AI/ML Learning Checklist β ΒΆ
START HERE β Essential Reading (Do This First)ΒΆ
Done MASTER_STUDY_GUIDE.md β Phase-by-phase learning notes, text explanations, and study schedule. Start here.
Done CAREER_ROADMAP.md β Target roles, interview prep, portfolio strategy, and 30-60-90 day plan.
Done REFERENCES.md β All videos, GitHub repos, courses, papers, and tools organized by phase.
Done INTERVIEW_PREP.md β 15 ML Q&A with answers, specific coding problems, 3 system design walkthroughs.
π How to Use This ChecklistΒΆ
Phase 0: Glossary & FoundationsΒΆ
Done 00-course-setup/2026_model_landscape.md β frontier models, open-weight models, benchmarks as of 2026
Done 00-course-setup/ai_dev_tools_2026.md β Cursor, Windsurf, Aider, GitHub Copilot comparison
Familiarize yourself with basic ML concepts
Understand the difference between supervised/unsupervised learning
Done MASTER_STUDY_GUIDE.md β choose your learning track (AI Engineer / ML Engineer / Data Scientist)
Phase 1: Python & Machine Learning (278 notebooks)ΒΆ
Python FoundationsΒΆ
Done NumPy tutorials in 02-data-science/1-numpy-examples/
Done pandas tutorials in 02-data-science/2-pandas-examples/
Done data science examples in 02-data-science/3-data-science-examples/
Scikit-learn Mastery (278 notebooks)ΒΆ
Done 02-data-science/5-scikit-learn/ - 278 example notebooks
Practice linear regression examples
Practice classification examples (SVM, Random Forest, etc.)
Practice clustering examples (K-means, DBSCAN, etc.)
Practice dimensionality reduction (PCA, t-SNE, etc.)
Microsoft LabsΒΆ
Done Machine Learning for Beginners (26 lessons)
Done Data Science for Beginners (20 lessons)
ProjectsΒΆ
Build a classification model (e.g., Iris dataset)
Build a regression model (e.g., housing prices)
Build a clustering project (e.g., customer segmentation)
Phase 2: Mathematics for MLΒΆ
Linear AlgebraΒΆ
Done 03-maths/foundational/01_linear_algebra_fundamentals.ipynb
Done 3Blue1Brown - Essence of Linear Algebra (see REFERENCES.md)
Calculus & DerivativesΒΆ
Watch 3Blue1Brown - Essence of Calculus
Probability & StatisticsΒΆ
Optimization & Gradient DescentΒΆ
ISLP Book (13 notebooks)ΒΆ
CS229 Course NotebooksΒΆ
Video CoursesΒΆ
Done Stanford CS229 (first 10 lectures) - see 22-references/videos/
Watch StatQuest ML fundamentals
Phase 3: Tokenization (8 notebooks)ΒΆ
Tokenizer NotebooksΒΆ
DocumentationΒΆ
Phase 4: Embeddings (10 notebooks)ΒΆ
Core Embedding NotebooksΒΆ
Similarity & SearchΒΆ
Advanced TopicsΒΆ
DocumentationΒΆ
Done README.md
Done QUICKSTART.md
Phase 5: Neural NetworksΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb - Overview and learning path
Done 01_neural_network_basics.ipynb - Neurons, layers, activations, forward pass
Done 02_backpropagation_explained.ipynb - Backprop from scratch with NumPy
Done 03_pytorch_fundamentals.ipynb - Tensors, autograd, training loop in PyTorch
Done 04_attention_mechanism.ipynb - Self-attention, multi-head attention from scratch
Done 05_transformer_architecture.ipynb - Full transformer: encoder, decoder, positional encoding
Math FoundationΒΆ
Microsoft LabsΒΆ
Done AI for Beginners (24 lessons)
Video CoursesΒΆ
Done 3Blue1Brown - Neural Networks series (see REFERENCES.md)
Done Andrej Karpathy - Neural Networks: Zero to Hero (see REFERENCES.md)
Done MIT 6.S191 - Intro to Deep Learning (see REFERENCES.md)
ProjectsΒΆ
Build a neural network from scratch using only NumPy (no PyTorch)
Implement backpropagation by hand for a 2-layer network
Train a transformer on a character-level language modeling task
