1.1

Mathematics for AI

Linear Algebra

  • Vectors, matrices, tensors
  • Matrix operations and transformations
  • Eigenvalues and eigenvectors
  • Singular Value Decomposition (SVD)

Calculus

  • Derivatives and partial derivatives
  • Gradient, Jacobian, Hessian
  • Chain rule and backpropagation
  • Optimization techniques

Probability & Statistics

  • Probability distributions (Gaussian, Bernoulli, Multinomial)
  • Bayes' theorem
  • Maximum Likelihood Estimation (MLE)
  • Statistical inference and hypothesis testing
  • Expectation, variance, covariance

Discrete Mathematics

  • Graph theory
  • Combinatorics
  • Logic and set theory
1.2

Programming Fundamentals

Python Programming

  • Data structures (lists, dictionaries, sets, tuples)
  • Object-oriented programming
  • Functional programming concepts
  • File handling and I/O operations

Essential Libraries

  • NumPy (numerical computing)
1.3

Data Structures & Algorithms

  • Arrays, linked lists, stacks, queues
  • Trees (binary trees, BST, heaps)
  • Graphs and graph algorithms
  • Sorting and searching algorithms
  • Dynamic programming
  • Time and space complexity analysis
2.1

Introduction to Machine Learning

  • Types of learning (supervised, unsupervised, reinforcement)
  • Bias-variance tradeoff
  • Overfitting and underfitting
  • Train-test split, cross-validation
  • Performance metrics
  • Feature engineering and selection
2.2

Supervised Learning

Regression Algorithms

  • Linear Regression
  • Polynomial Regression
  • Ridge and Lasso Regression
  • ElasticNet
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Gradient Boosting Regression
  • XGBoost, LightGBM, CatBoost

Classification Algorithms

  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • Gradient Boosting Classifiers
  • AdaBoost
  • Multi-layer Perceptron (MLP)
2.3

Unsupervised Learning

Clustering Algorithms

  • K-Means Clustering
  • Hierarchical Clustering (Agglomerative, Divisive)
  • DBSCAN
  • Mean Shift
  • Gaussian Mixture Models (GMM)
  • Spectral Clustering
  • OPTICS

Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • t-SNE (t-distributed Stochastic Neighbor Embedding)
  • UMAP (Uniform Manifold Approximation)
  • Autoencoders
  • Factor Analysis
  • Independent Component Analysis (ICA)

Association Rule Learning

  • Apriori Algorithm
  • FP-Growth
  • ECLAT
2.4

Ensemble Methods

  • Bagging
  • Boosting (AdaBoost, Gradient Boosting)
  • Stacking
  • Voting classifiers
  • Blending
2.5

Model Evaluation & Selection

  • Confusion matrix
  • Precision, Recall, F1-score
  • ROC-AUC curve
  • Mean Squared Error (MSE), RMSE, MAE
  • R-squared
  • Hyperparameter tuning (Grid Search, Random Search)
  • Bayesian Optimization
3.1

Neural Networks Fundamentals

  • Perceptrons
  • Multi-layer Perceptrons (MLP)
  • Activation functions (ReLU, Sigmoid, Tanh, Softmax, Leaky ReLU, ELU, GELU, Swish)
  • Forward propagation
  • Backpropagation
  • Loss functions (Cross-entropy, MSE, Hinge loss)
  • Gradient descent variants (SGD, Adam, RMSprop, AdaGrad, Momentum)
  • Batch normalization
  • Layer normalization
  • Dropout and regularization
  • Weight initialization techniques
3.2

Convolutional Neural Networks (CNN)

  • Convolution operations
  • Pooling layers (Max, Average, Global)

CNN Architectures

  • LeNet
  • AlexNet
  • VGGNet
  • ResNet (Residual Networks)
  • Inception (GoogLeNet)
  • MobileNet
  • EfficientNet
  • DenseNet
  • Transfer learning
  • Data augmentation

Applications

  • Object detection (YOLO, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN)
  • Semantic segmentation (U-Net, FCN, SegNet, DeepLab)
  • Instance segmentation
3.3

Recurrent Neural Networks (RNN)

  • Simple RNN
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Unit (GRU)
  • Bidirectional RNN
  • Encoder-Decoder architectures
  • Sequence-to-Sequence models
  • Attention mechanism
  • Time series forecasting
3.4

