University of Colorado Boulder - Machine Learning for Marketers
- Offered byCoursera
Machine Learning for Marketers at Coursera Overview
Duration | 22 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning for Marketers at Coursera Highlights
- Earn a certificate after completion of the course
- Assignment and projects for practice
- Financial aid available
Machine Learning for Marketers at Coursera Course details
- Campaign Analysis and Testing
- Predictive Analytics in Marketing
- Machine Learning
- Personalized Marketing Strategies
- Advanced Machine Learning Techniques
- Customer Behavior and Preference Analysis
- "Machine Learning for Marketers" is an advanced course tailored for professionals looking to integrate machine learning into their marketing strategie
- 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
Machine Learning for Marketers at Coursera Curriculum
Supervised Learning for Strategic Marketing
Supervised Learning
Prediction Model Performance Evaluation
Cross-Validation (CV)
K-Fold Cross-Validation (CV)
Improving Predictions with Supervised Learning
Visualizing Model Comparisons
CART Trees: Finding an Informative Split
A Note About Readings & R-Scripts
Model Drift
Root Mean Squared Error (RMSE)
CART Trees
R-Scripts - CART Trees
Mall_Customers.csv
Supervised Learning Quiz
Prediction Model Performance Evaluation Quiz
Cross-Validation (CV) Quiz
K-Fold Cross-Validation (CV) Quiz
Improving Predictions with Supervised Learning Quiz
Visualizing Model Comparisons Quiz
CART Trees: Finding an Informative Split Quiz
Module 1 Graded Quiz
CART Tree Analysis
Regression Trees: Growing Larger Trees
Reading CART Trees
Classification Trees: Part 1
Classification Trees: Part 2
Using Classification Trees and Causal Trees for Prediction, Explanation, and Targeting
Optimal Targeting Using Causal Trees
Regression Trees
R-Scripts - Supervised Learning - CART and Random Forest
TelcoChurn.csv
RandomForest
Causal Trees, Forests, & HTE Models
Regression Trees: Growing Larger Trees Quiz
Reading CART Trees Quiz
Classification Trees: Part 1 Quiz
Classification Trees: Part 2 Quiz
Using Classification Trees and Causal Trees for Prediction, Explanation, and Targeting Quiz
Optimal Targeting Using Causal Trees Quiz
Module 2 Graded Quiz
Improving the Accuracy of Predictions
Accuracy and Confusion Matrix for a Classifier
Other Supervised Learning Algorithms: RF, GBM
Multiple Adaptive Regression Splines (MARS)
Support Vector Machines (SVMs)
K Nearest Neighbors (KNN), Naïve Bayes (NB), and Deep Learning
Combining and Comparing Models: Stacked Learning and Model Comparisons
Confusion Matrix
R-Scripts - Supervised Learning for Continuous Dependent Variable (“Regression”)
Mall_Customers.csv
R-Scripts - Supervised Learning for Discrete Dependent Variable (“Classification”)
TelcoChurn.csv
Supervised Machine Learning Algorithms
Multiple Adaptive Regression Splines (MARS)
Support Vector Machines (SVMs)
Naïve Bayes, Neural Networks, and Deep Learning
Superlearning/ Stacked Learning
Accuracy and Confusion Matrix for a Classifier Quiz
Other Supervised Learning Algorithms: RF, GBM Quiz
Multiple Adaptive Regression Splines (MARS) Quiz
Support Vector Machines (SVMs) Quiz
K Nearest Neighbors (KNN), Naïve Bayes (NB), and Deep Learning Quiz
Combining and Comparing Models: Stacked Learning and Model Comparisons Quiz
Module 3 Graded Quiz
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