Phase 6: Vector Databases (7 notebooks)ΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb
DocumentationΒΆ
Done README.md
ProjectsΒΆ
Build a semantic search engine with Chroma
Deploy a vector database locally
Benchmark different vector databases
Phase 7: RAG SystemsΒΆ
Core RAG NotebooksΒΆ
Done 00_START_HERE.ipynb - RAG overview and pipeline architecture
Done 01_basic_rag.ipynb - Minimal RAG from scratch
Done 02_document_processing.ipynb - Chunking strategies and document loaders
Done 03_langchain_rag.ipynb - RAG with LangChain LCEL
Done 04_llamaindex_rag.ipynb - RAG with LlamaIndex
Done 05_advanced_retrieval.ipynb - HyDE, query expansion, reranking
Done 06_conversation_rag.ipynb - Multi-turn conversational RAG
Done 07_evaluation.ipynb - RAGAS metrics: faithfulness, relevancy, precision
Done 09_advanced_retrieval.ipynb - Parent-child retrieval, multi-vector, ensemble
Done 10_graphrag_visual_rag.ipynb - GraphRAG and multimodal RAG
DocumentationΒΆ
Done 08-rag/README.md
Microsoft LabsΒΆ
Done Generative AI for Beginners (18 lessons)
Done LangChain for Beginners (6 lessons)
ProjectsΒΆ
Build a basic RAG chatbot over a PDF
Build a document Q&A system with hybrid search + reranking
Implement RAG with RAGAS evaluation metrics
Build a fully local RAG (Ollama + ChromaDB, no cloud APIs)
Phase 8: MLOpsΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb - MLOps lifecycle overview
Done 01_experiment_tracking.ipynb - MLflow: log params, metrics, artifacts
Done 02_fastapi_basics.ipynb - Build REST API for model serving
Done 03_model_deployment.ipynb - Package and deploy end-to-end
Done 04_docker_ml.ipynb - Containerize ML models with Docker
Done 05_monitoring.ipynb - Detect data drift and model degradation
Done 06_ci_cd_pipeline.ipynb - GitHub Actions for ML CI/CD
Done 07_cloud_deployment.ipynb - Deploy to AWS/GCP/Azure
Done 09_llm_infrastructure.ipynb - vLLM, TGI, LLM serving at scale
DocumentationΒΆ
Done 09-mlops/README.md
Video CoursesΒΆ
Watch Made With ML - MLOps (free at madewithml.com)
Watch Weights & Biases tutorials for experiment tracking
ProjectsΒΆ
Deploy a classifier with FastAPI + Docker + GitHub Actions CI/CD
Set up MLflow experiment tracking for any training run
Deploy a local LLM server with vLLM (OpenAI-compatible endpoint)
Phase 9: SpecializationsΒΆ
Done 10-specializations/README.md for guidance on choosing your path
Path A: Computer Vision (best for CV Engineer / Multimodal AI roles)ΒΆ
Done 00_START_HERE.ipynb - CV overview and roadmap
Done 01_image_classification.ipynb - CNNs, ResNet, EfficientNet with PyTorch
Done 02_object_detection.ipynb - YOLO, Faster R-CNN, bounding boxes
Done 03_clip_embeddings.ipynb - CLIP for zero-shot image classification + search
Done 04_stable_diffusion.ipynb - Diffusion models and image generation
Done 05_multimodal_rag.ipynb - RAG over images + text
Path B: NLP (best for NLP Engineer / Text AI roles)ΒΆ
Done 00_START_HERE.ipynb - NLP overview
Done 01_ner.ipynb - Named Entity Recognition with transformers
Done 02_translation.ipynb - Sequence-to-sequence translation (MarianMT, Helsinki-NLP)
Done 03_summarization.ipynb - Abstractive summarization with BART/T5
Done 04_sentiment_analysis.ipynb - BERT fine-tuning for sentiment
Done 05_information_extraction.ipynb - Structured extraction from text
Path C: AI Agents Specialization (best for AI Engineer roles β pairs with Phase 14)ΒΆ
Done 00_START_HERE.ipynb - Agents specialization overview
Done 01_function_calling.ipynb - Advanced tool/function calling patterns
