In this module, we will refresh your knowledge of statistics and probability, emphasizing Python practices to apply these concepts. You'll explore data types, key statistical measures, common data distributions, and advanced visualizations, all while working with Python libraries like matplotlib and Seaborn. Additionally, you will learn essential probability concepts such as covariance, correlation, conditional probability, and Bayes' Theorem, applying them in real-world examples.

In this module, we will dive into the world of predictive modeling, starting with linear and polynomial regression to make predictions from sample data. You'll also learn how to work with multiple regression models in Python to predict values based on multiple attributes, such as car prices. Lastly, we will introduce you to the concept of multi-level models, giving you insight into this advanced modeling approach.

In this module, we will guide you through various machine learning concepts and techniques using Python, starting with supervised and unsupervised learning. You'll learn to implement models like Naive Bayes, K-Means clustering, and decision trees, while also diving into more advanced methods such as XGBoost and Support Vector Machines (SVM). Additionally, we’ll cover ensemble learning and how to combine multiple models for better results, equipping you with the skills to tackle a range of machine learning tasks.

In this module, we will explore the core concepts behind recommender systems, focusing on both user-based and item-based collaborative filtering techniques. You'll work with real-world datasets, such as MovieLens, to apply cosine similarity and build your own movie recommendation system. We’ll also guide you through refining the accuracy of your recommendations and provide opportunities for you to improve the system with your own ideas.