About This Program
Transform your career with our flagship AI Engineer Program. Master Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI through 40+ real-world projects. Learn from industry experts and get guaranteed interview opportunities.
What You'll Get
40+ Industry Projects
1:1 Mentorship
Guaranteed Interview Calls
Lifetime Access to Content
Job Placement Support
Program Curriculum
1Python, SQL & Software Engineering Foundations3 Weeks
Key Topics:
Python Syntax, Data Structures and Object-Oriented Design
Type Hints, Iterators, Decorators and Context Managers
NumPy, Pandas and Vectorized Data Processing
SQL Queries, Joins, Aggregations and Window Functions
Git, Virtual Environments, Packaging and Dependency Management
Unit Testing, Debugging, Logging and Clean Code
2Mathematics, Probability & Statistical Reasoning4 Weeks
Key Topics:
Vectors, Matrices, Eigenvalues and Matrix Decomposition
Differential Calculus, Gradients and Optimization
Probability Distributions and Bayesian Reasoning
Sampling, Estimation and Confidence Intervals
Hypothesis Testing and Statistical Significance
Experimental Design, A/B Testing and Power Analysis
3Data Engineering, Exploration & Feature Pipelines3 Weeks
Key Topics:
Data Acquisition from Files, Databases and APIs
Data Cleaning, Missing Values and Outlier Analysis
Exploratory Data Analysis and Statistical Visualization
Relational Modeling, Warehouses and Analytical Data Design
Feature Engineering, Transformation and Leakage Prevention
Reproducible Data Pipelines, Validation and Data Versioning
4Machine Learning Foundations5 Weeks
Key Topics:
Problem Framing, Baselines and Learning Objectives
Linear, Polynomial and Regularized Regression
Logistic Regression, Decision Trees and Support Vector Machines
Preprocessing Pipelines and Categorical Feature Encoding
Train, Validation and Test Set Design
Classification, Regression and Ranking Metrics
5Advanced Machine Learning & Applied Modeling4 Weeks
Key Topics:
Random Forests, Gradient Boosting and XGBoost
Clustering, Dimensionality Reduction and Representation Learning
Time-Series Forecasting and Temporal Validation
Recommendation Systems and Ranking Models
Anomaly Detection and Imbalanced Learning
Hyperparameter Optimization, Calibration and Explainability
6Deep Learning & Neural Network Engineering5 Weeks
Key Topics:
Perceptrons, Backpropagation and Computational Graphs
PyTorch and TensorFlow/Keras Workflows
Optimization, Regularization and Initialization
Convolutional and Recurrent Neural Networks
Attention Mechanisms and Sequence Modeling
Transfer Learning, Distributed Training and Model Compression
7Natural Language Processing & Transformers4 Weeks
Key Topics:
Text Normalization, Tokenization and Linguistic Features
Word, Sentence and Contextual Embeddings
Attention and Transformer Architecture
Encoder Models, BERT and Sequence Classification
Information Extraction, Search and Semantic Similarity
Fine-Tuning, Parameter-Efficient Training and NLP Evaluation
8Computer Vision & Multimodal AI3 Weeks
Key Topics:
Image Processing, Augmentation and Visual Data Pipelines
Image Classification and Transfer Learning
Object Detection with YOLO and Region-Based Models
Semantic and Instance Segmentation
Vision Transformers and Image Embeddings
Vision-Language Models, Video Analytics and Responsible Vision
9Generative AI & Foundation Models3 Weeks
Key Topics:
Foundation Model Architecture, Tokenization and Inference
Prompt Engineering, Structured Outputs and Prompt Templates
Model APIs, Function Calling and Tool Integration
GANs, Variational Autoencoders and Diffusion Models
Supervised Fine-Tuning, LoRA and Quantization
Latency, Throughput, Context and Cost Optimization
10RAG, AI Agents & Intelligent Applications4 Weeks
Key Topics:
Embeddings, Vector Indexes and Vector Databases
Document Ingestion, Chunking and Metadata Design
Hybrid Retrieval, Reranking, Grounding and Citations
Agent Planning, Tool Use and Workflow Orchestration
Memory, State, Human Approval and Failure Recovery
RAG and Agent Evaluation, Tracing and Observability
11AI Evaluation, Safety, Security & Governance3 Weeks
Key Topics:
Evaluation Datasets, Benchmarks and Human Evaluation
Hallucination, Robustness, Fairness and Bias Assessment
Prompt Injection, Data Leakage and Adversarial Testing
Privacy, Copyright, Consent and Sensitive Data Handling
Model Cards, Data Sheets and Decision Documentation
NIST AI RMF, Responsible AI Controls and Risk Reviews
12MLOps, LLMOps & Production Architecture3 Weeks
Key Topics:
Experiment Tracking, Model Registry and Reproducibility
Automated Training, Testing and CI/CD Pipelines
Batch, Real-Time and Streaming Model Serving
Docker, Kubernetes and Cloud AI Deployment
Model, Data and Prompt Monitoring with Drift Detection
Reliability, Rollbacks, Observability and Cost Management
13Faculty-Guided Capstone & Career Readiness4 Weeks
Key Topics:
Problem Selection, Research Review and Success Criteria
Data, Model and System Architecture Proposal
End-to-End AI Product Implementation
Evaluation, Red Teaming and Responsible AI Review
Production Deployment, Monitoring and Technical Documentation
Capstone Demonstration, Architecture Defense and Peer Review
Portfolio Development, ML System Design and Technical Interviews
Course Fee
₹1,20,000/ 12 Months
Limited seats available for next batch.