Done 02_react_pattern.ipynb - ReAct implementation deep dive
Done 03_langgraph_agents.ipynb - Stateful agents with LangGraph
Done 04_multi_agent_systems.ipynb - Multi-agent orchestration patterns
Done 05_memory_state.ipynb - Agent memory: short-term, long-term, episodic
Done 06_production.ipynb - Production agent: reliability, cost, monitoring
Microsoft LabsΒΆ
Phase 10: Prompt Engineering (6 notebooks) π₯ΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb
Done 05_structured_outputs_dspy.ipynb - DSPy: programmatic prompt optimization and structured outputs
Done 06_long_context_strategies.ipynb - long-context prompting, chunking, and retrieval-aware strategies
DocumentationΒΆ
Done README.md
Video CoursesΒΆ
DeepLearning.AI - ChatGPT Prompt Engineering
Google Prompting Essentials
ProjectsΒΆ
Build a ReAct agent with tools
Create a prompt optimization system
Build a multi-step reasoning chain
Phase 11: LLM Fine-tuning (12 notebooks) π₯ΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb - Overview, decision framework, hardware guide
Done 01_dataset_preparation.ipynb - Data cleaning, formatting, augmentation
Done 02_supervised_finetuning.ipynb - SFT with SFTTrainer, Flash Attention 2
Done 03_lora_basics.ipynb - LoRA fundamentals and configuration
Done 04_qlora_efficient.ipynb - QLoRA, DoRA, RSLoRA deep dive
Done 05_dpo_alignment.ipynb - DPO, RLHF, alignment techniques
Done 06_evaluation.ipynb - BLEU, BERTScore, LLM-as-judge, benchmarks
Done 07_deployment.ipynb - vLLM, Ollama, Docker, production serving
Done 08_grpo_reasoning_training.ipynb - GRPO: training reasoning models (DeepSeek-style)
Done 09_unsloth_fast_finetuning.ipynb - 2-4x faster fine-tuning with Unsloth
Done 10_quantization_gptq_awq.ipynb - quantization for deployment efficiency
Done 11_rlhf_constitutional_ai.ipynb - RLHF, constitutional AI, and alignment framing
DocumentationΒΆ
Done README.md
Video CoursesΒΆ
DeepLearning.AI - Finetuning Large Language Models
Hugging Face - Transformers Course
ProjectsΒΆ
Fine-tune a model with LoRA on a custom dataset
Apply DPO alignment to improve helpfulness
Deploy a fine-tuned model with vLLM
Compare QLoRA vs DoRA quality vs memory tradeoffs
Phase 12: Multimodal AI π₯ΒΆ
Vision-Language ModelsΒΆ
Done vision-language/01_clip_basics.ipynb - CLIP: aligning image and text embeddings, zero-shot classification
Image GenerationΒΆ
Done image-generation/01_stable_diffusion.ipynb - Stable Diffusion: latent diffusion, text-to-image, img2img
DocumentationΒΆ
Done README.md
ProjectsΒΆ
Build an image search engine: embed images with CLIP, query by text
Create a text-to-image generator with custom prompts and negative prompts
Build a multimodal chatbot that can describe and discuss images (use GPT-5.4, Gemini 3.1, or Qwen2.5-VL locally)
Phase 13: Local LLMs (6 notebooks) π₯ΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb - local LLM setup overview and learning path
Done 01_ollama_quickstart.ipynb - Ollama setup and first models
Done 02_open_source_models_overview.ipynb - Model landscape comparison
Done 03_local_rag_with_ollama.ipynb - Fully local RAG (Ollama + ChromaDB)
Done 04_llm_server_and_api.ipynb - vLLM, llama.cpp, OpenAI-compatible servers
Done 05_speculative_decoding.ipynb - latency reduction and local inference optimization concepts
DocumentationΒΆ
Done README.md
Video CoursesΒΆ
Matthew Berman - Local LLMs
ProjectsΒΆ
Set up Ollama and run Qwen 3 / Llama 4 locally
Build a fully local private RAG system over your documents
Deploy a local LLM server with OpenAI-compatible API
