Applied Machine Learning & Data Science Techniques Course Overview

The Applied Machine Learning & Data Science Techniques course is a comprehensive 3-day program designed to equip participants with essential skills in data exploration, clustering, recommendation systems, regression analysis, decision systems, forecasting, and neural networks.

Students will learn to effectively conduct Exploratory Data Analysis (EDA), implement clustering methods like K-means and DBSCAN, and build robust recommendation systems using collaborative and content-based filtering. The course also covers advanced regression techniques, decision trees, and ensemble methods. Practical applications include developing forecasting models and understanding deep learning architectures like CNNs and RNNs. By the end, participants will be ready to apply these data science techniques in real-world business scenarios, enhancing decision-making and optimizing performance.