Transformer Architecture

  • Self-attention mechanism
  • Multi-head attention
  • Positional encoding
  • Transformer encoder-decoder
  • BERT (Bidirectional Encoder Representations)
  • GPT (Generative Pre-trained Transformer)
  • T5 (Text-to-Text Transfer Transformer)
  • Vision Transformers (ViT)
  • CLIP (Contrastive Language-Image Pre-training)
3.5

Generative Models

GANs

  • Vanilla GAN
  • DCGAN
  • Conditional GAN (cGAN)
  • StyleGAN, StyleGAN2, StyleGAN3
  • CycleGAN
  • Pix2Pix
  • Progressive GAN

VAE & Diffusion

  • Variational Autoencoders (VAE)
  • Denoising Diffusion Probabilistic Models (DDPM)
  • Stable Diffusion
  • DALL-E
  • Midjourney architecture concepts
  • Flow-based models
  • Energy-based models
3.6

Advanced Deep Learning Techniques

  • Neural Architecture Search (NAS)
  • Meta-learning
  • Few-shot learning
  • Zero-shot learning
  • Contrastive learning
  • Self-supervised learning
  • Knowledge distillation
  • Pruning and quantization
  • Model compression
4.1

Text Preprocessing

  • Tokenization
  • Stemming and lemmatization
  • Stop word removal
  • Text normalization
  • Regular expressions
4.2

Text Representation

  • Bag of Words (BoW)
  • TF-IDF
  • Word embeddings (Word2Vec, GloVe, FastText)
  • Contextualized embeddings (ELMo, BERT)
  • Sentence embeddings
4.3

NLP Tasks & Algorithms

  • Text classification
  • Named Entity Recognition (NER)
  • Part-of-Speech (POS) tagging
  • Sentiment analysis
  • Machine translation
  • Text summarization (extractive, abstractive)
  • Question answering
  • Language modeling
  • Text generation
  • Information extraction
  • Coreference resolution
  • Dependency parsing
4.4

Advanced NLP Models

  • BERT and variants (RoBERTa, ALBERT, DistilBERT)
  • GPT series (GPT-2, GPT-3, GPT-4)
  • XLNet
  • ELECTRA
  • LLaMA
  • Mistral
  • Claude architecture concepts
  • Prompt engineering
  • Fine-tuning strategies
  • Retrieval-Augmented Generation (RAG)
5.1

Image Processing Fundamentals

  • Image representation
  • Color spaces (RGB, HSV, LAB)
  • Filtering and convolution
  • Edge detection (Sobel, Canny)
  • Morphological operations
  • Image transformations
5.2

Computer Vision Tasks

  • Image classification
  • Object detection
  • Object tracking
  • Semantic segmentation
  • Instance segmentation
  • Panoptic segmentation
  • Pose estimation
  • Facial recognition
  • Image captioning
  • Visual question answering
  • Optical Character Recognition (OCR)
  • Image super-resolution
  • Style transfer
  • Image inpainting
  • Depth estimation
5.3

Vision Models & Architectures

  • YOLO (v1-v8)
  • Faster R-CNN family
  • RetinaNet
  • EfficientDet
  • DETR (Detection Transformer)
  • SAM (Segment Anything Model)
  • CLIP
  • Vision Transformers
6.1

RL Fundamentals

  • Markov Decision Processes (MDP)
  • States, actions, rewards
  • Policy and value functions
  • Bellman equations
  • Exploration vs exploitation
6.2

RL Algorithms

Model-Free Methods

  • Q-Learning
  • SARSA
  • Deep Q-Networks (DQN)
  • Double DQN
  • Dueling DQN
  • Policy Gradients
  • REINFORCE
  • Actor-Critic methods
  • A2C (Advantage Actor-Critic)
  • A3C (Asynchronous Actor-Critic)
  • PPO (Proximal Policy Optimization)
  • TRPO (Trust Region Policy Optimization)
  • DDPG (Deep Deterministic Policy Gradient)
  • TD3 (Twin Delayed DDPG)
  • SAC (Soft Actor-Critic)

Model-Based Methods

  • Monte Carlo Tree Search (MCTS)
  • AlphaZero
  • MuZero
  • World models
6.3

Advanced RL Topics

  • Multi-agent RL
  • Inverse RL
  • Imitation learning
  • Hierarchical RL
  • Meta-RL
  • Offline RL
7.1

