Module 1: Introduction to Python

• Introduction to programming languages
• Why Python?
• Python use cases and career scope
• Installing Python and IDE setup
• Writing and running Python programs

Module 2: Python Basics

• Variables and data types
• Input and output operations
• Operators and expressions
• Keywords and identifiers
• Type conversion

Module 3: Control Flow Statements

• Conditional statements (if, elif, else)
• Looping statements (for, while)
• Break, continue, and pass
• Nested loops

Module 4: Data Structures in Python

• Strings and string operations
• Lists and list methods
• Tuples and tuple operations
• Sets and set methods
• Dictionaries and dictionary operations

Module 5: Functions & Modules

• Defining and calling functions
• Function arguments and return values
• Lambda functions
• Built-in functions
• Creating and importing modules
• Packages

Module 6: Object-Oriented Programming (OOP)

• Classes and objects
• Constructors
• Instance and class variables
• Inheritance
• Polymorphism
• Encapsulation
• Abstraction

Module 7: Exception Handling

• Types of errors
• Try, except, else, finally
• Custom exceptions
• Best practices for error handling

Module 8: File Handling

• Reading and writing files
• File modes
• Working with CSV and text files
• File handling use cases

Module 9: Python Libraries & Utilities

• Math and random modules
• Date and time module
• OS and sys modules
• Regular expressions (regex)

Module 10: Python for Automation (Basics)

• Automating simple tasks
• Working with scripts
• Introduction to automation use cases

Module 11: Mini Project

• Problem statement discussion
• Logic building
• Python-based mini project
• Code review and optimization

Module 12: Interview & Placement Preparation

• Python interview questions
• Coding practice sessions
• Resume preparation
• Mock interviews
• Career guidance

Module 1: Python Ecosystem for AI & ML

• Python environment setup for AI development
• Virtual environments (venv, Conda)
• Jupyter Notebook & Google Colab for ML workflows
• Writing efficient, reusable Python code for ML projects

Module 2: Data Handling & Numerical Computing

• NumPy arrays, broadcasting, vectorization
• Linear algebra basics for ML (matrices, vectors)
• Random number generation & sampling
• Performance optimization using NumPy

Module 3: Data Analysis & Manipulation

• Pandas Series & DataFrames
• Data cleaning & preprocessing techniques
• Handling missing values & outliers
• Feature selection & transformation
• Time-series data handling
  

Module 4: Data Visualization for ML

• Data visualization using Matplotlib & Seaborn
• Statistical plots for data understanding
• Visualizing distributions, correlations & trends
• Visualization for model insights

Module 5: Statistics & Probability for Machine Learning

• Descriptive statistics
• Probability distributions
• Sampling techniques
• Hypothesis testing concepts
• Correlation & covariance

Module 6: Feature Engineering & Data Preparation

• Encoding categorical variables
• Feature scaling & normalization
• Dimensionality reduction concepts
• Data pipelines for ML workflows

Module 7: Python for Machine Learning Workflows

• Introduction to Scikit-learn
• Building ML pipelines
• Train-test split & cross-validation
• Model evaluation metrics
• Saving & loading models

Module 8: Python for Deep Learning Foundations

• Introduction to TensorFlow & PyTorch
• Tensors and computational graphs
• Data loaders & batching
• Training loops & optimization basics

Module 9: Python for Generative AI & LLMs

• Working with APIs (OpenAI / Hugging Face)
• Prompt engineering using Python
• Text generation & embeddings
• Image generation workflows
• Tokenization & vector databases basics
  

Module 10: Automation & AI Pipelines

• Automating ML workflows using Python
• Model inference pipelines
• Working with JSON, REST APIs
• Logging & monitoring basics

Module 11: Mini Projects (Hands-On)

• Data analysis & visualization project
• ML preprocessing pipeline project
• LLM-based text generation application
• Generative AI automation script

Module 1: Introduction to Machine Learning

• Definition and types of ML: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
• Role of ML in AI and Generative AI
• ML workflow: Data → Features → Model → Evaluation → Deployment
• Applications of ML across industries (Healthcare, Finance, NLP, Vision)

