Loading
AI and Data Science Mastery Program 5.5 Months · ₹56,000
Data & AI Enrolling Now

AI and Data Science
Mastery Program

Go from zero to job-ready in 5.5 months. Master Python, Statistics, Machine Learning, Deep Learning, NLP & Generative AI — with real-world projects and dedicated placement support.

5.5 Months
Live Online
5+ Industry-Ready Projects
100% Placement Assistance
Lifetime Access to Materials
Industry-Recognized Certificate
Most Popular
Course Investment
₹56,000
Duration5.5 Months
ModeLive Online
Modules9 Modules + Capstone Project
Next Batch28th Feb 2026
CertificateIndustry-Recognized
Enroll Now →
5.5
Months Duration
9+
Course Modules
5+
Industry-Ready Projects
1,240+
Students Enrolled

What You'll Learn

Our AI and Data Science program is built for professionals who want to transition into one of the most in-demand careers in tech. You'll work with real-world datasets, build predictive models, and learn to communicate data-driven decisions — the way top companies expect.

The program covers everything from Python fundamentals through advanced Deep Learning, NLP, and Generative AI — with hands-on projects at every stage.

  • Hands-on projects with real industry datasets
  • Capstone project with personalized mentorship
  • Resume building, LinkedIn & mock interview prep
  • Job referrals to 50+ hiring partner companies
  • Statistics, ML, Deep Learning, NLP & Gen AI
  • Recorded sessions for unlimited revision

Ideal Candidates

Career Switchers Working professionals looking to move into AI and Data Science roles
Fresh Graduates Engineering, science, or commerce graduates looking for a strong start
Analysts & Developers Professionals who want to level up with ML & AI skills
Entrepreneurs Business owners who want to make data-backed strategic decisions

Syllabus

Module 1:

  • Introduction to Python and Setup
  • Python Syntax and Variables
  • Data Types (int, float, string, boolean)
  • Operators (Arithmetic, Comparison, Logical, Assignment)
  • Control Flow (if-else, loops)
  • Data Structures (Lists, Tuples, Sets, Dictionaries)
  • Functions and Lambda Expressions
  • Object-Oriented Programming (Classes, Objects, Inheritance)
  • Modules and Packages
  • File Handling (CSV, JSON, Excel, Text files)
  • Exception Handling and Debugging
  • NumPy for Numerical Computing
  • Pandas for Data Manipulation
  • Data Cleaning and Preprocessing
  • Matplotlib for Data Visualization
  • Seaborn for Statistical Plots
  • Working with APIs and Web Scraping
  • Regular Expressions
  • DateTime Module for Time Series

Module 2:

  • Introduction to Machine Learning and Its Types
  • Supervised Learning vs Unsupervised Learning vs Reinforcement Learning
  • Data Preprocessing and Feature Engineering
  • Handling Missing Values and Outliers
  • Feature Scaling (Normalization, Standardization)
  • Encoding Categorical Variables (One Hot, Label Encoding)
  • Linear Regression and Polynomial Regression
  • Ridge and Lasso Regression (Regularization)
  • Logistic Regression for Classification
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes Classifier
  • Ensemble Methods (Bagging, Boosting, Stacking)
  • XGBoost, AdaBoost and Gradient Boosting
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN and Other Clustering Algorithms
  • Principal Component Analysis (PCA)
  • t-SNE for Dimensionality Reduction
  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC)
  • Confusion Matrix and Classification Report
  • Cross-Validation Techniques
  • Hyperparameter Tuning (Grid Search, Random Search)
  • Overfitting, Underfitting, and Regularization (L1, L2)
  • Bias-Variance Tradeoff
  • Recommendation Systems
  • Time Series Forecasting (ARIMA, Prophet)
  • Anomaly Detection Techniques

Module 3:

  • Introduction to Neural Networks
  • Perceptrons and Multi-Layer Perceptrons (MLPs)
  • Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
  • Forward and Backward Propagation
  • Loss Functions and Optimization Algorithms (SGD, Adam, RMSprop)
  • Deep Neural Networks (DNNs) Architecture
  • Convolutional Neural Networks (CNNs)
  • Image Classification with CNNs
  • Object Detection (YOLO, R-CNN, SSD)
  • Image Segmentation Techniques
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Units (GRU)
  • Sequence-to-Sequence Models
  • Autoencoders and Variational Autoencoders (VAE)
  • Generative Adversarial Networks (GANs)
  • Transfer Learning Concepts
  • Pre-trained Models (VGG, ResNet, InceptionNet, MobileNet)
  • Fine-tuning Pre-trained Models
  • TensorFlow and Keras Framework
  • PyTorch Framework
  • Regularization Techniques (Dropout, Batch Normalization)
  • Hyperparameter Tuning for Deep Learning
  • GPU Acceleration and Distributed Training
  • Model Deployment and Serving

