost
- Gradient boosting libraries
- Great for tabular data
- Competition winners
Deep Learning Libraries
Hugging Face Transformers
- Pre-trained models
- Easy fine-tuning
- Docs
timm (PyTorch Image Models)
- Computer vision models
- Pre-trained weights
- GitHub
torchvision / torchtext / torchaudio
- Domain-specific tools for PyTorch
- Datasets and transforms
Detectron2
- Object detection and segmentation
- Facebook AI Research
- GitHub
Computer Vision
OpenCV
- Computer vision library
- Image processing
- Docs
Albumentations
- Image augmentation
- Fast and flexible
- Docs
YOLO (Ultralytics)
- Object detection
- Easy to use
- Docs
spaCy
- Industrial-strength NLP
- Fast and efficient
- Docs
NLTK
- Natural language toolkit
- Educational resource
- Docs
Gensim
- Topic modeling
- Word2Vec implementation
- Docs
LangChain
- LLM application framework
- Chains and agents
- Docs
LlamaIndex
- Data framework for LLMs
- RAG applications
- Docs
OpenAI API
- GPT-3.5, GPT-4
- DALL-E, Whisper
- Docs
Anthropic Claude
- Claude API
- Large context window
- Docs
Vector Databases
ChromaDB
- Embeddings database
- Easy to use
- Docs
Pinecone
- Managed vector database
- Fast and scalable
- Website
Weaviate
- Open-source vector DB
- GraphQL API
- Docs
FAISS
- Facebook AI Similarity Search
- Efficient similarity search
- GitHub
MLOps & Experiment Tracking
MLflow
- Experiment tracking
- Model registry
- Docs
Weights & Biases (W&B)
- Experiment tracking
- Visualization
- Website
TensorBoard
- Visualization toolkit
- Training metrics
- Docs
DVC (Data Version Control)
- Version control for data/models
- Pipeline management
- Docs
Neptune.ai
- Experiment tracking
- Model registry
- Website
Deployment
FastAPI
- Modern web framework
- Fast and easy
- Docs
Flask
- Lightweight web framework
- Simple and flexible
- Docs
Streamlit
- Quick ML web apps
- No frontend knowledge needed
- Docs
Gradio
- ML model interfaces
- Share demos easily
- Docs
Docker
- Containerization
- Consistent environments
- Docs
Kubernetes
- Container orchestration
- Scalable deployments
- Docs
AWS SageMaker
- End-to-end ML platform
- Managed infrastructure
- Docs
Google Cloud Vertex AI
- Unified ML platform
- AutoML capabilities
- Docs
Azure Machine Learning
- Enterprise ML platform
- MLOps tools
- Docs
Hugging Face Spaces
- Free ML app hosting
- Gradio/Streamlit support
- Website
📊 Datasets
General Purpose
Kaggle Datasets
- Thousands of datasets
- Competitions
- Website
UCI Machine Learning Repository
- Classic ML datasets
- Well-documented
- Website
Google Dataset Search
Papers with Code Datasets
- Datasets from papers
- Benchmarks
- Website
Computer Vision
ImageNet
- 14M images, 1000 classes
- Image classification
- Website
COCO (Common Objects in Context)
- Object detection, segmentation
- Keypoint detection
- Website
CIFAR-10/100
- Small image classification
- 60,000 images
- Website
Open Images
- 9M images
- 600 object classes
- Website
Natural Language Processing
Common Crawl
- Web crawl data
- Used for training LLMs
- Website
The Pile
- 825 GB diverse text data
- For language models
- Website
GLUE Benchmark
- NLP task suite
- Model evaluation
- Website
SQuAD (Stanford Question Answering Dataset)
- Reading comprehension
- Q&A pairs
- Website
Audio
LibriSpeech
- Speech recognition
- 1000 hours of audio
- Website
Common Voice (Mozilla)
- Multilingual speech dataset
- Open source
- Website
Time Series
UCR Time Series Archive
- 128 time series datasets
- Classification tasks
- Website
👥 Communities
Forums & Discussion
r/MachineLearning (Reddit)
- Research discussions
- Paper releases
- Link
r/learnmachinelearning (Reddit)
- Learning resources
- Beginner-friendly
- Link
Stack Overflow
- Technical Q&A
- Code debugging
- Link
Cross Validated (Stack Exchange)
- Statistics and ML theory
- Link
Discord Servers
Hugging Face Discord
- NLP and Transformers
- Active community
- Join
PyTorch Discord
- PyTorch help
- Discussions
- Join
AI/ML Discord Communities
- Various specialized servers
- Search for invite links
Conferences
NeurIPS - Neural Information Processing Systems
ICML - International Conference on Machine Learning
CVPR - Computer Vision and Pattern Recognition
ACL - Association for Computational Linguistics
ICLR - International Conference on Learning Representations
📰 Newsletters
The Batch (DeepLearning.AI)
- Weekly AI news
- Andrew Ng’s insights
- Subscribe
Import AI
- Weekly AI newsletter
- Research summaries
- Subscribe
TLDR AI
The Gradient
- AI research magazine
- Long-form articles
- Website
Kaggle
- Competitions
- Learn & practice
- Website
LeetCode
- Coding problems
- ML/AI section
- Website
HackerRank
- AI challenges
- Skill certification
- Website
DrivenData
- Social good competitions
- Real-world problems
- Website
📱 Mobile Apps
Brilliant
- Interactive math & CS
- AI courses
- App Link
Coursera / Udacity Apps
- Learn on the go
- Downloadable lectures
💡 Tips for Using These Resources
- Don’t try to consume everything - Pick 2-3 resources per topic
- Prioritize hands-on practice - Reading/watching < Coding
- Follow a structured path - Complete one course before jumping to another
- Join one community - Active participation > Lurking in many
- Build projects - Apply what you learn immediately
- Contribute to open source - Learn from code reviews
- Stay updated - Follow 2-3 newsletters max
- Read papers - Start with blog summaries, then read originals
🔄 Resource Update Schedule
This resource list is updated regularly. Check back for:
- New courses and books
- Latest tools and frameworks
- Updated links
Last Updated: 2025
Happy Learning! 🚀