Module 2: Supervised Learning

• Regression Algorithms
  • Linear Regression, Polynomial Regression
  • Regularization: Ridge, Lasso, ElasticNet
  • Evaluation metrics: MSE, RMSE, R², MAE

     

• Classification Algorithms
  • Logistic Regression, K-Nearest Neighbors (KNN)
  • Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM)
  • Support Vector Machines (SVM)
  • Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC

     

• Practical Hands-On
  • Predicting housing prices (regression)
  • Classifying emails as spam/non-spam (classification)
    

Module 3: Unsupervised Learning

• Clustering Techniques
  • K-Means, Hierarchical Clustering, DBSCAN
  • Silhouette score & elbow method

     

• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE, UMAP for visualization

     

• Anomaly Detection
  • Isolation Forest, One-Class SVM

     

• Hands-On Projects
  • Customer segmentation using clustering
  • Fraud detection using anomaly detection
    

Module 4: Feature Engineering & Data Preprocessing

• Handling missing data and outliers
• Encoding categorical variables
• Feature scaling: Standardization, Normalization
• Feature selection techniques
• Creating ML pipelines for repeatable workflows

Module 5: Model Evaluation & Optimization

• Train-Test split, K-Fold Cross Validation
• Hyperparameter tuning: Grid Search, Random Search
• Regularization techniques to prevent overfitting
• Bias-Variance tradeoff and model selection
• Model interpretation with SHAP and LIME

Module 6: Ensemble Learning

• Bagging & Boosting techniques
• Random Forest, AdaBoost, Gradient Boosting
• XGBoost, LightGBM, CatBoost practical applications
• Voting and stacking ensemble methods
  

Module 7: Introduction to Neural Networks

• Perceptron and multilayer neural networks
• Activation functions: Sigmoid, ReLU, Softmax
• Forward propagation and backpropagation
• Training neural networks using TensorFlow & PyTorch

Module 8: Advanced Machine Learning Techniques

• Support Vector Machines (SVM) in-depth
• Kernel methods
• Time-series forecasting using ARIMA & LSTM
• Natural Language Processing (NLP) basics for AI
• Reinforcement learning overview

Module 9: Machine Learning for Generative AI

• Role of ML in Generative AI
• Dimensionality reduction for generative models
• Feature representation for LLMs and GANs
• Preparing datasets for text, image, and audio generation
• Introduction to prompt engineering and embeddings

   

Module 1: Introduction to Deep Learning

• What is Deep Learning and its importance in AI
• Difference between ML and DL
• Applications of Deep Learning: Computer Vision, NLP, Speech Recognition, Generative AI
• Overview of Deep Learning frameworks: TensorFlow & PyTorch
• GPU vs CPU for training

Module 2: Neural Network Fundamentals

• Perceptron and Multilayer Perceptrons (MLP)
• Activation functions: Sigmoid, ReLU, Tanh, Softmax
• Forward propagation & backpropagation
• Loss functions: Mean Squared Error, Cross-Entropy Loss
• Gradient descent and optimization basics
  

Module 3: Deep Learning Model Training

• Data preparation for Deep Learning
• Batch training, mini-batch gradient descent
• Learning rate, momentum, and optimizers (SGD, Adam, RMSProp)
• Overfitting and regularization: Dropout, L1/L2 Regularization
• Early stopping and checkpointing

Module 4: Convolutional Neural Networks (CNN)

• CNN architecture and concepts: Convolution, Pooling, Flattening
• Building CNNs for image classification
• Transfer learning: Pretrained models (VGG, ResNet, Inception)
• Data augmentation and image preprocessing
• Hands-on project: Image classification with CIFAR-10 / MNIST

Module 5: Recurrent Neural Networks (RNN)

• Sequence data and time-series basics
• RNN architecture and limitations
• LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) networks
• Applications in NLP, forecasting, and sequential prediction
• Hands-on project: Text sentiment analysis or stock price prediction

Module 6: Advanced Deep Learning Architectures

• Autoencoders for dimensionality reduction & anomaly detection
• Variational Autoencoders (VAE) for generative tasks
• Generative Adversarial Networks (GANs) basics
• Attention mechanism and introduction to Transformers