Phase 14: AI Agents (9 notebooks) π₯ HOT TOPICΒΆ
Core NotebooksΒΆ
Done 01_intro_to_agents.ipynb - What agents are, ReAct loop, agent anatomy
Done 02_function_calling.ipynb - Tool/function calling with OpenAI and Anthropic APIs
Done 03_react_pattern.ipynb - Reasoning + Acting loop implementation
Done 04_agent_frameworks.ipynb - LangChain Agents, LangGraph overview
Done 05_multi_agent_systems.ipynb - Orchestrator + worker agents, CrewAI, AutoGen
Done 06_mcp_model_context_protocol.ipynb - MCP: new standard for AI tool integration (2026)
Done 07_openai_agents_sdk_langgraph.ipynb - OpenAI Agents SDK and LangGraph stateful agents
Done 08_reasoning_models.ipynb - o1, o3, DeepSeek R1 - using reasoning models in agents
Done 09_autonomous_agents_2026.ipynb - State of the art: autonomous agents in 2026
DocumentationΒΆ
Pre/Post AssessmentΒΆ
Done pre-quiz.md before starting
Done post-quiz.md after finishing
ProjectsΒΆ
Build a research agent with web search + calculator + code execution tools
Build a multi-agent pipeline: planner β executor β reviewer
Build an MCP-powered agent connected to your own data sources
Create a LangGraph stateful agent with persistent memory
Phase 15: Real-Time Streaming AI (4 notebooks) π₯ΒΆ
Core NotebooksΒΆ
Done 01_streaming_responses.ipynb - SSE, OpenAI/Anthropic streaming, TTFT/TPS metrics
Done 02_websocket_connections.ipynb - WebSocket protocol, connection management, auth
Done 03_real_time_rag.ipynb - Streaming RAG, progressive context, citation tracking
Done 04_production_streaming.ipynb - Rate limiting, circuit breakers, Prometheus, load testing
DocumentationΒΆ
Done README.md
ProjectsΒΆ
Build a ChatGPT-style streaming chat interface with FastAPI + SSE
Create a real-time RAG system that streams sources then answers
Deploy a production streaming server with monitoring dashboards
Supplementary Phases (Do These Alongside or After Core Track)ΒΆ
Model Evaluation β 16-model-evaluation/ΒΆ
Done 01_classification_metrics.ipynb - Precision, recall, F1, ROC-AUC, confusion matrices
Done 02_regression_metrics.ipynb - RMSE, MAE, RΒ², residual analysis
Done 03_llm_evaluation.ipynb - BLEU, BERTScore, RAGAS, LLM-as-judge
Done 04_bias_fairness.ipynb - Demographic parity, equal opportunity, disparate impact
Done 05_model_comparison.ipynb - Statistical tests for model comparison (paired t-test, McNemar)
Debugging & Troubleshooting β 17-debugging-troubleshooting/ΒΆ
Done 01_debugging_workflow.ipynb - Systematic debugging: data β model β serving
Done 02_data_issues.ipynb - Missing values, class imbalance, label noise, distribution shift
Done 03_performance_profiling.ipynb - CPU/GPU profiling, memory leaks, training speed bottlenecks
Done 04_model_debugging.ipynb - Loss curves, gradient issues, overfitting diagnosis
Done 05_error_analysis.ipynb - Confusion matrix deep dive, error slicing, failure mode analysis
Low-Code AI Tools β 18-low-code-ai-tools/ΒΆ
Done 01_gradio_basics.ipynb - Build ML demos in minutes with Gradio
Done 02_streamlit_apps.ipynb - Data apps with Streamlit
Done 03_huggingface_spaces.ipynb - Deploy your demo for free on HuggingFace Spaces
Done 04_automl_platforms.ipynb - AutoML with H2O, AutoSklearn, TPOT
Done 05_end_to_end_project.ipynb - Full project: model β Gradio β HF Spaces deployment
AI Safety & Red Teaming β 19-ai-safety-redteaming/ΒΆ
Done 01_prompt_security.ipynb - Prompt injection attacks and defenses
Done 02_content_moderation.ipynb - Filtering harmful content with classifiers and LLM guards
Done 03_pii_privacy.ipynb - PII detection, redaction, and privacy-preserving ML
