AI & Machine Learning
From data wrangling to deploying production ML models — build intelligent systems that solve real problems. No PhD required, just curiosity and Python.
Program Overview
Build Systems That Think
Go beyond theory. Build, train, evaluate, and deploy ML models that solve real business problems.
Why AI/ML Now?
AI is reshaping every industry. Companies are desperate for engineers who can build, deploy, and maintain ML systems — not just run notebooks. This program teaches you to ship production AI, not just prototype it.
- End-to-end ML pipeline — from raw data to deployed model
- Work with LLMs, transformers, and the latest AI tools
- Kaggle competitions for real-world practice
- Deploy models as APIs that companies can actually use
4+
ML Projects
180+
Coding Hours
10+
Algorithms Mastered
6 Mo
Placement Support
Curriculum
From Data to Deployment
A progressive curriculum that builds your ML skills layer by layer — with projects at every stage.
Weeks 1-2
Python Fundamentals & Data Wrangling
Master the data science toolkit and learn to wrangle messy real-world datasets.
- Python programming — data structures, functions, OOP, decorators
- NumPy for numerical computing and array operations
- Data analysis and manipulation with Pandas
- Data visualization with Matplotlib and Seaborn
- Jupyter Notebook workflow and best practices
- Working with real-world datasets — cleaning, handling missing values
Weeks 3-4
Machine Learning with Scikit-learn
Learn classical ML algorithms and how to evaluate model performance rigorously.
- Supervised learning — linear regression, logistic regression, decision trees
- Ensemble methods — Random Forest, Gradient Boosting, XGBoost
- Unsupervised learning — K-Means, DBSCAN, PCA
- Feature engineering and feature selection techniques
- Model evaluation — cross-validation, confusion matrix, ROC curves
- Hyperparameter tuning with GridSearch and Optuna
Weeks 5-7
Deep Learning with TensorFlow & Keras
Build neural networks from scratch and learn transfer learning with industry models.
- Neural network fundamentals — perceptrons, activation functions, backpropagation
- Building CNNs for image classification and object detection
- RNNs, LSTMs, and sequence-to-sequence models
- Transfer learning with pre-trained models (ResNet, BERT)
- Model optimization — regularization, dropout, batch normalization
- GPU training setup and experiment tracking with MLflow
Weeks 8-9
Natural Language Processing
Work with text data, transformers, and build conversational AI systems.
- Text preprocessing — tokenization, stemming, lemmatization
- Word embeddings — Word2Vec, GloVe, FastText
- Transformer architecture and attention mechanisms
- Working with Hugging Face models and pipelines
- Sentiment analysis, text classification, and named entity recognition
- Building conversational AI with LangChain and OpenAI APIs
Weeks 10-12
Model Deployment & Career Prep
Ship your models to production and prepare for ML engineering interviews.
- Building REST APIs for ML models with FastAPI
- Model serialization — pickle, joblib, ONNX
- Containerizing ML services with Docker
- Deployment to AWS (SageMaker, EC2) and Hugging Face Spaces
- Kaggle competition participation and portfolio building
- Mock interviews focused on ML system design questions
Tech Stack
Your AI Toolkit
Industry-standard tools and frameworks used by ML teams at top companies.
Languages & Core
Machine Learning
Deep Learning & NLP
Deployment
Projects
What You'll Build
Production-grade AI applications — not Jupyter notebooks that never leave your laptop.
Sentiment Analyzer
NLP-powered sentiment analysis engine that processes customer reviews, social media posts, and survey responses — with a live dashboard showing trends and insights.
Recommendation Engine
Collaborative and content-based filtering system for product recommendations, built with real e-commerce data. Deployed as an API that returns personalized suggestions.
AI Chatbot with RAG
Intelligent conversational agent using LangChain and OpenAI APIs with retrieval-augmented generation (RAG), custom knowledge bases, and a polished chat interface.
Image Classifier
CNN-based image classification system using transfer learning with ResNet. Trained on custom datasets, deployed as a web service with real-time prediction.
Benefits
What's Included
GPU Cloud Labs
Access to cloud GPU environments for training deep learning models — no expensive hardware required.
Real Datasets
Work with production-scale datasets from industry partners — not clean, toy datasets from textbooks.
Kaggle Profile
Build a Kaggle profile with competition entries and published notebooks that recruiters actively search.
ML Career Support
6 months of placement support targeted at ML engineer, data scientist, and AI developer roles.
Eligibility
Who Is This For?
You don't need a math degree to start. You need a logical mind, Python basics, and the desire to build things that think.
- CS / IT students who want to specialize in AI and data science
- Developers looking to add ML to their existing skill set
- Data analysts ready to move from Excel to Python-powered insights
- Researchers transitioning from academia to industry AI roles
- Anyone with Python basics who wants to break into machine learning
Prerequisites
Basic Python programming. Familiarity with high school math (linear algebra, probability) is helpful but not mandatory.
What You Need
A laptop with 8 GB+ RAM (16 GB recommended), stable internet. GPU access provided via cloud labs.
Outcome
4 deployed ML projects, Kaggle profile with competition entries, and placement support for ML/AI roles.
Support
Learn From Practitioners
ML Mentor
A practicing ML engineer who reviews your models, debugs training issues, and teaches you production-grade practices.
Study Groups
Cohort-based learning with paper reading sessions, Kaggle team competitions, and collaborative model building.
Career Strategy
ML portfolio reviews, Kaggle profile optimization, mock ML system design interviews, and referrals to AI companies.
Start Your AI Journey
Seats are limited to 15 per batch. Contact us for program details, batch schedules, and enrollment.
Contact us for details about fees, batch timings, and scholarship options.
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