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Marketing Analytics Using Machine Learning Techniques

Name: Marketing Analytics Using Machine Learning Techniques
Price: 59 USD
Author: Asish Biswas

Gain insights into applied machine learning for marketing analytics. Explore data science techniques, create predictive models with Python libraries, and drive results with data-driven decisions.

Created by Asish Biswas

Marketing Analytics Using Machine Learning Techniques

What's included

38 lessons across 7 modules
4 quizzes with instant feedback
on every lesson

Join 3.1 million developers at

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

Learning Roadmap

38 Lessons4 Quizzes5 Challenges

Introduction

Get familiar with essential machine learning skills for marketing analytics, including data exploration, model building, and evaluation.

Getting Started

Data Manipulation

Get started with data exploration, wrangling, and modeling using pandas for marketing analytics.

Data Exploration

Data Wrangling

Data Modeling

Predicting Customer Revenue

8 Lessons

Examine predicting customer revenue using linear regression, dataset exploration, feature engineering, model building, and evaluation.

Customer Segmentation

9 Lessons

Grasp the fundamentals of customer segmentation via clustering techniques, feature engineering, and PCA.

Predicting Customer Churn

9 Lessons

Map out the steps for predicting customer churn using data analysis and machine learning.

Predicting Customer Lifetime Value (CLV)

7 Lessons

Follow the process of analyzing and predicting Customer Lifetime Value using data and machine learning.

Conclusion

Learn how to improve marketing strategies using machine learning for predictions and segmentation.

Course Summary

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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 specific topics such as customer revenue prediction using Linear Regression, customer segmentation using the K-Means Algorithm, customer churn prediction using Logistic Regression, and Customer Lifetime Value (CLV) analysis and prediction. Whether you're a marketer or business professional, this course will give you the skills to make data-driven decisions and drive results. You’ll have hands-on experience predicting future revenue, segmenting your customers into different groups, and predicting customer churn

ABOUT THE AUTHOR

Asish Biswas

I am an experienced Data Engineer with a passion for building data-driven solutions. With over a decade of experience building end-to-end machine learning and data analytic products, I'm here to share my knowledge to help you start an analytic career.

Learn more about Asish

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