Graph Neural Networks

  • Graph Convolutional Networks (GCN)
  • GraphSAGE
  • Graph Attention Networks (GAT)
  • Message Passing Neural Networks
  • Graph autoencoders
  • Applications: social networks, molecular chemistry
7.2

Time Series Analysis

  • ARIMA, SARIMA
  • Prophet
  • LSTM for time series
  • Temporal Convolutional Networks (TCN)
  • TimeGAN
  • Attention-based models for time series
7.3

Recommender Systems

  • Collaborative filtering
  • Content-based filtering
  • Matrix factorization
  • Neural collaborative filtering
  • Deep learning for recommendations
  • Context-aware recommendations
7.4

Speech & Audio Processing

  • Speech recognition (ASR)
  • Text-to-Speech (TTS)
  • Speaker recognition
  • Audio classification
  • Music generation
  • Whisper (OpenAI)
  • Wav2Vec
7.5

Multi-modal AI

  • Vision-Language models
  • Audio-Visual learning
  • CLIP, ALIGN
  • Flamingo
  • GPT-4V (Vision)
  • Gemini (multimodal)
7.6

Edge AI & Optimization

  • Model quantization
  • Pruning techniques
  • Knowledge distillation
  • TensorFlow Lite
8.1

ML Pipeline Development

  • Data collection and storage
  • Data versioning
  • Feature stores
  • Model training pipelines
  • Experiment tracking
  • Model versioning
8.2

Model Deployment

  • REST APIs (Flask, FastAPI)
  • Model serving (TensorFlow Serving, TorchServe)
  • Containerization (Docker)
  • Orchestration (Kubernetes)
  • Serverless deployment
  • Edge deployment
8.3

MLOps Tools & Practices

  • Version control (Git, DVC)
  • Experiment tracking (MLflow, Weights & Biases, Neptune)
  • Pipeline orchestration (Airflow, Kubeflow, Prefect)
  • Model monitoring
  • A/B testing
  • CI/CD for ML
  • Feature engineering automation
8.4

Cloud Platforms

  • AWS (SageMaker, EC2, S3, Lambda)
  • Google Cloud (Vertex AI, Cloud ML)
  • Azure (Azure ML)
◆

Deep Learning Frameworks

  • TensorFlow / Keras
  • PyTorch / Lightning
  • JAX / Flax
  • MXNet
  • Caffe
  • ONNX
◆

Machine Learning Libraries

  • Scikit-learn
  • XGBoost
  • LightGBM
  • CatBoost
  • H2O.ai
  • PyCaret
◆

NLP Tools

  • Hugging Face Transformers
  • spaCy
  • NLTK
  • Gensim
  • AllenNLP
  • Flair
  • LangChain
  • LlamaIndex
◆

Computer Vision Tools

  • OpenCV
  • Pillow
  • Albumentations
  • imgaug
  • Detectron2
  • MMDetection
  • YOLO implementations (Ultralytics)
◆

Data Processing

  • Pandas
  • NumPy
  • Dask
  • Polars
  • Apache Spark (PySpark)
  • Rapids (GPU acceleration)
◆

Visualization

  • Matplotlib
  • Seaborn
  • Streamlit
  • Gradio
◆

RL Frameworks

  • OpenAI Gym
  • Stable Baselines3
  • RLlib (Ray)
  • Dopamine
  • TF-Agents
◆

AutoML Tools

  • Auto-sklearn
  • TPOT
  • AutoKeras
  • H2O AutoML
  • Google AutoML
◆

MLOps & Experiment Tracking

  • MLflow
  • Weights & Biases
  • Neptune.ai
  • Comet.ml
  • TensorBoard
  • DVC (Data Version Control)
  • Kubeflow
◆

Development Environments

  • Jupyter Notebook / JupyterLab
  • Google Colab
  • Kaggle Notebooks
  • VS Code with extensions
  • PyCharm
✦

Large Language Models (LLMs)

  • GPT-4 Turbo and GPT-4o (multimodal capabilities)
  • Claude 4 (Opus, Sonnet) - extended context windows
  • Gemini 1.5 Pro - 1M+ token context window
  • LLaMA 3 - open-source improvements
  • Mistral Large and Mixtral MoE
  • Command R+ by Cohere
  • Phi-3 by Microsoft (small language models)
✦

