Overview

Build a strong foundation in exploratory data analysis (EDA) and machine learning with this hands-on course. Designed for learners with basic Python and ML knowledge, you’ll move step by step from preparing datasets to implementing some of the most widely used algorithms in real-world applications. Your journey begins with EDA, where you’ll learn to visualize data, detect patterns, and handle missing or outlier values to ensure your datasets are clean and reliable. From there, you’ll dive into linear regression and mastering predictive modeling techniques for forecasting and trend analysis. Next, you’ll explore logistic regression, focusing on classification problems and learning how to evaluate your models using tools like the AUC-ROC curve. You’ll apply these skills to practical case studies, gaining insight into real-world use cases such as employee attrition prediction. The course then introduces the Naive Bayes classifier, teaching you how to apply probabilistic methods for fast, efficient predictions, before finishing with decision trees. You’ll understand key concepts like entropy and the Gini index and practice hyperparameter tuning to optimize your models for accuracy. By the end of this 5-module course, you will have: • Gained confidence in preparing and analyzing datasets with EDA techniques. • Implemented linear and logistic regression for predictive and classification tasks. • Applied Naive Bayes and decision trees to solve practical machine learning problems. • Built the skills to take on more advanced machine learning projects. This course is ideal for learners who already have some experience with Python and ML basics and want to strengthen their ability to model, analyze, and solve real-world data problems. Updated in May 2025, this course now includes Coursera Coach: An interactive learning companion that helps you test your knowledge, challenge assumptions, and deepen your understanding as you progress.

Syllabus

Exploratory Data Analysis

In this module, we will explore the importance of exploratory data analysis (EDA) in the data science process. You will learn various tools and processes to uncover patterns, detect anomalies, and summarize key features of your data. The module includes several hands-on projects, allowing you to apply EDA techniques to real-world datasets.

Linear Regression

In this module, we will dive deep into linear regression, a core machine learning technique. You will gain a comprehensive understanding of its underlying concepts, including cost functions and gradient descent. Through hands-on projects, you'll build and optimize models using real-world data, focusing on both theoretical foundations and practical applications.

Logistic Regression

In this module, we will introduce you to logistic regression, an essential algorithm for binary classification problems. You will explore how to prepare data, build models, and assess their performance. Additionally, you will learn how to optimize logistic regression models using techniques such as AUC-ROC and feature engineering.

Naive Bayes Classification Algorithm

In this module, we will cover the Naive Bayes classification algorithm, focusing on its probabilistic nature and applications in classification tasks. Through real-world case studies, such as employee attrition prediction, you will learn how to build and optimize Naive Bayes models effectively.

Decision Tree Classifier

In this module, we will introduce decision tree classifiers, focusing on how they work and their advantages in classification tasks. You will explore key concepts such as the Gini Index, Entropy, and pruning. By the end of this module, you will be able to apply decision trees to real-world datasets and optimize them for improved model performance.