Deep Learning / AI / ML Specialization

Module 1: Foundations of AI & Machine Learning

Topics

• Introduction to AI, ML & Deep Learning

• Evolution of AI systems

• AI vs ML vs Deep Learning

• Types of learning (Supervised, Unsupervised, Reinforcement)

• Real-world AI applications

• AI system lifecycle

Subtopics

• Key differences between traditional programming and machine learning

• Data-driven vs rule-based systems

• End-to-end ML workflow (data → model → deployment → monitoring)

Module 2: Mathematical Foundations for Machine Learning

Topics

• Linear algebra essentials (vectors, matrices)

• Calculus basics (gradients, derivatives)

• Probability & statistics fundamentals

• Loss functions and optimization concepts

Subtopics

• Matrix operations in ML

• Derivatives in gradient descent

• Mean, variance, distributions

• Cost functions (MSE, Log Loss)

• Optimization techniques

Module 3: Supervised Learning Algorithms

Topics

• Linear regression

• Logistic regression

• k-Nearest Neighbors (KNN)

• Decision trees

• Bias-variance tradeoff

Subtopics

• Regression vs classification problems

• Model assumptions

• Underfitting vs overfitting

• Feature importance

Module 4: Model Evaluation & Validation

Topics

• Train/test split

• Cross-validation

• Precision, recall, F1-score

• ROC-AUC

• Overfitting and underfitting

Subtopics

• Confusion matrix interpretation

• Performance metrics for regression vs classification

• Model generalization

• Learning curves

Hands-on Labs

• Implement a regression model

• Build a classification pipeline

• Evaluate performance metrics

• Visualize learning curves

• Perform hyperparameter tuning exercise

This structure provides a clear progression from foundational theory to practical implementation and evaluation.

Module 5: Ensemble Methods

Topics

• Random Forest

• Gradient Boosting

• XGBoost concepts

• Model stacking

• Trade-offs in ensemble methods

Subtopics

• Bagging vs boosting

• Bias-variance improvements using ensembles

• Tree-based ensemble architectures

• When to use ensemble models vs single models

• Computational cost vs performance gains

Module 6: Unsupervised Learning

Topics

• k-Means clustering

• Hierarchical clustering

• PCA & dimensionality reduction

• Anomaly detection

• Feature embeddings

Subtopics

• Distance metrics in clustering

• Choosing optimal number of clusters (Elbow method, Silhouette score)

• Variance explained in PCA

• Outlier detection techniques

• Representation learning basics

Module 7: Feature Engineering & Data Pipelines

Topics

• Feature scaling & normalization

• Encoding categorical variables

• Handling missing data

• Data leakage prevention

• Feature importance analysis

Subtopics

• Standardization vs Min-Max scaling

• One-hot encoding vs label encoding

• Imputation strategies

• Train-test data separation best practices

• SHAP and feature importance techniques

Module 8: Model Optimization Techniques

Topics

• Regularization (L1, L2)

• Grid search & random search

• Early stopping

• Learning rate strategies

Subtopics

• Preventing overfitting

• Hyperparameter tuning workflows

• Validation-based optimization

• Optimization trade-offs

Hands-on Labs

• Build an ensemble model

• Perform clustering analysis

• Apply PCA on a dataset

• Execute a feature engineering workflow

• Conduct hyperparameter optimization

This module set strengthens practical ML expertise by covering advanced modeling, optimization, and real-world data engineering techniques.

Module 9: Introduction to Neural Networks

Topics

• Perceptron model

• Activation functions

• Forward propagation

• Backpropagation

• Gradient descent optimization

Subtopics

• Linear vs non-linear decision boundaries

• Common activation functions (ReLU, Sigmoid, Tanh, Softmax)

• Loss computation and gradient flow

• Chain rule in backpropagation

• Batch vs stochastic gradient descent

Module 10: Building Deep Neural Networks

Topics

• Multi-layer perceptrons (MLPs)

• Vanishing and exploding gradients

• Weight initialization

• Batch normalization

• Dropout regularization

Subtopics

• Hidden layer architecture design

• Xavier and He initialization

• Gradient stability techniques

• Regularization strategies for deep models

• Training deep networks effectively

Module 11: Convolutional Neural Networks (CNNs)

Topics

• Convolution operations

• Pooling layers

• CNN architectures

• Image classification workflows

• Transfer learning concepts

Subtopics

• Filters, stride, and padding

• Feature maps and hierarchical feature extraction

• Popular CNN design patterns

• Pretrained models and fine-tuning

• Performance optimization techniques

Module 12: Recurrent Neural Networks (RNNs)

Topics

• Sequence modeling

• LSTM & GRU

• Time-series forecasting

• NLP sequence tasks

Subtopics

• Vanishing gradient problem in RNNs

• Gated architectures

• Sequence-to-sequence models

• Applications in text and time-series data

Hands-on Labs

• Build a neural network from scratch

• Implement a CNN for image classification

• Apply transfer learning on a pretrained model

• Build an LSTM model for sequence prediction

• Compare and evaluate model performance

This module sequence provides a comprehensive deep learning foundation covering neural architectures for vision, sequence modeling, and real-world AI applications.

Module 13: Transformers & Attention Mechanism

Topics

• Attention fundamentals

• Self-attention

• Transformer architecture

• Positional encoding

• Pre-trained models overview

Subtopics

• Query, Key, Value (QKV) mechanism

• Scaled dot-product attention

• Multi-head attention

• Encoder–decoder architecture

• Context handling in large sequence models

Module 14: Generative AI & Foundation Models

Topics

• GANs overview

• Autoencoders

• Large Language Models (LLMs)

• Fine-tuning vs prompt engineering

• Ethical considerations

Subtopics

• Generator vs discriminator training dynamics

• Latent space representation

• Instruction tuning and alignment

• Responsible AI principles

• Bias, fairness, and safety considerations

Module 15: Model Scaling & Optimization

Topics

• Distributed training

• GPU/TPU acceleration

• Mixed precision training

• Model compression & quantization

• Performance benchmarking

Subtopics

• Data parallelism vs model parallelism

• Hardware acceleration strategies

• Memory optimization techniques

• Inference optimization

• Throughput vs latency trade-offs

Module 16: AI System Design & Architecture

Topics

• End-to-end ML pipelines

• Data versioning

• Model versioning

• Experiment tracking

• Deployment architectures

Subtopics

• CI/CD for ML systems

• Reproducibility in AI experiments

• Containerization and orchestration

• Real-time vs batch deployment

• Monitoring, logging, and drift detection

Hands-on Labs

• Implement attention mechanism from scratch

• Fine-tune a transformer model

• Optimize a deep learning model for performance

• Deploy a trained model as an API

• Monitor model performance and detect drift

This completes a comprehensive advanced AI curriculum covering transformers, generative AI, optimization strategies, and enterprise AI system design.