Generative AI

  • Sora - OpenAI's text-to-video model
  • Stable Diffusion 3 - improved image generation
  • DALL-E 3 - enhanced prompt following
  • Midjourney V6 - photorealistic generation
  • Runway Gen-2 - video generation
  • Pika - video generation from text
  • Google Imagen 2 and Gemini Imagen
✦

Multimodal AI

  • GPT-4V - vision capabilities in GPT-4
  • Gemini - native multimodal understanding
  • Claude 3 with vision
  • LLaVA - open-source vision-language models
  • Qwen-VL - visual language understanding
✦

AI Agents & Reasoning

  • AutoGPT and autonomous agents
  • LangChain and LangGraph for agent orchestration
  • CrewAI for multi-agent systems
  • Chain-of-Thought prompting
  • Tree of Thoughts reasoning
  • ReAct (Reasoning and Acting)
✦

Efficient AI

  • Mixture of Experts (MoE) architectures
  • LoRA and QLoRA for efficient fine-tuning
  • FlashAttention-2 for efficient transformers
  • Quantization techniques (INT8, INT4)
  • Speculative decoding for faster inference
✦

Open Source Breakthroughs

  • Meta's LLaMA series democratizing LLMs
  • Falcon models
  • MPT (MosaicML)
  • Stable LM
  • Open Assistant
  • Vicuna, Alpaca instruction-tuned models
✦

AI Safety & Alignment

  • Constitutional AI
  • RLHF (Reinforcement Learning from Human Feedback)
  • Red teaming techniques
  • Adversarial robustness
  • Interpretability tools (LIME, SHAP, Integrated Gradients)
✦

Computer Vision Advances

  • SAM (Segment Anything Model) - universal segmentation
  • DINO v2 - self-supervised vision transformers
  • YOLOv9 and YOLOv10 improvements
  • RT-DETR real-time detection transformer
  • DINOv2 for visual features
✦

Edge AI & Hardware

  • Apple M-series with Neural Engine
  • Qualcomm AI Engine
  • Google TPU v5
  • NVIDIA H100 GPUs
  • Groq LPU for inference
  • Cerebras wafer-scale engine
✦

Emerging Trends

  • Retrieval-Augmented Generation (RAG) systems
  • Vector databases (Pinecone, Weaviate, ChromaDB)
  • Synthetic data generation
  • AI-powered code generation (GitHub Copilot, Cursor)
  • Neuromorphic computing
  • Quantum machine learning
★

Beginner Projects (Weeks 1-8)

1. Iris Flower Classification

Use K-NN or Decision Trees. Focus: Data preprocessing, visualization, basic ML

2. House Price Prediction

Linear/polynomial regression. Focus: Feature engineering, regression metrics

3. Email Spam Detector

Naive Bayes or Logistic Regression. Focus: Text preprocessing, classification

4. Handwritten Digit Recognition (MNIST)

Basic neural network with Keras/PyTorch. Focus: Introduction to deep learning

5. Customer Segmentation

K-Means clustering. Focus: Unsupervised learning, visualization

6. Titanic Survival Prediction

Random Forest or XGBoost. Focus: Handling missing data, feature engineering

7. Movie Recommendation System

Collaborative filtering basics. Focus: Recommendation algorithms

8. Sentiment Analysis on Product Reviews

Bag of Words + Logistic Regression. Focus: NLP basics, text classification

★

Intermediate Projects (Months 3-6)

9. Image Classification with CNN

CIFAR-10 or custom dataset. Focus: CNN architecture, transfer learning

10. Chatbot with Intent Classification

Use BERT for intent recognition. Focus: Transformers, dialogue systems

11. Object Detection System

YOLO or Faster R-CNN. Focus: Computer vision, real-time detection

12. Time Series Forecasting

Stock price or weather prediction with LSTM. Focus: Sequential data, RNN variants

13. Face Recognition System

Use pre-trained models (FaceNet, ArcFace). Focus: Embedding learning, similarity metrics

