Machine learning & Data Science

Introduction

Machine Learning and Data Science are at the core of modern, data-driven decision making, powering applications such as predictive analytics, recommendation systems, fraud detection, and intelligent automation. This course is designed to provide a strong, end-to-end foundation in Machine Learning and Data Science, combining theoretical understanding with hands-on practical implementation.

The program begins with the basics of Python, the primary programming language used in data science and machine learning. Learners are then introduced to the fundamentals of Machine Learning, supported by essential mathematical concepts including statistics, linear algebra, and probability. From defining a business or analytical problem to data collection, preprocessing, exploratory data analysis (EDA), and data partitioning, the course emphasizes a structured and real-world approach to solving data problems.

Participants will gain in-depth knowledge of regression and classification metrics, followed by a comprehensive study of key machine learning algorithms. Topics include simple and logistic regression, clustering and dimensionality reduction (PCA), tree-based models, SVM, KNN, and advanced ensemble techniques such as Random Forest, Extra Trees, AdaBoost, XGBoost, and Extreme XGBoost. Core concepts like underfitting, overfitting, regularization, and hyperparameter tuning using GridSearchCV and Randomized Search are covered to ensure robust model development.

To bridge the gap between models and production, learners will build and deploy applications using Flask and Streamlit, follow best practices with VS Code and GitHub (CI/CD), and gain practical exposure to containerization using Docker. By the end of the course, participants will have a clear understanding of how to design, build, deploy, and maintain scalable machine learning solutions in real-world environments.