Module 7: Deep Learning for NLP

• Word embeddings: Word2Vec, GloVe, FastText
• Sequence-to-sequence models
• Transformer architecture: Attention, Encoder-Decoder
• Large Language Models (LLMs) overview
• Hands-on project: Text generation / summarization

Module 8: Deep Learning for Generative AI

• CNNs & GANs for image generation
• VAEs for latent space modeling
• Diffusion models basics
  
• Prompt engineering with LLMs for generative tasks
• Hands-on project: AI-generated text, images, or music

Module 9: Model Evaluation & Deployment

• Evaluating deep learning models (accuracy, F1, BLEU, ROUGE)
• Model saving & loading (HDF5, PyTorch formats)
• Exporting models for production (ONNX, TensorFlow Serving)
• Basic introduction to MLops for Deep Learning

Module 10: Hands-On Projects

• CNN Project: Image classification or object detection
• RNN/LSTM Project: Time-series forecasting or NLP task
• GAN Project: Image or audio generation
• Autoencoder/VAE Project: Anomaly detection / generative modeling

Module 1: Introduction to Generative AI

• What is Generative AI and its industry significance
• Difference between traditional AI and generative models
• Applications of Generative AI in business, art, NLP, and multimodal tasks
• Overview of GANs, VAEs, Diffusion Models, Transformers, and LLMs

Module 2: Data Preparation for Generative AI

• Dataset types for generative modeling (images, text, audio, multimodal)
• Data cleaning, preprocessing, and augmentation
• Feature representation and embeddings
• Tokenization for text, pixel normalization for images, and spectrograms for audio
• Handling large-scale datasets efficiently

Module 3: Generative Adversarial Networks (GANs)

• GAN architecture: Generator vs Discriminator
• Loss functions and training strategy
• Types of GANs: DCGAN, Conditional GAN, StyleGAN
• Challenges in training GANs (mode collapse, instability)
• Hands-on project: Image generation using DCGAN

Module 4: Variational Autoencoders (VAEs)

• Concept of latent space representation
• Encoder-Decoder architecture
• Sampling from latent space for generation
• Applications: Image synthesis, anomaly detection
• Hands-on project: Generating handwritten digits with VAE

Module 5: Diffusion Models

• Introduction to diffusion-based generative modeling
• Forward and reverse diffusion process
• Applications in image synthesis and enhancement
• Hands-on project: Image generation using diffusion model frameworks

Module 6: Transformers and Large Language Models (LLMs)

• Attention mechanism and Transformer architecture
• Encoder, Decoder, and Encoder-Decoder models
• Pretrained LLMs (GPT, BERT, LLaMA) for text generation
• Fine-tuning LLMs for domain-specific tasks
• Hands-on project: Chatbot or text summarization using Hugging Face Transformers

Module 7: Prompt Engineering & LLM Applications

• Fundamentals of prompt engineering
• Optimizing LLM outputs with few-shot and zero-shot learning
• Fine-tuning strategies: LoRA, PEFT
• Use cases: AI chatbots, text completion, code generation
• Hands-on project: Custom domain-specific LLM application

Module 8: Generative AI for Images & Multimedia

• Image generation using GANs, VAEs, and diffusion models
• Text-to-image generation (Stable Diffusion, DALL·E)
• AI for video and audio generation
• Multimodal AI concepts: Combining text, images, and audio
• Hands-on project: Text-to-image generation with Stable Diffusion
  

Module 9: Evaluation & Optimization of Generative Models

• Evaluation metrics for generative models: FID, IS, BLEU, ROUGE
• Model fine-tuning and hyperparameter optimization
• Reducing bias and ethical considerations in generative outputs
• Responsible AI practices for generative systems

Module 10: Deployment of Generative AI Models

• Exporting models for production (ONNX, TorchScript, TensorFlow SavedModel)
• Integration into web & mobile applications
• APIs and cloud deployment (AWS, Azure, GCP)
• Monitoring generative AI outputs in production

Module 11: Hands-On Projects

• GAN Project: Generating realistic images or art
• VAE Project: Latent space exploration for image synthesis
• Diffusion Model Project: High-quality text-to-image generation
• LLM Project: AI chatbot, summarization, or domain-specific assistant
• Multimodal AI Project: Combining text and image generation