Machine Learning with Python

IBM via Coursera

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

20 hours 2 minutes
Sessions

On-Demand
Subtitles

Arabic, French, Portuguese, Italian, German, Russian, English, Spanish, Korean, Thai, Indonesian, Farsi, Kazakh, Hindi, Swedish, Greek, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish, Hungarian, Bengali, Pashto, Urdu, Azerbaijani

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Overview

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Python is a core skill in machine learning, and this course equips you with the tools to apply it effectively. You’ll learn key ML concepts, build models with scikit-learn, and gain hands-on experience using Jupyter Notebooks. Start with regression techniques like linear, multiple linear, polynomial, and logistic regression. Then move into supervised models such as decision trees, K-Nearest Neighbors, and support vector machines. You’ll also explore unsupervised learning, including clustering methods and dimensionality reduction with PCA, t-SNE, and UMAP. Through real-world labs, you’ll practice model evaluation, cross-validation, regularization, and pipeline optimization. A final project on rainfall prediction and a course-wide exam will help you apply and reinforce your skills. Enroll now to start building machine learning models with confidence using Python.

Syllabus

Introduction to Machine Learning

In this module, you will explore foundational machine learning concepts that prepare you for hands-on modeling with Python. You will explain the relevance of Python and scikit-learn in machine learning, summarize the IBM AI Engineering certification path, and classify common types of learning algorithms. You’ll outline the stages of the machine learning model lifecycle and describe what a typical day looks like for a machine learning engineer. You will also compare key roles in the AI field, identify widely used open-source tools, and learn to utilize scikit-learn to build and evaluate simple models.

Linear and Logistic Regression

In this module, you will explore two essential regression techniques used in machine learning—linear and logistic regression. You’ll explain the role of regression in predicting outcomes, describe the differences between simple and multiple linear regression, and apply both using scikit-learn on real-world data. You will also interpret how polynomial and non-linear regression models capture complex patterns. The module introduces logistic regression as a classification method and guides you in training and testing classification models effectively. To support your learning, you’ll receive a Cheat Sheet: Linear and Logistic Regression that summarizes key concepts, formulas, and use cases.

Building Supervised Learning Models

In this module, you will build and evaluate a range of supervised machine learning models to solve both classification and regression problems. You’ll start by describing how classification models predict categorical outcomes, and implement multi-class classification strategies using real-world data. You’ll then explore how decision trees make predictions and apply them to both classification and regression tasks. The module also covers using support vector machines (SVM) for fraud detection, applying K-Nearest Neighbors (KNN) for customer classification, and training ensemble models like Random Forest and XGBoost to improve accuracy and efficiency. You’ll differentiate bias and variance in model performance and explore how ensemble methods help balance this tradeoff. To support your learning, you’ll receive a Cheat Sheet: Building Supervised Learning Models with key terms, model types, and evaluation tips.

Building Unsupervised Learning Models

In this module, you will learn how unsupervised learning techniques uncover hidden patterns in data without using labeled responses. You’ll describe clustering concepts and apply K-Means to real-world customer segmentation tasks. You’ll also compare DBSCAN and HDBSCAN models to identify dense clusters in spatial data. Moving beyond clustering, you’ll explore dimensionality reduction as a tool for simplifying high-dimensional datasets. You’ll apply PCA to uncover key components and use advanced techniques like t-SNE and UMAP to visualize data structure. To support your learning, you’ll receive a Cheat Sheet: Building Unsupervised Learning Models, highlighting core methods, practical use cases, and comparison guidelines.

Evaluating and Validating Machine Learning Models

In this module, you will learn how to assess the effectiveness of machine learning models using industry-standard evaluation and validation techniques. You’ll explain key classification and regression metrics, evaluate models using real-world data, and interpret results with tools like confusion matrices and feature importance charts. You'll explore how to assess clustering quality in unsupervised learning and apply cross-validation to reduce overfitting. The module also introduces regularization methods to improve model generalization and reduce feature complexity. Finally, you'll build complete machine learning pipelines and optimize them with GridSearchCV, while identifying common pitfalls like data leakage. To support your learning, you’ll receive a Cheat Sheet: Evaluating and Validating Machine Learning Models covering key metrics, techniques, and model tuning strategies.

Final Project and Exam

In this final module, you will apply and demonstrate the full range of skills you have gained throughout the course. You will start with a practice project using the Titanic dataset to build and optimize classification models using pipelines, cross-validation, and hyperparameter tuning. Then, you will complete the final project by developing a rainfall prediction classifier using historical weather data. This includes data cleaning, feature engineering, model building, and evaluating performance. To conclude the course, you will take a graded final exam that tests your knowledge across all six modules. This module gives you the opportunity to showcase your learning in both practical and theoretical contexts.

Taught by

SAEED AGHABOZORGI

Reviews

4.2 rating, based on 4 Class Central reviews

4.7 rating at Coursera based on 17495 ratings

Start your review of Machine Learning with Python

Berbelek @berbelek

I really liked this one. One of the best IBM data science courses available. It introduces broad list of subjects and provides some simple code to help you start building your own solutions. Recommend!
Mehdi Ebrahimi

Instructor is clear and know what he is doing. course videos covers basic info and techniques of machine learning, further instructions are taught in lab assignments.
Anonymous

The ML course in Python offers a comprehensive introduction to machine learning, covering essential topics such as data preprocessing, model selection, and evaluation metrics. With hands-on coding exercises and real-world projects, it effectively bridges the gap between theory and practice. The course structure is well-organized, progressively building on concepts to enhance understanding. The instructors provide clear explanations and are responsive to questions, ensuring a supportive learning environment. Overall, it's an excellent resource for anyone looking to gain practical skills in machine learning using Python.
Teerath Kumar Menghwar

Amazing course. Well organised. Need to add more things.

I have not got certificate. But let's see, when I can get certificate.