14. Text Summarization Tool

Extractive and abstractive methods. Focus: NLP, sequence-to-sequence models

15. Music Genre Classification

Audio signal processing + CNN. Focus: Audio analysis, spectrograms

16. Style Transfer Application

Neural style transfer. Focus: CNNs for artistic applications

17. Fake News Detector

BERT fine-tuning. Focus: Advanced NLP, classification

18. Pose Estimation for Fitness App

OpenPose or MediaPipe. Focus: Human pose estimation

★

Advanced Projects (Months 7-12)

19. Build Your Own ChatGPT Clone

Fine-tune GPT-2 or use LLaMA. Focus: LLMs, prompt engineering, deployment

20. Autonomous Driving Simulation

Lane detection, object tracking with RL. Focus: Computer vision + RL integration

21. Medical Image Segmentation

U-Net for tumor detection. Focus: Semantic segmentation, healthcare AI

22. Real-time Translator

Sequence-to-sequence with attention. Focus: Machine translation, deployment

23. Generate Art with GANs

StyleGAN2 or implement custom GAN. Focus: Generative models, training stability

24. Question Answering System

BERT for SQuAD-style QA. Focus: Reading comprehension, extractive QA

25. Video Action Recognition

3D CNNs or two-stream networks. Focus: Video understanding, temporal modeling

26. AlphaZero-style Game AI

Implement for Chess or Go. Focus: Reinforcement learning, MCTS

27. Document Understanding System

Layout analysis + OCR + NER. Focus: Multi-modal document AI

28. Voice Cloning Application

Tacotron 2 or similar TTS. Focus: Speech synthesis, audio processing

29. 3D Object Detection for Robotics

PointNet++ on LIDAR data. Focus: 3D vision, point clouds

30. Multimodal Search Engine

CLIP-based image-text search. Focus: Multimodal learning, embeddings

★

Expert Projects (12+ months)

31. Build a RAG System from Scratch

Custom retrieval + LLM integration. Focus: Vector databases, prompt engineering, full-stack AI

32. Develop Custom LLM

Train smaller model (1-7B parameters). Focus: Pre-training, distributed training, optimization

33. Real-time Deepfake Detector

Multi-model ensemble approach. Focus: Adversarial examples, media forensics

34. Neural Architecture Search System

Implement NAS for custom tasks. Focus: AutoML, meta-learning

35. AI Research Paper Implementation

Reproduce state-of-the-art results. Focus: Research skills, experimentation

36. Production ML System with MLOps

End-to-end pipeline with monitoring. Focus: MLOps, scalability, CI/CD

37. Multi-Agent RL Environment

Cooperative/competitive agents. Focus: Advanced RL, emergent behavior

38. Custom Diffusion Model

Implement DDPM for specific domain. Focus: Generative models, sampling techniques

39. Federated Learning System

Privacy-preserving ML. Focus: Distributed learning, security

40. AI Chip Design Optimizer

Use RL to optimize neural network architectures for hardware. Focus: Hardware-software co-design, efficiency

📚

Online Courses

🎓Foundational Courses

Andrew Ng's Machine Learning (Coursera) Deep Learning Specialization (Coursera) Fast.ai Practical Deep Learning CS229: Machine Learning (Stanford) CS231n: CNNs for Visual Recognition (Stanford) CS224n: NLP with Deep Learning (Stanford) Hugging Face NLP Course
📚

Books

  • "Hands-On Machine Learning" by Aurélien Geron
  • "Deep Learning" by Goodfellow, Bengio, Courville
  • "Pattern Recognition and Machine Learning" by Bishop
  • "Reinforcement Learning" by Sutton and Barto
  • "Speech and Language Processing" by Jurafsky and Martin
📚

Practice Platforms

  • Kaggle
  • LeetCode (for algorithms)
  • Papers with Code
  • GitHub
  • ArXiv (research papers)
📚

Communities

  • Reddit: r/MachineLearning, r/learnmachinelearning
  • Discord servers (Hugging Face, Fast.ai)
  • Twitter AI community
  • LinkedIn groups
  • Local AI meetups
📚

Tips for Success

  1. Build projects while learning - don't just consume content
  2. Read research papers regularly from ArXiv
  3. Participate in Kaggle competitions
  4. Contribute to open-source projects
  5. Document your learning through blogs or GitHub
  6. Network with the AI community
  7. Stay updated with latest research and tools
  8. Focus on fundamentals before chasing trends
  9. Practice coding daily
  10. Don't get overwhelmed - take it step by step