Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Embark on a hands-on learning journey through data science and machine learning with Python. In this course, you will gain a deep understanding of core data science concepts and machine learning techniques, while mastering essential Python libraries. You will build the skills necessary to analyze datasets, visualize results, and apply machine learning models to real-world data. The course begins with an introduction to data handling, including installing necessary tools like Anaconda, followed by a Python crash course. You will then explore foundational statistical concepts and their application using Python. Next, we delve into building predictive models, from linear regression to polynomial and multiple regression, and understanding their real-world applications. As you progress, you'll dive into machine learning techniques, such as supervised and unsupervised learning, including decision trees, support vector machines, and ensemble learning methods like XGBoost. Finally, you’ll learn how to build recommender systems, helping you understand the intricacies of collaborative filtering and how to improve your model’s predictions. This course is ideal for individuals eager to break into the world of data science and machine learning, as well as those wishing to enhance their Python skills for professional growth. The course assumes basic familiarity with programming concepts, making it perfect for beginners in the field.

Syllabus

Getting Started

In this module, we will introduce the course structure, expectations, and provide you with hands-on installation guidance for Anaconda on different platforms. You'll also be guided through the essentials of Python, focusing on key concepts such as data structures, functions, and loops, as well as getting started with the Pandas library for data analysis.

Statistics and Probability Refresher, and Python Practice

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.

Predictive Models

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.

Machine Learning with Python

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.

Recommender Systems

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.