Machine Learning for Marketers

Machine Learning for Marketers

This course is part of Data Science for Marketing Specialization

Instructors: A.W. Lukens

Access provided by Masterflex LLC, Part of Avantor

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Apply machine learning to improve targeted marketing & strategic decision-making. Analyze & forecast customer behaviors using advanced algorithms.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

29 assignments

Taught in English

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This course is part of the Data Science 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

"Machine Learning for Marketers" is an advanced course tailored for professionals looking to integrate machine learning into their marketing strategies. This course uniquely focuses on both predictive analytics and decision-making, using supervised learning methods to analyze and forecast customer behavior. Participants will learn to implement advanced machine learning techniques, enhancing the accuracy of predictions and informing better marketing decisions. The course also covers campaign analysis through rigorous testing methods like cross-validation, ensuring the reliability of marketing strategies. A key feature of this course is its coverage of unsupervised learning algorithms, enabling learners to uncover hidden patterns in marketing data for sophisticated customer segmentation and market analysis. Additionally, the course discusses optimizing product positioning using dimensionality reduction techniques and improving personalized customer experiences through recommender system technology.

Apply supervised learning to discover and visualize patterns in customer behaviors and refine your marketing strategies. This module equips you with the tools to improve predictive accuracy and tailor marketing efforts for maximum impact.

What's included

7 videos6 readings8 assignments

7 videos Total 31 minutes

Supervised Learning 3 minutes
Prediction Model Performance Evaluation 2 minutes
Cross-Validation (CV) 3 minutes
K-Fold Cross-Validation (CV) 5 minutes
Improving Predictions with Supervised Learning 5 minutes
Visualizing Model Comparisons 6 minutes
CART Trees: Finding an Informative Split 6 minutes

6 readings Total 88 minutes

A Note About Readings & R-Scripts 1 minute
Model Drift 20 minutes
Root Mean Squared Error (RMSE) 10 minutes
CART Trees 45 minutes
R-Scripts - CART Trees 10 minutes
Mall_Customers.csv 2 minutes

8 assignments Total 111 minutes

Module 1 Graded Quiz 30 minutes
Supervised Learning Quiz 15 minutes
Prediction Model Performance Evaluation Quiz 6 minutes
Cross-Validation (CV) Quiz 15 minutes
K-Fold Cross-Validation (CV) Quiz 9 minutes
Improving Predictions with Supervised Learning Quiz 12 minutes
Visualizing Model Comparisons Quiz 9 minutes
CART Trees: Finding an Informative Split Quiz 15 minutes

Transform your marketing approach with CART tree analysis. Learn to segment customers precisely and predict campaign responses, optimizing your marketing resources and strategies for better customer engagement and retention.

What's included

6 videos5 readings7 assignments

6 videos Total 36 minutes

Regression Trees: Growing Larger Trees 7 minutes
Reading CART Trees 7 minutes
Classification Trees: Part 1 5 minutes
Classification Trees: Part 2 6 minutes
Using Classification Trees and Causal Trees for Prediction, Explanation, and Targeting 5 minutes
Optimal Targeting Using Causal Trees 6 minutes

5 readings Total 165 minutes

Regression Trees 45 minutes
R-Scripts - Supervised Learning - CART and Random Forest 10 minutes
TelcoChurn.csv 5 minutes
RandomForest 60 minutes
Causal Trees, Forests, & HTE Models 45 minutes

7 assignments Total 117 minutes

Module 2 Graded Quiz 30 minutes
Regression Trees: Growing Larger Trees Quiz 15 minutes
Reading CART Trees Quiz 12 minutes
Classification Trees: Part 1 Quiz 15 minutes
Classification Trees: Part 2 Quiz 15 minutes
Using Classification Trees and Causal Trees for Prediction, Explanation, and Targeting Quiz 15 minutes
Optimal Targeting Using Causal Trees Quiz 15 minutes

Advance your predictive capabilities in marketing. This module teaches you to enhance model accuracy, tackle data imbalances, and select the most effective strategies, ensuring your marketing campaigns hit the mark every time.

What's included

6 videos10 readings7 assignments

6 videos Total 28 minutes

Accuracy and Confusion Matrix for a Classifier 6 minutes
Other Supervised Learning Algorithms: RF, GBM 3 minutes
Multiple Adaptive Regression Splines (MARS) 3 minutes
Support Vector Machines (SVMs) 4 minutes
K Nearest Neighbors (KNN), Naïve Bayes (NB), and Deep Learning 6 minutes
Combining and Comparing Models: Stacked Learning and Model Comparisons 6 minutes

10 readings Total 204 minutes

Confusion Matrix 20 minutes
R-Scripts - Supervised Learning for Continuous Dependent Variable (“Regression”) 10 minutes
Mall_Customers.csv 2 minutes
R-Scripts - Supervised Learning for Discrete Dependent Variable (“Classification”) 10 minutes
TelcoChurn.csv 2 minutes
Supervised Machine Learning Algorithms 60 minutes
Multiple Adaptive Regression Splines (MARS) 20 minutes
Support Vector Machines (SVMs) 30 minutes
Naïve Bayes, Neural Networks, and Deep Learning 30 minutes
Superlearning/ Stacked Learning 20 minutes

7 assignments Total 120 minutes

Module 3 Graded Quiz 30 minutes
Accuracy and Confusion Matrix for a Classifier Quiz 15 minutes
Other Supervised Learning Algorithms: RF, GBM Quiz 15 minutes
Multiple Adaptive Regression Splines (MARS) Quiz 15 minutes
Support Vector Machines (SVMs) Quiz 15 minutes
K Nearest Neighbors (KNN), Naïve Bayes (NB), and Deep Learning Quiz 15 minutes
Combining and Comparing Models: Stacked Learning and Model Comparisons Quiz 15 minutes

Apply unsupervised learning to uncover hidden patterns in marketing data. Use techniques such as Principal Components Analysis for insightful segmentation and personalized marketing strategies that can elevate customer engagement and lifetime value (LTV).

What's included

6 videos9 readings7 assignments

6 videos Total 37 minutes

Unsupervised Learning: Introduction 6 minutes
Principal Components Analysis (PCA) and Biplots 4 minutes
Interpreting Biplots 5 minutes
Perceptual Maps 7 minutes
Hidden Causes: Factor Analysis 7 minutes
Recommendation Systems: Main Ideas 8 minutes

9 readings Total 260 minutes

Clustering 10 minutes
R-Scripts - Create and Display K-Means Clusters 10 minutes
Principal Components Analysis (PCA), Perceptual Maps, and Nonlinear Alternatives to PCA 70 minutes
R-Scripts - Correlations 10 minutes
R-Scripts - Principal Components Analysis (PCA) and Biplots 10 minutes
Factor Analysis 80 minutes
R-Scripts - Factor Analysis 10 minutes
Recommendation Engines and Matrix Factorization 50 minutes
R-Scripts - Matrix Factorization For Recommendation Systems 10 minutes