Marketing Analytics Using Machine Learning Techniques - AI-Powered Learning for Developers

Beginner

38 Lessons

Certificate of Completion

AI-POWERED

Explanations

This course includes

108 Playgrounds

5 Challenges

47 Illustrations

This course includes

108 Playgrounds

5 Challenges

47 Illustrations

Course Overview

In this course, you’ll learn applied machine learning in marketing analytics and cover modern data science techniques such as data exploration, data preprocessing, feature engineering and evaluation. You’ll gain hands-on experience with the Python libraries pandas, Scikit-learn, and seaborn, and learn how to use them to perform data wrangling, data analysis, create predictive models, and visualize your results. This course will introduce you to basic data manipulation techniques. Further, you’ll cover spec...Show More

TAKEAWAY SKILLS

Python

Data Manipulation

What You'll Learn

A working knowledge of using Python libraries such as pandas, scikit-learn, and seaborn for data analysis, visualization, and building machine learning models

An understanding of marketing analytics concepts and applying them in Python

The ability to create and interpret linear regression models for customer revenue prediction

A working knowledge of the K-Means Algorithm and its applications in customer segmentation

An understanding of logistic regression and its use for customer churn prediction

Proficiency in customer lifetime value (CLV) analysis and prediction

A working knowledge of making data-driven decisions and optimizing marketing strategies

Course Content

Introduction

Getting Started

Data Manipulation

Data Exploration Data Wrangling Data Modeling

Predicting Customer Revenue

Introduction to Linear Regression Explore the Dataset Feature Engineering Relationships Between Dependent and Independent Variables Model Building and Evaluation Challenge: Revenue Prediction Solution: Revenue Prediction Quiz: Revenue Prediction

Customer Segmentation

Introduction to Customer Segmentation Explore the Dataset Feature Engineering Customer Segmentation Interpreting the Segmentation Result Dimensionality Reduction with PCA Challenge: Customer Segmentation Solution: Customer Segmentation Quiz: Customer Segmentation

Predicting Customer Churn

Introduction to Customer Churn Explore the Dataset Feature Engineering: One-Hot Encoding Feature Engineering: Remove Redundant Features Churn Prediction with Logistic Regression Churn Prediction with Decision Tree Challenge: Customer Churn Solution: Customer Churn Quiz: Customer Churn

Predicting Customer Lifetime Value (CLV)

7 Lessons

Conclusion

1 Lesson

COURSE AUTHOR

How You'll Learn

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