Done 04_bias_fairness.ipynb - Bias detection in LLM outputs, debiasing techniques
Done 05_red_teaming.ipynb - Systematic red teaming: jailbreaks, adversarial prompts, evaluation
π§ͺ Hands-On LabsΒΆ
Microsoft Labs (100+ lessons)ΒΆ
Complete at least 3 full courses
Build all course projects
Video Courses (50+ channels)ΒΆ
Done Stanford CS229 (full course) β see REFERENCES.md for direct links
Done Andrej Karpathy - Build GPT from scratch β see REFERENCES.md
Done 3Blue1Brown neural network series β see REFERENCES.md
Cloud PlatformsΒΆ
Complete AWS SageMaker tutorials
Try Google Cloud AI Studio
Deploy a model on cloud
π MDTP Framework - Comprehensive Knowledge MapΒΆ
M - ModelsΒΆ
M0. Statistical Thinking & IntuitionΒΆ
Probability, random variables, distributions
Model vs. reality, digital twins
Structure and causality
Inference vs generalization (Breimanβs βTwo Culturesβ)
Observation, experiment and experiment design
Treatment and treatment effects
Measurement errors and missing data
M1. Statistical InferenceΒΆ
Data generating process, sample vs population
Sampling techniques and inference
Frequentist and Bayesian views
Measuring model quality and performance
Model lifecycle, data and model drift
M2. EconometricsΒΆ
Cross-section, time series, panel and spatial data
Linear regression and OLS
Violation of OLS assumptions (Peter Kennedyβs 10 Commandments)
Estimation techniques: OLS, GMM, maximum likelihood, Bayesian
M4. Classic Machine LearningΒΆ
Classification, regression, clustering
Dimensionality reduction, decision trees
Support vector machines and discriminant analysis
Model selection and performance evaluation
M5. Neural Networks & Deep LearningΒΆ
Simple perceptron and NN construction
Gradient descent, backpropagation, regularization
Traditional architectures (feed-forward, CNN, RNN, GAN, transformers)
Embeddings and vector representations (word2vec, sentence-transformers)
Tokenization (BPE, WordPiece, SentencePiece)
Attention mechanism (self-attention, multi-head attention)
Mixture of Experts (MoE) architecture
M6. NLP, CV and Advanced SubfieldsΒΆ
Classic NLP (Jurafsky and Martin)
Large Language Models (GPT-4, Claude, LLaMA, Gemini)
Transformer architecture, prompt engineering
RAGs and fine-tuning
Instruction tuning and alignment (RLHF, DPO, constitutional AI)
In-context learning (few-shot, zero-shot, chain-of-thought)
Model compression (quantization: 4-bit, 8-bit; distillation; LoRA/QLoRA)
Hallucination detection and mitigation
Computer vision fundamentals
Multimodal models (CLIP, GPT-4V, vision-language)
Diffusion models (Stable Diffusion, DALL-E, Midjourney)
M9. Modern AI Architectures (2020-2025)ΒΆ
Transformer variants (BERT, GPT, T5)
Vision Transformers (ViT) for image understanding
State Space Models (Mamba, structured state spaces)
Retrieval-augmented architectures (RAG systems)
Sparse models and efficient transformers (Sparse Transformers, Longformer)
D - DataΒΆ
D1. Data SourcesΒΆ
Data collection, observation vs experiment
Physical sensors
Proprietary vs open data, official statistics
Data distribution and providers
Data protection and privacy
D2. Data AnalysisΒΆ
EDA and descriptive statistics
Graphs, visualizations, dashboards
Analysis as a DAG
Reproducible research
D3. Data EngineeringΒΆ
Structured data, serialization formats (CSV, JSON, XML)
SQL for tabular data
Dataframes (pandas, polars)
Processing large datasets (MapReduce, Hadoop, Spark)
Everything as a vector: text, images, sound, video
Data ingestion, transform, storage, retrieval
