Artificial Intelligence & Data Science

Python, Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI — applied, not theoretical.

8 Months · Intermediate to Advanced · Live Online

Most AI courses stop at fit() and predict(). This one goes from raw data to a model someone else can actually call.

About this course

A full path from Python and statistics through classical ML, deep learning, NLP, computer vision, and generative AI — every module anchored to a dataset and a working model, not slides.

What you'll build

An end-to-end ML pipeline with a real deployment step, an NLP classifier trained on real text, a CNN-based image classifier, and a generative AI capstone project using a production LLM API.

What's covered

• Python for data science: NumPy, Pandas, data wrangling
• Statistics and probability fundamentals for ML
• Data cleaning, exploratory data analysis, and feature engineering
• Regression models: linear, logistic, regularization
• Classification models: decision trees, random forests, SVMs, boosting
• Clustering and unsupervised learning (k-means, hierarchical)
• Model evaluation, cross-validation, and hyperparameter tuning
• Neural network fundamentals and backpropagation
• Deep learning with PyTorch: building and training networks
• CNNs and computer vision fundamentals, transfer learning
• NLP: tokenization, embeddings, and transformer architectures
• Working with pretrained transformer models
• Generative AI: prompt engineering, retrieval-augmented generation (RAG)
• LLM evaluation and safety/guardrail basics
• Model deployment fundamentals: serving a model behind an API

Curriculum

• Week 1: Python for data science: NumPy and Pandas fundamentals
• Week 2: Data cleaning, exploratory data analysis, and visualization
• Week 3: Statistics and probability fundamentals for ML
• Week 4: Feature engineering and regression models: linear, logistic, regularization
• Week 5: Classification models: decision trees, random forests, SVMs
• Week 6: Boosting algorithms (XGBoost, LightGBM) and ensemble methods
• Week 7: Clustering and unsupervised learning: k-means, hierarchical, PCA
• Week 8: Model evaluation, cross-validation, and hyperparameter tuning
• Week 9: Project: end-to-end ML pipeline from raw data to deployed model
• Week 10: Neural network fundamentals: perceptrons, backpropagation, activation functions
• Week 11: Deep learning with PyTorch: building and training networks
• Week 12: CNNs and computer vision fundamentals
• Week 13: Transfer learning and data augmentation for vision tasks
• Week 14: Project: computer vision image classifier
• Week 15: NLP fundamentals: tokenization, embeddings, sequence models
• Week 16: Transformers and pretrained models: attention, fine-tuning basics
• Week 17: Project: NLP sentiment classifier
• Week 18: Generative AI: prompt engineering fundamentals
• Week 19: Retrieval-augmented generation (RAG) and vector search
• Week 20: LLM evaluation, guardrails, and safety basics
• Week 21: Model deployment fundamentals: serving models behind an API
• Week 22: Capstone project: architecture and build
• Week 23: Capstone project: evaluation and deployment
• Week 24: Capstone review, portfolio prep, and interview preparation

Projects you'll build

• End-to-end ML pipeline — From raw dataset to a deployed model with a REST API front end, including evaluation and monitoring basics.
• NLP sentiment classifier — Trained and fine-tuned on real text data, deployed behind a simple API.
• Computer vision image classifier — CNN-based classifier trained on a real image dataset, with data augmentation and transfer learning.
• Generative AI capstone — Built using a pretrained LLM via API, with prompt engineering, retrieval-augmented generation, and evaluation.

Who this is for

Developers and analysts who want to move into applied AI/ML roles, with enough math and Python fundamentals to keep up (or willingness to build them in the first month).

Why this one's different

Every model you build gets deployed behind an API at least once — the point is knowing what happens after fit(), not just during it.

Pricing

• STANDARD: ₹59,999
• PREMIUM: ₹89,999
• INTERNSHIP: ₹9,999

FAQs

Do I need a math background?

Basic statistics helps. We cover the probability and stats fundamentals needed for the ML modules in week 3.

Is this the same as the Generative AI-only track?

No — this is the full 8-month AI & Data Science path. Agentic AI is a separate, shorter advanced course for those who've already got ML/DL fundamentals.

Do I need a GPU?

No — training is done on cloud notebooks provided as part of the course. A laptop that can run a browser is enough.

Will this cover LLM fine-tuning?

We cover prompt engineering and RAG in depth. Fine-tuning is introduced conceptually; deep fine-tuning work is covered in the Agentic AI track.