Artificial Intelligence & Machine Learning
AI & Machine Learning Fundamentals
Build a strong understanding of Artificial Intelligence, Machine Learning, supervised and unsupervised learning techniques.
Data Analysis & Model Development
Learn to work with real-world data using Python, NumPy and Pandas, and build, train and evaluate machine learning models.
Computer Vision & Deep Learning
Explore neural networks, deep learning, OpenCV, image processing and object detection for intelligent applications.
Real-World AI Applications
Apply AI and Machine Learning concepts to practical projects, automation systems and real-world industry applications.
8
Months Program
32
Week Roadmap
7 + 1
Months Training + Internship
11
Modules Across 8 Phases
About the Program
PG Diploma in Data Science & Machine Learning
Learn data science. Build intelligent systems.
Turn your curiosity for data into a high-impact tech career with the Post Graduate Diploma in Data Science & Machine Learning — an intensive 8-month, industry-ready program crafted for the next generation of data professionals.
This is more than classroom learning. It is a hands-on career transformation where you learn to think like a data scientist, code like an engineer and solve problems like an industry expert. From day one, you work with real datasets, real tools and real challenges used in today’s technology industry.
Why Choose This Program?
Strong Foundations
Build a strong foundation in Python, statistics and data analytics from the ground up.
End-to-End ML Workflows
Learn the complete machine learning workflow, from raw data preparation to evaluated and deployed models.
Real Projects & Case Studies
Work with real-world business and public datasets throughout the program.
7 + 1 Month Structure
Gain seven months of intensive training followed by a guided industry internship.
Career Mentoring
Build your portfolio and prepare for interviews with guidance from expert mentors.
Modern 2026 Toolstack
Gain exposure to modern tools including Power BI, Tableau, Snowflake, cloud platforms and AI-assisted analytics.
Learning Pathway
Roadmap & Syllabus
8 phases, taught with hands-on labs and projects.
Phase 1 · Weeks 1–4 Data Science Foundations & SQL
- Introduction to Data Science and the data lifecycle
- Understanding structured and unstructured data
- Relational databases and database concepts
- SQL fundamentals, queries and filtering
- Joins, aggregations and grouping
- Subqueries and data manipulation
- Working with real-world datasets using SQL
Phase 2 · Weeks 5–8 Data Cleaning, Feature Engineering & Excel
- Data cleaning and preprocessing techniques
- Handling missing values and duplicate records
- Data transformation and normalization
- Feature engineering fundamentals
- Excel formulas and functions for data analysis
- Pivot tables, charts and data reporting
- Practical data cleaning exercises and projects
Phase 3 · Weeks 9–12 Data Visualization & Business Intelligence
- Principles of effective data visualization
- Charts, graphs and interactive dashboards
- Business Intelligence concepts and workflows
- Dashboard development and reporting
- Data storytelling and communicating insights
- Introduction to Power BI and Tableau
- Business-focused data visualization projects
Phase 4 · Weeks 13–16 Python & Statistics for Data Science
- Python programming fundamentals for data science
- NumPy and Pandas for data analysis
- Data manipulation and exploratory data analysis
- Probability and statistical concepts
- Descriptive and inferential statistics
- Data distributions and hypothesis testing
- Data visualization using Matplotlib and Python
Phase 5 · Weeks 17–20 Machine Learning & Model Building
- Introduction to Machine Learning workflows
- Supervised and unsupervised learning
- Regression and classification algorithms
- Clustering and pattern discovery
- Feature selection and model optimization
- Model training, validation and evaluation
- Performance metrics and practical ML projects
Phase 6 · Weeks 21–24 Advanced Data Science & AI Tools
- Advanced data analysis techniques
- Introduction to Deep Learning concepts
- Neural networks and AI fundamentals
- Natural Language Processing fundamentals
- Computer Vision and image analysis concepts
- Introduction to Generative AI and Large Language Models
- Working with modern AI and data science tools
Phase 7 · Weeks 25–28 Capstone Projects
- Industry-oriented data science project development
- Problem identification and solution design
- Data collection, cleaning and preparation
- Exploratory data analysis and feature engineering
- Machine Learning model development
- Model evaluation and deployment concepts
- Technical documentation and project presentation
Phase 8 · Weeks 29–32 Industry Internship
- Real-world industry exposure
- Working with practical data science and machine learning projects
- Applying analytics and ML concepts to business problems
- Collaborative project development
- Data analysis, model testing and performance improvement
- Portfolio development and project presentation
- Career preparation and industry readiness
Skills You Will Gain
Core Data Science Foundation
- Clear understanding of the data science lifecycle, data types, data sources and pipelines
Data Cleaning & Feature Engineering
- Cleaning messy real-world data, handling missing values and outliers, and preparing high-quality data for machine learning
Data Visualization & BI
- Interactive dashboards and reports using Excel, Power BI and Tableau for effective decision-making
Python & Statistics
- Data analysis using Python with statistical techniques to explore data, test hypotheses and support decisions
Machine Learning & Modern Tools
- Machine learning, forecasting and AI-assisted analytics to build, evaluate and deploy solutions
Technology Stack
- Python and SQL
- NumPy, Pandas and SciPy
- Matplotlib, Seaborn, Power BI and Tableau
- MySQL, PostgreSQL, SQLite, Snowflake and BigQuery concepts
Career Paths
Analytics & BI Roles
- Data Analyst — SQL, Excel, Python and BI tools for business decision-making
- Business Intelligence Analyst — Power BI and Tableau dashboards and data storytelling
Data Science & ML Roles
- Junior / Associate Data Scientist — Statistics, Python and ML for predictive models
- Machine Learning Engineer (Entry Level) — Model development, feature engineering and performance optimization
Entry & Future-Ready Roles
- Data Science Intern / Trainee — Real projects, capstone work and internship experience
- AI & Analytics Professional — Modern AI tools, forecasting and cloud data platforms