Vector databases (Pinecone, Weaviate, ChromaDB, pgvector)
Embedding storage and retrieval (semantic search, HNSW, FAISS)
Real-time data streaming (Kafka, Flink)
Data versioning (DVC, LakeFS)
D4. Pipelines & OrchestrationΒΆ
Modelling pipelines (Airflow)
Model delivery (FastAPI)
MLOps platforms (MLflow, Weights & Biases, Kubeflow, Neptune)
Feature stores (Feast, Tecton)
A/B testing and experimentation frameworks
Model versioning and registry
D5. Data ManagementΒΆ
Data quality assurance
Corporate data governance (DMBOK)
T - Tools (Code & Infrastructure)ΒΆ
T0. Writing CodeΒΆ
Linux and command line (local and remote)
Software development practices (version control, unit testing, APIs)
DevOps, product thinking and metrics
Python, R, Julia ecosystems
T1. Software ToolsΒΆ
Statistical packages and documentation
R for statistics, scikit-learn for ML
PyTorch, TensorFlow, Keras for neural networks
LLM frameworks (LangChain, LlamaIndex, Haystack)
Hugging Face ecosystem (Transformers, Datasets, Accelerate, PEFT)
Vector database libraries (FAISS, Annoy, hnswlib)
Modern ML frameworks (JAX, XGBoost, LightGBM, CatBoost)
T2. Cloud & Computing InfrastructureΒΆ
Cloud services and infrastructure provisioning
GPU computing (CUDA, distributed training, multi-GPU)
Container orchestration (Docker, Kubernetes for ML)
Serverless ML (AWS Lambda, Modal, Banana, Replicate)
Edge deployment (TensorFlow Lite, ONNX Runtime)
LLM hosting (vLLM, TGI, Ollama for local models)
T2.1. Understanding the Cloud (Business Perspective)ΒΆ
Client-server foundation and architecture
Virtualization and resource efficiency
Storage solutions (S3, block, object storage)
Containerization and orchestration (Kubernetes)
PaaS and serverless (Lambda)
Networking: zones, CDNs, load balancing
On-premise vs cloud tradeoffs
Economics of cloud computing
Cloud providers: AWS, GCP, Azure
Data center energy efficiency
T2.2. Databases & StorageΒΆ
HDD vs SSD vs Cloud (S3) tradeoffs
File systems (HDFS)
Relational databases (PostgreSQL, MySQL)
NoSQL types: Key-Value, Document, Column, Graph
Vector databases (Pinecone, Weaviate, ChromaDB)
Time series databases (InfluxDB, TimescaleDB)
Large data processing (MapReduce, Hadoop, Spark)
Search databases (ElasticSearch, Solr)
Data warehouses (Snowflake, Databricks, BigQuery)
Database theory: ACID, CAP theorem, BASE
OLAP vs OLTP
T2.3. Data Engineering ToolsΒΆ
Data engineering lifecycle: Generation β Storage β Ingestion β Transformation β Serving
Workflow orchestration (Airflow, Prefect, Dagster)
MLFlow for ML lifecycle management
Modern data stack components
Ingestion: Fivetran, Airbyte, Stitch
Transform: dbt, Spark
BI tools: Looker, Tableau, PowerBI
T3. Development Tools & EnvironmentΒΆ
Jupyter ecosystem (JupyterLab, notebooks, extensions)
IDEs and AI coding assistants (VS Code, Cursor, GitHub Copilot)
Experiment tracking (Weights & Biases, Neptune, Comet)
Data labeling tools (Label Studio, Prodigy, Scale AI)
P - ProductisationΒΆ
P1. Productisation & Business ValueΒΆ
Risk, learning and experimentation in business context
Modelling hypothesis and expected outcomes
Data-model-action workflows
Team roles and hiring
Measuring business outcomes and ML impact
RAG systems architecture and implementation
Prompt engineering as systematic discipline
AI agent frameworks (AutoGPT, LangChain agents, function calling)
Model monitoring (drift detection, performance tracking)
Cost optimization (token usage, caching, batch processing)
P1.1. ML Project Lifecycle FrameworkΒΆ
A. Identify Business Case - Hypothesis and expected ROI
