PHASE 1: PYTHON & SOFTWARE ENGINEERING (20 Hrs.)

Advanced Python Programming

• Object-Oriented Programming (classes, inheritance, polymorphism)
• Decorators and context managers
• Generators and iterators
• Async/await and concurrent programming
• Memory management and optimization
• Type hints and static typing
• Error handling and exceptions

Software Development Best Practices

• Git version control (branching, merging, rebasing)
• SOLID principles
• Code review practices

Development Environment Setup

• VS Code/PyCharm configuration
• Virtual environments (venv, conda)
• Docker basics
• SSH and remote development

PHASE 2: MATHEMATICS FOR AI (20 Hrs.)

Linear Algebra

• Vectors and matrices operations
• Matrix multiplication and transformations
• Eigenvalues and eigenvectors
• Singular Value Decomposition (SVD)
• Matrix factorization techniques
• Linear transformations visualization
• Applications in dimensionality reduction

Calculus & Optimization

• Derivatives and partial derivatives
• Gradient computation
• Chain rule and backpropagation
• Gradient descent variants (SGD, Momentum, Adam)
• Convex optimization
• Loss function landscapes
• Local vs global minima

Probability & Statistics

• Probability distributions (Normal, Bernoulli, Multinomial)
• Bayes' theorem and Bayesian inference
• Maximum Likelihood Estimation (MLE)
• Expectation and variance
• Hypothesis testing
• Confidence intervals
• Statistical significance

Information Theory

• Entropy and cross-entropy
• KL divergence
• Mutual information
• Information gain
• Applications in loss functions

PHASE 3: DATA ENGINEERING (20 H)

Data Manipulation

• Pandas DataFrames operations
• Data cleaning and preprocessing
• Handling missing values
• Feature engineering techniques
• Time series data handling
• Data aggregation and grouping
• Merging and joining datasets

Data Visualization

• Matplotlib fundamentals
• Seaborn statistical plots
• Plotly interactive visualizations
• Data exploration techniques
• Statistical plots (histograms, box plots, scatter plots)
• Correlation matrices and heatmaps
• Dashboard creation

Big Data Tools

• Apache Spark (PySpark) basics
• Distributed computing concepts
• Dask for parallel computing
• Efficient data formats (Parquet, Arrow)
• SQL optimization
• Working with large datasets
• Memory-efficient processing

Data Pipeline Architecture

• ETL (Extract, Transform, Load) processes
• Apache Airflow for orchestration
• Workflow management
• Data validation with Great Expectations
• Pipeline scheduling and monitoring
• Error handling in pipelines
• Data versioning with DVC

PHASE 4: CLASSICAL MACHINE LEARNING (20 H)

Supervised Learning - Regression

• Linear regression and regularization (Ridge, Lasso, Elastic Net)
• Polynomial regression
• Gradient descent implementation
• Feature scaling and normalization
• Bias-variance tradeoff
• Overfitting and underfitting
• Cross-validation techniques

Supervised Learning - Classification

• Logistic regression
• Decision trees and entropy
• Random Forests
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Support Vector Machines (SVM)
• Naive Bayes
• K-Nearest Neighbors (KNN)

Ensemble Methods

• Bagging and bootstrap aggregating
• Boosting algorithms (AdaBoost, Gradient Boosting)
• Stacking and blending
• Voting classifiers
• Feature importance
• Hyperparameter tuning for ensembles
• When to use each ensemble method

Unsupervised Learning

• K-Means clustering
• Hierarchical clustering
• DBSCAN and density-based clustering
• Gaussian Mixture Models
• Principal Component Analysis (PCA)
• dimensionality reduction

Model Evaluation & Selection

• Train-test-validation split
• Cross-validation strategies (k-fold, stratified, time-series)
• Evaluation metrics (accuracy, precision, recall, F1, ROC-AUC)
• Confusion matrix analysis
• Precision-recall curves
• Imbalanced data handling (SMOTE, class weights)
• Hyperparameter optimization (Grid Search, Random Search, Bayesian Optimization)

Feature Engineering

• Feature creation and extraction
• Feature selection methods
• Encoding categorical variables (One-Hot, Label, Target)
• Feature scaling techniques
• Handling outliers
• Polynomial features
• Domain-specific feature engineering

PHASE 5: DEEP LEARNING FUNDAMENTALS (16 H)

Neural Network Basics

• Perceptron and activation functions
• Forward propagation
• Backpropagation algorithm
• Loss functions (MSE, Cross-Entropy)
• Weight initialization strategies
• Batch, mini-batch, and stochastic gradient descent
• Learning rate scheduling

Training Deep Networks

• Vanishing and exploding gradients
• Batch normalization
• Layer normalization
• Dropout and regularization
• Early stopping
• Learning rate decay
• Gradient clipping

Optimization Algorithms

• SGD with momentum
• RMSprop
• Adam and AdamW
• Learning rate schedulers
• Adaptive learning rates
• Second-order optimization methods
• Optimizer comparison

PyTorch Fundamentals

• Tensors and operations
• Autograd and automatic differentiation
• Building custom models (nn.Module)
• DataLoader and Dataset classes
• Training loops
• Saving and loading models
• GPU acceleration with CUDA

PyTorch Lightning

• Structured training with Lightning
• LightningModule architecture
• Trainer class features
• Callbacks and logging
• Multi-GPU training
• Hyperparameter tuning
• Best practices for organized code

