Mastering Machine Learning Algorithms using Python

Updated in May 2025. This course now 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. In this course, you will explore advanced machine learning algorithms and unsupervised learning techniques to enhance your model-building skills. You’ll learn how to improve model performance using ensemble methods like Random Forest, apply Support Vector Machines (SVM) for complex classification tasks, and reduce dimensionality with techniques like Principal Component Analysis (PCA). By the end of the course, you'll also have an understanding of unsupervised learning through K-Means clustering and an introduction to deep learning. The course begins with an introduction to ensemble learning using Random Forests, where you'll understand how this method improves predictive model accuracy and reduces overfitting. You will then dive into Support Vector Machines (SVM), learning to apply this powerful technique to solve complex classification problems, including how to optimize SVM models for better performance. Next, you will explore Principal Component Analysis (PCA) to reduce dimensionality and optimize model performance, enabling you to work with high-dimensional datasets more effectively. You will also learn about K-Means clustering for unsupervised learning, focusing on how to detect patterns and anomalies in unlabeled data. Finally, the course concludes with an introduction to deep learning, exploring how this rapidly growing field builds on traditional machine learning concepts. You will gain an understanding of how deep learning can be applied to a range of complex tasks such as image and speech recognition. This course is ideal for learners with prior experience in machine learning and Python who are ready to tackle more advanced topics. Familiarity with statistics and linear algebra is helpful.

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.