B. Create Adequate Model - Value chain representation
C. State Proposed Change - Why, what, how, success criteria
D. Prove with Experiments - Run controlled tests
E. Scale or Pivot - Based on results
F. Continuous Improvement - Feedback loops
P1.2. Data Team Roles & ResponsibilitiesΒΆ
Understand different role functions:
Full-Stack Data Scientist
Data Scientist/Modeler
Data Engineer/Architect
Machine Learning Engineer
Research Scientist
Business Analyst
Product Manager
P1.3. Production ChallengesΒΆ
Technical: Data quality, model drift, infrastructure failures
Organizational: Silos, unclear responsibilities
Business: Unclear success criteria, ROI validation
P2. Applications, Domains & CasesΒΆ
Recommender systems (RecSys)
Clinical trials
Quality control and industrial automation
Finance applications
Conversational AI and chatbots
Code generation (GitHub Copilot, code assistants)
Document intelligence (Q&A, summarization, extraction)
Search and information retrieval (semantic search, hybrid search)
P3. Society Impacts & RegulationΒΆ
Fairness, biases, equity, ethics
Grounds for AI regulation and policy
AI safety and alignment (value alignment, red teaming)
Prompt injection and security vulnerabilities
Environmental impact (carbon footprint of training)
AI watermarking and detection
Copyright and IP issues with generative AI
π Documentation & ResourcesΒΆ
Done 22-references/README.md - All hands-on labs
Done REFERENCES.md - Full curated video, repo, and paper list
π― Final Projects (Build Your Portfolio)ΒΆ
Beginner ProjectsΒΆ
Iris classification (scikit-learn)
House price prediction (regression)
Customer segmentation (clustering)
Intermediate ProjectsΒΆ
Sentiment analysis (NLP)
Image classifier (CNN)
Recommendation system (collaborative filtering)
Advanced ProjectsΒΆ
RAG chatbot for your documents
Fine-tuned LLM for specific domain
Multimodal AI application (vision + language)
Production ProjectsΒΆ
Deploy ML model with FastAPI + Docker
Build CI/CD pipeline for ML
Create monitoring dashboard for ML models
β Completion MilestonesΒΆ
Foundation Complete - Phases 0-2 done (Python, Math, ML basics)
Modern AI Stack - Phases 3-6 done (Tokenization, Embeddings, Neural Nets, Vector DBs)
Production Ready - Phases 7-9 done (RAG, MLOps, Specializations)
Cutting-Edge AI - Phases 10-13 done (Prompt Engineering, Fine-tuning, Multimodal, Local LLMs)
Advanced Research - Phases 24-25 done (Advanced Math, Deep Learning Research)
Portfolio Built - 5+ projects deployed and documented
Certified - At least 1 ML certification (AWS, Google, Azure, or DeepLearning.AI)
Phase 24: Advanced Deep Learning (RESEARCH LEVEL) π¬ΒΆ
Generative Models - AdvancedΒΆ
Done 01_gan_mathematics.ipynb - GAN theory and vanilla implementation
Done 02_wgan_theory_implementation.ipynb - Wasserstein GAN with gradient penalty
Done 03_variational_autoencoders_advanced.ipynb - VAE theory, ELBO, Ξ²-VAE
Done 04_neural_ode.ipynb - Neural ODEs and continuous normalizing flows
Advanced Topics (To Be Implemented)ΒΆ
Info-GAN and Conditional GANs
Bayesian GANs
Advanced VAE architectures
3D Vision and NeRF
Vision Transformers (ViT)
Advanced Transformer architectures
Phase 25: Reinforcement Learning (7 notebooks)ΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb - RL fundamentals and overview
Done 01_markov_decision_processes.ipynb - MDP theory and Bellman equations
Done 02_q_learning.ipynb - tabular RL and temporal-difference learning
Done 03_deep_q_networks.ipynb - function approximation with deep networks