Module 4:

  • Introduction to Natural Language Processing
  • Text Preprocessing and Cleaning
  • Tokenization Techniques
  • Stemming and Lemmatization
  • Stop Words Removal
  • Bag of Words (BoW) Model
  • TF-IDF (Term Frequency-Inverse Document Frequency)
  • Word Embeddings (Word2Vec, GloVe, FastText)
  • N-grams and Language Models
  • Sentiment Analysis
  • Text Classification
  • Named Entity Recognition (NER)
  • Part-of-Speech (POS) Tagging
  • Topic Modeling (LDA, LSA)
  • Text Summarization
  • Language Translation
  • Chatbot Development
  • Attention Mechanism
  • Transformer Architecture
  • BERT and Its Variants
  • GPT Models Overview
  • Large Language Models (LLMs)
  • Prompt Engineering Techniques
  • Hugging Face Transformers Library
  • NLTK and spaCy Libraries
  • Question Answering Systems

Module 5:

  • Introduction to Databases
  • Relational Database Management Systems (RDBMS)
  • Database Design and ER Diagrams
  • Normalization (1NF, 2NF, 3NF, BCNF)
  • SQL Basics and Syntax
  • Data Definition Language (DDL) - CREATE, ALTER, DROP
  • Data Manipulation Language (DML) - SELECT, INSERT, UPDATE, DELETE
  • Filtering Data with WHERE Clause
  • Sorting and Grouping Data (ORDER BY, GROUP BY, HAVING)
  • Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
  • SQL Joins (INNER, LEFT, RIGHT, FULL OUTER, CROSS)
  • Subqueries and Nested Queries
  • Common Table Expressions (CTEs)
  • Window Functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG)
  • Views and Materialized Views
  • Indexes and Query Optimization
  • Stored Procedures and Functions
  • Triggers and Events
  • Transactions and ACID Properties
  • NoSQL Databases Introduction
  • MongoDB Basics and Operations
  • Connecting Databases with Python (SQLAlchemy, PyMongo)
  • Database Migration and Version Control

Module 6:

  • Introduction to Cloud Computing
  • Cloud Service Models (IaaS, PaaS, SaaS)
  • Cloud Deployment Models (Public, Private, Hybrid)
  • Overview of AWS (Amazon Web Services)
  • Overview of Microsoft Azure
  • Overview of Google Cloud Platform (GCP)
  • Cloud Storage Solutions (S3, Azure Blob, Google Cloud Storage)
  • AWS SageMaker for ML Model Training and Deployment
  • Azure Machine Learning Studio
  • Google AI Platform and Vertex AI
  • Containerization with Docker
  • Container Orchestration with Kubernetes
  • Serverless Computing (AWS Lambda, Azure Functions)
  • Model Deployment Strategies
  • Cloud-based APIs and Endpoints
  • CI/CD Pipelines for ML Models
  • Model Monitoring and Logging
  • Auto-scaling and Load Balancing
  • Cloud Security and IAM (Identity Access Management)
  • Cost Optimization in Cloud

Module 7:

  • Introduction to Web Frameworks
  • Flask Installation and Setup
  • Flask Application Structure
  • Routing and URL Mapping
  • HTTP Methods (GET, POST, PUT, DELETE)
  • Request and Response Handling
  • Jinja2 Templating Engine
  • Static Files and CSS Integration
  • Form Handling with Flask-WTF
  • Building RESTful APIs
  • JSON Serialization and Deserialization
  • API Authentication (JWT, OAuth)
  • Error Handling and Logging
  • Serving Machine Learning Models via API
  • FastAPI Introduction and Benefits
  • Building APIs with FastAPI
  • Data Validation with Pydantic
  • Asynchronous Programming with FastAPI
  • API Documentation with Swagger/OpenAPI
  • Testing Flask/FastAPI Applications
  • Deployment to Production (Gunicorn, Nginx)

Module 8:

  • Jupyter Notebook and JupyterLab
  • Google Colab for Cloud Computing
  • VS Code Setup and Extensions for Data Science
  • PyCharm IDE for Python Development
  • Anaconda Distribution and Package Management
  • Git and GitHub for Version Control
  • Git Branching and Merging Strategies
  • Collaboration with Pull Requests
  • Plotly for Interactive Visualizations
  • Dash for Building Analytical Web Applications
  • Streamlit for Rapid Prototyping
  • Tableau for Business Intelligence
  • Power BI for Data Visualization
  • Excel for Data Analysis
  • Docker Fundamentals and Containerization
  • Docker Compose for Multi-container Applications
  • MLflow for Experiment Tracking
  • Model Registry and Versioning
  • Apache Airflow for Workflow Orchestration
  • DVC (Data Version Control)
  • Great Expectations for Data Quality

