Master Machine Learning & Generative AI

Learn AI, ML, and generative AI with Python, deep learning, NLP, and cloud to build end-to-end smart solutions.

150k+ Placements to Date
600+ Hiring Partners
76 Lakhs Highest Annual Salary

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Next Batch starts in October

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Flexible Learning Modes to Fit Your Schedule

Interactive Classroom Sessions
Live Virtual Instructor-Led Classes
Self-Guided Online Modules
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Build an Impressive Portfolio

Expand Your Career Opportunities

Stay Ahead with Industry Trends

Master Cutting-Edge Development Tools

High-Demand Careers in AI & Data Science

The AI and data science industry is booming, creating strong demand for skilled professionals who can analyze data, build predictive models, and deploy AI solutions. Expertise in ML, DL, NLP, and GenAI opens doors to highly lucrative roles.

Designation

Annual Salary

Hiring Companies

data Scientist

₹8–15 LPA (Entry-Level) ₹15–25 LPA (Mid-Level) ₹30+ LPA (Senior-Level)

Data Scientists extract insights from structured and unstructured data using Python, R, SQL, and machine learning frameworks to drive strategic decision-making.

machine learning ₹6–15 LPA (Entry-Level) ₹15–25 LPA (Mid-Level) ₹25–40+ LPA (Senior-Level)

Machine Learning Engineers develop predictive models, design algorithms, and deploy AI solutions using tools like TensorFlow, PyTorch, and Scikit-learn.

data engineer

₹6–12 LPA (Entry-Level) ₹12–25 LPA (Mid-Level) ₹25+ LPA (Senior-Level)

Data Engineers build scalable data pipelines, manage databases, and optimize data flows for analysis.

ai specialist

₹8–20 LPA (Entry-Level) ₹20–35 LPA (Mid-Level) ₹35+ LPA (Senior-Level)

AI Specialists design and develop intelligent systems, focusing on natural language processing, computer vision, and AI-driven solutions.

Statistician

₹5–10 LPA (Entry-Level) ₹10–18 LPA (Mid-Level) ₹18+ LPA (Senior-Level)

Statisticians use statistical methods and tools to analyze data, interpret results, and make recommendations for business and research purposes.

nlp engineer

₹8–18 LPA (Entry-Level) ₹20–35+ LPA (Mid-Level)

NLP Engineers work on language-based AI systems, such as chatbots, sentiment analysis tools, and speech-to-text systems, using Python and NLP libraries.

High-Demand Careers in AI & Data Science

Annual Salary

data Scientist

₹8–15 LPA (Entry-Level) ₹15–25 LPA (Mid-Level) ₹30+ LPA (Senior-Level)

Hiring Companies

Data Scientists extract insights from structured and unstructured data using Python, R, SQL, and machine learning frameworks to drive strategic decision-making.

Course Description

Complete Data Science & AI Curriculum

Master the full spectrum from databases to Generative AI through 8 comprehensive modules covering
400+ hours of content with hands-on projects and real-world applications.

Course Details:

Duration: 6–8 months intensive program
Mode: Live online classes with recorded sessions
Schedule: Weekend batches and weekday evening options
Support: 24/7 doubt resolution and mentor guidance
Assessment: Weekly assignments and capstone projects

Who Should Enroll

Fresh graduates seeking entry into data science and AI fields
Working professionals looking to transition into ML/AI roles
Software developers wanting to upskill in data science
Business analysts aiming to enhance technical capabilities
Students pursuing computer science or related disciplines
Entrepreneurs planning to build AI-powered solutions

Our cohorts mix beginners and working professionals. Mentors tailor guidance to your background so you can ramp up quickly and apply concepts in real projects.

What You’ll Learn

Use essential tools for data science and AI practice
Manage databases (SQL/NoSQL) and Python data connectivity
Build ML & DL models, NLP pipelines, and GenAI assistants
Deploy to AWS, Azure, and GCP with best-practice workflows

Data Science, ML & Gen AI Course Curriculum

Master Tools, Techniques, and Real-World Applications

Our industry-aligned curriculum spans 8 comprehensive modules, from database fundamentals to advanced Generative AI. Each module combines theoretical concepts with practical implementation, ensuring you master both the science and art of data-driven decision making through hands-on projects and real-world applications.