Done 04_policy_based_methods.ipynb - policy gradients and actor-critic ideas
Done 05_advanced_topics_applications.ipynb - broader RL applications and advanced concepts
Done 06_practical_exercises.ipynb - hands-on RL practice
Advanced TopicsΒΆ
Policy gradients and actor-critic methods (planned)
Deep reinforcement learning (planned)
Multi-agent reinforcement learning (planned)
Phase 26: Time Series Analysis & Forecasting (7 notebooks)ΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb - Time series fundamentals
Done 01_time_series_fundamentals.ipynb - decomposition, autocorrelation, and forecasting basics
Done 02_classical_statistical_methods.ipynb - ARIMA-style and statistical forecasting methods
Done 03_facebook_prophet.ipynb - Prophet for business forecasting workflows
Done 04_deep_learning_time_series.ipynb - deep learning approaches for forecasting
Done 05_advanced_techniques_applications.ipynb - advanced forecasting and applied scenarios
Done 06_practical_applications_exercises.ipynb - hands-on forecasting exercises
Advanced TopicsΒΆ
GARCH models for volatility (planned)
State space models (planned)
Bayesian time series (planned)
Phase 27: Causal Inference & Experimental Design (7 notebooks)ΒΆ
Core NotebooksΒΆ
Done 00_START_HERE.ipynb - Causal inference overview
Done 01_causal_fundamentals.ipynb - causal questions, estimands, and foundational ideas
Done 02_causal_graphs_dags.ipynb - DAGs and causal graphs
Done 03_experimental_design.ipynb - RCT design and analysis
Done 04_observational_methods.ipynb - Propensity scores, matching
Done 05_advanced_topics_applications.ipynb - advanced topics, confounding, and applied causal analysis
Done 06_quasi_experimental_designs.ipynb - RDD, DiD, IV methods
Advanced TopicsΒΆ
Mediation analysis (planned)
Causal discovery (planned)
Difference-in-differences with multiple periods (planned)
π Completion MilestonesΒΆ
Foundation Complete - Phases 0-2 done (Python, Math, ML basics)
Modern AI Stack - Phases 3-6 done (Tokenization, Embeddings, Neural Nets, Vector DBs)
Production Ready - Phases 7-9 done (RAG, MLOps, Specializations)
Cutting-Edge AI - Phases 10-13 done (Prompt Engineering, Fine-tuning, Multimodal, Local LLMs)
Research Mastery - Phases 24-27 done (Advanced Deep Learning, Reinforcement Learning, Time Series, Causal Inference)
Portfolio Built - 5+ projects deployed and documented
Certified - At least 1 ML certification (AWS, Google, Azure, or DeepLearning.AI)
π Next Steps After CompletionΒΆ
Contribute to open-source ML projects (start with docs/issues on popular repos)
Write blog posts about what you built (1 post per major project)
Mentor others in AI/ML
Build your own AI product
Apply for ML/AI jobs or research positions
Continue learning (AI never stops evolving!)
Read and implement papers from top conferences (NeurIPS, ICML, ICLR)
Contribute to ML research
Career Readiness MilestonesΒΆ
Resume Updated β AI/ML skills section added, projects listed
LinkedIn Updated β Headline reflects AI/ML focus, summary updated
GitHub Polished β 4+ repos pinned, each with README + demo
Project 1 Live β RAG chatbot deployed (HF Spaces / Render)
Project 2 Live β Fine-tuned model with evaluation results documented
Project 3 Live β MLOps pipeline or AI agent
Interview Prep β Practiced 10 core ML concept questions
Coding Practice β Solved 20+ pandas/sklearn coding problems
System Design β Practiced 3 ML system design scenarios
Networking β Connected with 10+ AI engineers on LinkedIn
Applications Sent β Applied to 20+ positions