Python basic data structures, Lists, Tuples, Sets, Dictionaries, Functions, and Loops

Control Structures, File Handling, Comprehensions OOPs, Generators, and Libraries

Excel based (Importing, Grouping, Pivots), SQL based (Aggregation), Git Fundamentals, Collaboration and Version Control

Python for data science (Numpy, Pandas, Matplotlib, SciPy, Scikit-learn, etc.)

Data cleaning, feature selection, and normalization

Hands-on exercises, case studies, and discussions

Linear regression and evaluation metrics

Multiple, Polynomial, Overfitting, solution to Over Fitting

Evaluation metrics

Logistic regression, Decision tree, Random Forest, SVM, Model Deployment basics (store, load, predict)

Basics, distance matrix and applications

How to implement clustering (Agglomerative clustering) and connect with business requirements, Algorithms (PCA, CFA), Association Rule Mining, DB Scan, and Anomaly Detection (Nearest Neighbor and Isolation Forest)

Ensemble technique with examples (its difference from supervised and unsupervised learning); types (Bagging and boosting )

Bagging and boosting and different algorithms; Libraries (Adaboost, GB, XGB, Catboost)

Types of timeseries data, AR and MA Modelling

ARIMA, FB Prophet, and implement data

Cross-validation, neural networks, activation functions, and DL frameworks

Cross-validation, neural network coding, and applications

Perceptrons, math behind perceptrons, and Python implementation

Introduction to MLPs, forward propagation, Python implementation, Introduction, math derivation, and Python implementation

Introduction to optimisers, activation functions, loss functions, Overfitting scenario

Best practices in choosing optimisers, activation functions, loss function, batch normalization and dropout technique

Convolution Neural Network (Filters - Feature Detectors, Pooling - Avg, Max, Padding and Stride); Basic Architecture

Pre-trained networks, transfer learning, and fine tuning

Recurrent Neural Networks (Temporal Nature of Data, Recurrent Mechanism, Types of RNN), LSTM Gates, and GRU Gates

Applications, Drawbacks of RNN, LSTM and its drawbacks, GRU. Attention Mechanism, and Transformers

Autoencoders, DBN, and RBM

Applications of networks for various use cases

Introduction to NLP, Text Preprocessing, Text Tokenization, and Word Embeddings

Text Classification, Use of Sequence models (RNN, LSTM, GRU), NER, Information Extraction, and Machine Translation

VAE, GAN, Architecture, training process of generator and discriminator, DCGAN, WGAN and other GANs—introduction and sequence generation

Implementation and application

Attention mechanism; transformer architecture; BERT (Bidirectional Encoder Representations from Transformers); ViLBERT; GPT (Generative Pre-trained Transformer) and applications of transformer models

Applications of BERT /VilBERT and transformer models

Applications of Generative AI in different domains

Examples of prompt engineering, fine tuning, API creation, and integration

Fine-tuning, transfer learning, prompt engineering, applications, other LLMs, RAG architecture, frameworks for RAG implementation, and building RAG based apps

Prompt engineering, vector databases (FAISS, Chroma Db), and building applications based on RAG

Introduction to Agentic AI, core concepts in LLM-powered Agentic AI - agent architecture

Hands-on exercises

Markov Decision Processes (MDPs); Q-Learning and Deep Q Networks (DQN); Actor-Critic models; exploration vs. exploitation strategies

Focus on applications of reinforcement learning

The capstone project is a comprehensive, real-world assignment in which participants apply their knowledge and skills to solve industry-specific problems

It integrates concepts from their coursework, encouraging critical thinking and innovation

Capstone projects help participants gain hands-on experience, making them industry-ready by demonstrating their ability to tackle complex challenges in a professional setting