Courses
Free Webinar - AI/ GenAI/ ML Basic
Are you a student curious about Artificial Intelligence, Machine Learning, or how to build your own intelligent apps? Join us for an exciting FREE webinar designed especially for beginner students who are eager to explore the world of AI.
What to Expect in the Webinar:
Learn how AI/ GenAI/ ML technologies are shaping the world
A beginner-friendly look into the power of Generative AI (like ChatGPT & AI image generation)
A sneak peek into our upcoming hands-on AI/ML course
Real-world examples and engaging demos
Interactive Q&A session with our instructors
Connect with mentors and like-minded students
Foundations of Programming & Data Science
This course is designed to introduce students to the exciting world of computer programming and data science. Through hands-on activities, real-world projects, and guided instruction, students will build a solid foundation in coding and learn how data is used to solve real-life problems.
What You’ll Learn:
Programming Fundamentals
Introduction to Python
Variables, loops, conditionals, and functions
Writing clean, readable, and logical code
Data Science Essentials
What is data and why it matters
Collecting, organizing, and visualizing data
Using Python libraries like Pandas and Matplotlib
Basic statistics and data interpretation
Real-World Applications
How companies use data to make decisions
Mini-projects using real datasets (e.g., weather, sports, social media)
Key Skills Gained:
Computational thinking
Problem-solving with code
Data analysis and visualization
Confidence in using technology to create solutions
Who Should Enroll:
No prior experience is needed! This course is perfect for beginner school students who are curious about technology, enjoy problem-solving, or want to explore future careers in STEM.
Advanced Machine Learning
This advanced-level course is designed for students who have a foundational understanding of programming and are ready to take the next step in data science. Students will explore core concepts in statistics, regression analysis, and classification techniques—the building blocks of predictive modeling and modern AI systems.
Through interactive lessons, hands-on coding projects, and real-world datasets, students will gain practical experience in analyzing data, uncovering patterns, and building their own predictive models using Python.
What You’ll Learn:
Statistics for Data Science
Descriptive statistics: mean, median, variance, standard deviation
Data distributions and probability basics
Introduction to inferential statistics and hypothesis testing
Regression Analysis
What is regression and why it’s used
Building simple and multiple linear regression models
Interpreting regression outputs and measuring model accuracy
Visualizing relationships using scatter plots and trendlines
Classification Techniques
Understanding classification problems in real-world contexts (e.g., email spam detection, medical diagnosis)
Introduction to algorithms like K-Nearest Neighbors and Decision Trees
Evaluating model performance: accuracy, confusion matrix, precision/recall
Tools & Technologies:
Python
Pandas, NumPy, Matplotlib, Seaborn
Scikit-learn for machine learning mode
Who Should Enroll:
This course is ideal for students who have completed an introductory programming or data science course and are eager to explore machine learning foundations and data-driven decision-making.