Machine Learning for Marketers

Machine Learning for Marketers

本课程是 Data Science for Marketing 专项课程的一部分

位教师：A.W. Lukens

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4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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

您将获得的技能

Strategic Marketing
Customer Analysis
Data-Driven Decision-Making
Customer Insights
Supervised Learning
Campaign Management
Customer experience improvement
Marketing Analytics
Model Evaluation
Unsupervised Learning
AI Personalization
Marketing Strategies
Classification And Regression Tree (CART)
Predictive Analytics
Applied Machine Learning
Machine Learning
Personalized Service
Dimensionality Reduction
Marketing Effectiveness
技能部分已折叠。显示 8 项技能，共 19 项。

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作业

29 项作业

授课语言：英语（English）

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本课程是 Data Science for Marketing 专项课程专项课程的一部分

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该课程共有4个模块

"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.

涵盖的内容

7个视频6篇阅读材料8个作业

7个视频总计31分钟

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

6篇阅读材料总计88分钟

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

8个作业总计111分钟

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

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.

涵盖的内容

6个视频5篇阅读材料7个作业

6个视频总计36分钟

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

5篇阅读材料总计165分钟

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

7个作业总计117分钟

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

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.

涵盖的内容

6个视频10篇阅读材料7个作业

6个视频总计28分钟

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

10篇阅读材料总计204分钟

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

7个作业总计120分钟

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

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).

涵盖的内容

6个视频9篇阅读材料7个作业

6个视频总计37分钟

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

9篇阅读材料总计260分钟

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