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Customer Insights and Personalization with Machine Learning

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Customer Insights and Personalization with Machine Learning

This course is part of Machine Learning and Generative AI for Marketing Specialization

Instructor: Packt - Course Instructors

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply sentiment analysis techniques to extract actionable customer insights
Build predictive models and run A/B tests to improve engagement outcomes
Design and implement ML-based customer segmentation and recommendation systems

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Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Machine Learning and Generative AI for Marketing Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

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.

What's included

1 video8 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

8 readingsTotal 46 minutes

Introduction5 minutes
Practical Example Twitter Airline Sentiment Dataset7 minutes
Addressing Class Imbalance6 minutes
Feature Engineering6 minutes
Understanding Misclassifications5 minutes
Evaluating Model Performance6 minutes
Collecting Data from Other Platforms6 minutes
Temporal Trends Tracking the Brand Narrative5 minutes

1 assignmentTotal 16 minutes

Customer Insight Through Sentiment Analysis16 minutes

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.

What's included

1 video7 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

7 readingsTotal 44 minutes

Introduction5 minutes
Target and Feature Variables5 minutes
Predicting and Evaluating Random Forest Model7 minutes
Gradient Boosted Decision Tree (GBDT) Modeling5 minutes
Predicting Customer Conversion with Deep Learning Algorithms4 minutes
Training the Wide Neural Network Model6 minutes
Deep Neural Network Modeling12 minutes

1 assignmentTotal 16 minutes

Evaluating Models and Strategies for Customer Engagement16 minutes

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.

What's included

1 video5 readings1 assignment

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.

What's included

1 video6 readings1 assignment

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Instructor

Packt - Course Instructors

Packt

1,926 Courses558,431 learners

Offered by

Packt

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.