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Flagship Program

Artificial Intelligence & Data Science Full Stack

A complete 240-hour path from Python and statistics to Machine Learning, Deep Learning, NLP, Generative & Agentic AI and MLOps deployment — choose a 6-month weekday (Mon–Fri) batch or a 9-month weekend (Sat–Sun) batch — built for both freshers targeting their first AI/DS role and professionals upskilling into it.

Book Free Weekend Demo View Full Curriculum

Data science team reviewing machine learning model results

Data science team collaborating on an analytics project

Indian data science team applauding after a successful project presentation

Student learning data science and machine learning on a laptop

Who this course is for

Fresh graduates

Engineering / science / commerce graduates who want a structured, job-ready path into AI & Data Science roles with zero prior coding depth required.

Working professionals

Developers, analysts and QA/support professionals looking to upskill into ML/AI engineering or data science roles with a proof-of-work portfolio.

Full curriculum — 240 hours across 10 modules

Every module blends theory with hands-on labs. Click a module to see topic-level detail.

Python syntax, data structures & OOP
Git, GitHub & version control workflows
SQL: joins, window functions, query tuning
REST APIs & working with JSON
NumPy for numerical computing
Pandas for data wrangling
Data cleaning & missing-value handling
Jupyter, Colab & VS Code workflows
Linux / command-line essentials
DSA & problem solving for interviews

Descriptive statistics & distributions
Probability theory & Bayes' theorem
Hypothesis testing & A/B testing
Correlation & regression fundamentals
Sampling methods & confidence intervals
Exploratory Data Analysis (EDA)
Outlier detection & treatment
Feature engineering fundamentals

Matplotlib & Seaborn
Interactive dashboards with Plotly
Power BI / Tableau essentials
Data storytelling for stakeholders
Designing executive dashboards
KPI & metric design

Scikit-learn for classical ML pipelines
Regression & classification fundamentals
Decision Trees, Random Forest
XGBoost & LightGBM
SVM & K-Nearest Neighbours
Clustering: K-Means, hierarchical, DBSCAN
Dimensionality reduction: PCA, t-SNE
Model evaluation & cross-validation
Hyperparameter tuning (GridSearch, Optuna)
Handling imbalanced datasets
Time-series forecasting (ARIMA, Prophet)

Neural networks & backpropagation
TensorFlow & Keras
PyTorch fundamentals
CNNs for computer vision
RNN, LSTM & GRU for sequences
Transfer learning & pretrained models
Transformer architecture fundamentals
Regularization, dropout & batch norm
GPU training & experiment tracking

Text preprocessing & tokenization
Word embeddings: Word2Vec, GloVe
Sequence models for NLP
Transformers & attention mechanism
Hugging Face Transformers library
NER & sentiment analysis
Text classification & summarization
Building an NLP microservice

LLM fundamentals & prompt engineering
OpenAI, Claude & Gemini APIs
Open-source LLMs via Hugging Face & Ollama
Embeddings & vector databases (FAISS, Pinecone, Chroma)
Building RAG (Retrieval-Augmented Generation) pipelines
LangChain fundamentals
Fine-tuning basics (LoRA / QLoRA overview)
Responsible AI, hallucination mitigation & guardrails
Building a GenAI-powered web app

Introduction to AI agents & autonomous workflows
LangGraph basics for stateful agents
Multi-agent collaboration with CrewAI
Model Context Protocol (MCP) overview
Tool-calling & function-calling patterns
Agent memory & planning basics

Model packaging with Flask / FastAPI
Docker & containerization
Experiment tracking with MLflow
CI/CD pipelines for ML
Model monitoring & drift detection
Cloud deployment on AWS / Azure / GCP
Introduction to Spark for big data
Databricks for big-data & ML pipelines
API design & scalability basics

Capstone 1: End-to-end predictive analytics project
Capstone 2: Computer vision / NLP deep learning project
Capstone 3: GenAI-powered RAG app, deployed to cloud
Resume building & GitHub portfolio
Mock technical interviews
Aptitude & communication workshop

Tools & technologies you'll master

Career outcomes

Data ScientistMachine Learning EngineerAI EngineerData AnalystBusiness Intelligence AnalystGenAI Developer

Certificate of Completion

A credential you can show employers

Every graduate receives a verifiable AiTeky certificate of completion. Here's a sample of what yours will look like.

Sample AiTeky certificate of completion for the AI & Data Science Full Stack course