Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

This course equips learners with practical skills to implement machine learning in real-world scenarios. You will gain expertise in classifying images, scoring opinions, and recommending products or media, applying essential ML strategies. Through hands-on examples, the course enhances your ability to translate data into actionable insights. You'll improve model accuracy, evaluate performance, and deploy solutions for tangible outcomes. Combining theory with applied projects, the course emphasizes practical problem-solving, ethical data usage, and model optimization strategies. The lessons are grounded in real datasets and industry-relevant techniques. Ideal for aspiring data scientists, analysts, and developers with basic Python or ML knowledge. No prior advanced ML experience is required. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. This Specialization is based on the book, Machine Learning For Dummies, by John Paul Mueller. From Machine Learning For Dummies Copyright © 2026 by John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Used by arrangement with John Wiley & Sons, Inc.

Syllabus

Classifying Images

This module introduces key techniques for improving image classification models, including image augmentation, regularization methods, and the use of convolutional neural networks (CNNs). Learners will also explore transfer learning and practical strategies for building and adapting image classifiers using deep learning frameworks.

Scoring Opinions and Sentiments

This module introduces key techniques for converting text into numerical data for natural language processing, including bag-of-words, tokenization, and self-attention models. Learners will explore how machines interpret, process, and classify text using both traditional and deep learning approaches, and how pre-trained models can enhance sentiment analysis. Practical challenges such as character encoding and text representation are also addressed.

Recommending Products and Movies

This module introduces the fundamentals of recommender systems, focusing on how collaborative filtering and Singular Value Decomposition (SVD) are used to personalize product and movie suggestions. Learners will explore real-world datasets, understand similarity measures, and discover how advanced techniques extract meaningful patterns from user behavior data.

Ten Ways to Improve Your Machine Learning Models

This module explores practical strategies to enhance machine learning model performance, including analyzing learning curves, selecting appropriate evaluation metrics, and applying feature engineering techniques. Learners will gain hands-on experience with cross-validation and hyperparameter optimization to address bias and variance issues.

Ten Guidelines for Ethical Data Usage

This module examines the principles of ethical data handling, focusing on legal compliance and the challenges of using personal information in AI training. Learners will explore common misconceptions about data ownership, pitfalls in inferring sensitive information, and strategies for recognizing rare but impactful events in data-driven systems.