PHASE 6: COMPUTER VISION (16 H)

Convolutional Neural Networks (CNN)

• Convolution operation
• Pooling layers (Max, Average)
• Padding and stride
• Receptive fields
• CNN architectures overview
• Feature maps visualization
• Transfer learning concepts

CNN Architectures

• LeNet and AlexNet (historical context)
• VGG networks
• ResNet and skip connections
• Inception networks
• MobileNet for mobile devices
• Vision Transformers (ViT)

Image Classification

• Multi-class classification
• Fine-tuning pre-trained models
• Data augmentation techniques
• Transfer learning strategies
• Handling imbalanced datasets
• Model evaluation for images
• Deployment considerations

Object Detection

• Bounding box prediction
• YOLO (You Only Look Once) family
• Faster R-CNN
• SSD (Single Shot Detector)
• DETR (Detection Transformer)
• Non-maximum suppression
• Evaluation metrics (mAP, IoU)

Image Segmentation

• Semantic vs instance segmentation
• U-Net architecture
• Mask R-CNN
• DeepLab
• Segmentation loss functions
• Post-processing techniques
• Medical imaging applications

Advanced CV Topics

• GANs for image generation
• Style transfer
• Image super-resolution
• Pose estimation
• Facial recognition
• OCR (Optical Character Recognition)
• Video understanding basics

Computer Vision Tools

• OpenCV for image processing
• Albumentations for augmentation
• Detectron2 for detection
• torchvision and timm libraries
• Roboflow for dataset management
• CVAT for annotation
• Supervision for visualization

PHASE 7: NATURAL LANGUAGE PROCESSING (20 H)

NLP Fundamentals

• Text preprocessing (tokenization, stemming, lemmatization)
• Bag of Words (BoW)
• TF-IDF
• Word embeddings (Word2Vec, GloVe)
• N-grams and language models
• Named Entity Recognition (NER)
• Part-of-speech tagging

Sequence Models

• Recurrent Neural Networks (RNN)
• Long Short-Term Memory (LSTM)
• Gated Recurrent Units (GRU)
• Bidirectional RNNs
• Sequence-to-sequence models
• Attention mechanism
• Encoder-decoder architecture

Transformer Architecture

• Self-attention mechanism
• Multi-head attention
• Positional encoding
• Transformer encoder and decoder
• Layer normalization in transformers
• Feed-forward networks
• Residual connections

BERT and Encoder Models

• BERT pre-training (MLM, NSP)
• Fine-tuning BERT
• RoBERTa improvements
• DistilBERT for efficiency
• ALBERT architecture
• DeBERTa enhancements
• Sentence embeddings

GPT and Decoder Models

• GPT architecture
• Autoregressive language modeling
• GPT-2 and scaling
• GPT-3 and few-shot learning
• Text generation techniques
• Sampling strategies (greedy, beam search, top-k, nucleus)
• Controlling generation

Encoder-Decoder Models

• T5 (Text-to-Text Transfer Transformer)
• BART architecture
• Machine translation
• Text summarization
• Question answering
• Multi-task learning
• Zero-shot and few-shot tasks

NLP Tasks & Applications

• Sentiment analysis
• Text classification
• Named Entity Recognition
• Question answering systems
• Text summarization
• Machine translation
• Chatbots and conversational AI

Tokenization

• Byte-Pair Encoding (BPE)
• WordPiece tokenization
• SentencePiece
• Unigram tokenization
• Creating custom tokenizers
• Subword tokenization benefits
• Vocabulary size considerations

PHASE 8: LARGE LANGUAGE MODELS (18 H)

LLM Fundamentals

• Transformer scaling laws
• Pre-training objectives
• Model architectures (GPT, LLaMA, Mistral, Gemma)
• Context window and attention
• Efficient attention mechanisms (FlashAttention)
• Model size vs performance tradeoffs
• Compute requirements

Model Quantization

• INT8 and INT4 quantization
• GPTQ (GPT Quantization)
• AWQ (Activation-aware Weight Quantization)
• GGUF format
• Post-training quantization
• Quantization-aware training
• Accuracy vs efficiency tradeoffs

Fine-tuning Techniques

• Full fine-tuning
• Supervised Fine-Tuning (SFT)
• Instruction tuning
• Dataset preparation
• Training hyperparameters
• Evaluation during fine-tuning
• Catastrophic forgetting prevention

Parameter-Efficient Fine-Tuning (PEFT)

• LoRA (Low-Rank Adaptation)
• QLoRA (Quantized LoRA)
• Prefix tuning
• Prompt tuning
• Adapter layers
• IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations)
• When to use each PEFT method

Alignment & RLHF

• Reinforcement Learning from Human Feedback
• Reward modeling
• Proximal Policy Optimization (PPO)
• Direct Preference Optimization (DPO)
• Constitutional AI
• Safety alignment
• Value alignment challenges

Prompt Engineering

• Zero-shot prompting
• Few-shot prompting
• Chain-of-Thought (CoT)
• Tree of Thoughts
• ReAct (Reasoning + Acting)
• Self-consistency
• Prompt optimization techniques

PHASE 9: LLM AGENTS & APPLICATIONS (10 H)

LLM Agents Fundamentals

• Agent architecture patterns
• ReAct agent framework
• Plan-and-Execute agents
• Reflexion for self-improvement
• Agent memory systems
• Tool/function calling
• Agent evaluation