Overview

This course explores how machine learning can be used to uncover deep customer insights and deliver personalized experiences at scale. It highlights the growing importance of data-driven decision-making in enhancing customer engagement and business performance. Learners will develop the ability to analyze customer sentiment, predict behaviors, and design targeted strategies using A/B testing and predictive analytics. By the end of the course, participants will be able to implement practical personalization techniques that improve customer satisfaction and retention. What sets this course apart is its balance between foundational concepts and real-world applications, including recommendation systems and customer segmentation. It bridges theory with hands-on approaches used in modern business environments. This course is ideal for data professionals, marketers, and business analysts looking to leverage machine learning for customer-centric strategies. A basic understanding of data analysis and machine learning concepts is recommended. 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.

Syllabus

Enhancing Customer Insight with Sentiment Analysis

This module introduces the fundamentals of sentiment analysis for marketing, using real-world social media data to uncover customer attitudes. Learners will explore techniques for handling class imbalance, feature engineering, and evaluating model performance. The module also covers strategies for collecting and analyzing sentiment data across multiple platforms and tracking temporal trends in brand perception.

Leveraging Predictive Analytics and A/B Testing for Customer Engagement

This module explores how to use predictive analytics and A/B testing to enhance customer engagement through data-driven marketing strategies. Learners will gain hands-on experience building and evaluating machine learning models, including random forests, gradient boosted decision trees, and neural networks, to predict customer behavior and optimize marketing outcomes.

Personalized Product Recommendations

This module explores the foundations and practical techniques behind personalized product recommendation systems. Learners will examine algorithms such as Apriori for association rule mining and collaborative filtering methods, including both user-based and item-based approaches. Through hands-on examples, you will gain the skills to implement and analyze recommendation strategies using real-world data.

Segmenting Customers with Machine Learning

This module introduces learners to customer segmentation using machine learning techniques, focusing on K-means clustering. You will explore how to segment customers based on purchase behaviors, demographic factors, and product interests, as well as how to preprocess data and evaluate clustering results.