Module 9:

  • Introduction to Generative AI
  • Generative vs Discriminative Models
  • Applications of Generative AI
  • Large Language Models (LLMs) Overview
  • GPT (Generative Pre-trained Transformer) Architecture
  • BERT, T5, and Other Transformer Models
  • Prompt Engineering Fundamentals
  • Prompt Design Patterns and Best Practices
  • Few-Shot and Zero-Shot Learning
  • Chain-of-Thought Prompting
  • Working with OpenAI API
  • GPT-3.5, GPT-4, and GPT-4 Turbo
  • Azure OpenAI Service Integration
  • Google Gemini and Palm API
  • Hugging Face Transformers Library
  • LangChain Framework for LLM Applications
  • LlamaIndex for Data-Augmented LLMs
  • Retrieval Augmented Generation (RAG)
  • Vector Databases (Pinecone, ChromaDB, FAISS)
  • Building AI Chatbots
  • Content Generation Applications
  • AI-Powered Virtual Assistants
  • Code Generation with AI (GitHub Copilot, Codex)
  • Image Generation (DALL-E, Stable Diffusion, Midjourney)
  • Fine-Tuning LLMs for Custom Use Cases
  • Model Deployment and Serving
  • Ethical AI and Responsible AI Development
  • AI Governance and Compliance
  • Bias Detection and Mitigation

Tools & Technologies You'll Master

Industry-standard tools used by top AI and Data Science teams worldwide

Languages

Python Python
SQL SQL

Libraries & Frameworks

Pandas Pandas
NumPy NumPy
Scikit-learn Scikit-learn
TensorFlow TensorFlow
Keras Keras
Matplotlib Matplotlib
Flask Flask
FastAPI FastAPI

Platforms & Tools

Jupyter Jupyter
Docker Docker
Git Git
Tableau
Power BI
Seaborn

Your Learning Journey

A proven 4-step process that takes you from beginner to job-ready

01

Learn

Live instructor-led sessions with industry experts. Every concept is taught with real-world examples.

02

Practice

Daily assignments, weekly assessments, and coding exercises to reinforce what you've learned.

03

Build

Work on real-world capstone projects with mentor guidance. Build a portfolio that impresses employers.

04

Get Placed

Mock interviews, resume reviews, and direct referrals to 50+ hiring partners across India.

Our Alumni Work At

Our graduates are employed at India's leading technology companies

Learn from Industry Leaders

Our instructors bring 10+ years of real-world experience from top MNCs — not just textbook knowledge.

VT

VCTC Expert Faculty

Senior Data Scientist & AI Practitioner

10+ Years Experience 500+ Students Trained Industry Certified
500+
Students Mentored
10+
Years in Industry
15+
Live Projects Guided

What Our Students Say

★★★★★

"This program literally changed my career trajectory. Within 3 months of completing the course, I landed a Data Scientist role at an MNC in Pune. The real-world projects gave me the confidence to ace interviews."

PS
Priya S. Data Scientist at Infosys
Career Switcher
★★★★★

"I was a fresher with zero coding background. The curriculum was so well structured that I could follow along easily. The placement team connected me with 5 companies, and I got placed within weeks."

AK
Amit K. ML Engineer at TCS
Fresh Graduate
★★★★★

"The best investment I made in my career. The capstone project on Customer Churn Prediction became my talking point in every interview. Trainers are exceptional — they make complex topics simple."

RD
Rahul D. Data Analyst at Deloitte
Working Professional
Confident in technical interviews
Python, ML, Deep Learning, NLP & Gen AI
Optimized resume + strong LinkedIn brand
Avg. Starting Salary ₹8 LPA
Start Your Transformation →

Frequently Asked Questions

No. We start from the fundamentals. Many of our successful graduates had zero coding experience when they joined. The program is designed to take you from absolute beginner to job-ready.

Yes. We provide dedicated placement assistance including resume reviews, daily & weekly mock interviews, and direct referrals to our 50+ hiring partners across Pune and India.

We offer weekday evening and weekend batches to accommodate working professionals. All sessions are also recorded for review. You can reach out to check current batch timings.

Yes. This course is conducted as live online instructor-led sessions. You interact with the trainer in real time, ask questions, and work on projects — just like a physical classroom experience.

Your AI and Data Science Career
Starts Here

Join 1,240+ students who transformed their careers with VCTC. Next batch starts 28th February.

₹56,000
✔ 100% Placement Assistance ✔ Lifetime Access ✔ Certificate Included
Enquire
Now