Download the complete Data Scinece Curriculum

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Objective: Build a strong Python foundation for data science and automation.

Python Basics: Data types, loops, conditionals
Functions, Modules, Exception Handling
File Handling, JSON, CSV
OOPs Concepts in Python

Mini Projects:

Library Management Application
Attendance Tracker using file I/O
Build a personal task tracker using OOPs

Objective: Learn to manage structured and unstructured data with SQL/NoSQL and connect them with Python.

SQL & NoSQL Concepts: Keys, Joins, CRUD
Databases: MySQL, PostgreSQL, SQLite
MongoDB for NoSQL
Advanced SQL: Triggers, Procedures, CTE
Python DB Integration: PyMySQL, SQLAlchemy

Mini Projects:

Student Management System with MySQL backend
MongoDB-based product catalog with CRUD operations

Objective: Develop statistical and mathematical skills to support data interpretation and ML algorithms.

Descriptive & Inferential Statistics
Probability Theory: Bayes, Distributions
Hypothesis Testing, p-values
Linear Algebra: Matrix operations, Eigenvalues
Data Visualization of Statistical Properties

Mini Projects:

Stock Price Prediction use case using Statistical Analysis
A/B testing for marketing Analysis.

Objective: Collect, clean, and visualize data to extract meaningful business insights.

Data Collection: APIs, Web Scraping, Kaggle
Data Cleaning, Transformation: Outliers, Nulls, Scaling
EDA using Pandas
Feature Engineering: Encoding, Binning
Visualization Tools: Matplotlib, Seaborn, Plotly, Streamlit

Mini Projects:

Create a dashboard for sales trends using real-time API
Visualize and analyze cricket match data

Objective: Learn to build, train, and evaluate predictive models using real-world data.ML Concepts: Training, Overfitting, Evaluation

Regression: Linear, Polynomial
Classification: SVM, KNN, Naïve Bayes, Decision Trees
Clustering: K-Means, Hierarchical
Dimensionality Reduction: PCA, Autoencoders
Evaluation Metrics: Accuracy, F1, ROC-AUC
Deployment: Flask, Pickle, Joblib

Mini Projects:

E-commerce product recommendation engine
Live plot using matplotlib Animation
Loan approval prediction model

Objective: Enable machines to understand, interpret, and generate human language.

Text Preprocessing: Tokenization, Lemmatization, Stopwords
Word Embeddings: TF-IDF, Word2Vec
Applications: Sentiment Analysis, NER, Text Classification

Mini Projects:

Twitter sentiment analysis
Resume classification system for HR filtering

Objective: Understand and implement deep learning architectures for vision and sequence tasks.

Neural Network Basics: Layers, Activation Functions, Backprop
CNNs for Image Classification
RNNs & Transformers for Sequence Models
Transfer Learning: VGG, ResNet, MobileNet
Frameworks: TensorFlow, Keras, PyTorch

Mini Projects:

Image classification using CNN

Objective: Build intelligent systems using Large Language Models and search-based generation techniques.

GenAI Concepts: LLMs, GPT, BERT, VAEs, GANs
Fine-Tuning LLMs using Hugging Face
RAG Pipeline
Building Chatbots with Open Source APIs

Mini Projects:

AI chatbot with Fine – Tuning
AI Text Summarizer
Personalized blog content generator using GPT

Objective: Utilize cloud infrastructure for model training, storage, and deployment at scale.

Cloud Concepts: IaaS, PaaS, SaaS
Platforms: AWS, Azure, GCP Overview
Services: SageMaker

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Hands-On Learning Build End-to-End AI Projectes

Work on top capstone projects that replicate real industry scenarios, such as building a Generative AI Chatbot using LLMs, Hugging Face, RAG, and LangChain, developing a Real-Time Sentiment Analysis Dashboard with NLP, Pandas, and Streamlit, creating an Anime Face Generator with GANs using PyTorch/TensorFlow, implementing a B&W to Color Image Converter with CNNs, and conducting Business Report Analysis through APIs, web scraping, and data visualization.
Throughout these projects, you will gain hands-on experience with Python, deep learning frameworks, Pandas, NumPy, Matplotlib, NLP, GANs, transformers, Streamlit, and AWS SageMaker. You’ll also learn to build, train, and deploy AI models while implementing complete end-to-end AI workflows, preparing you for professional roles in the field.