ML Washington University

Machine Learning Specialization Offered By the University Of Washington at Coursera

All the slides, programming assignments and datasets can be found in the table below

Content

Course Title	🔗 My Repository
1. Foundations	More Info & Resources
2. Regression	More Info & Resources
3. Classification	More Info & Resources
4. Clustering & Retrieval	More Info & Resources

Machine Learning Foundations: A Case Study Approach

• Regression: Predicting House Prices
• Classification: Analyzing Sentiment
• Clustering and Similarity: Retrieving Documents
• Recommending Products
• Deep Learning: Searching for Images
• 🔗 Coursera

Machine Learning: Regression

• Simple Linear Regression
• Multiple Regression
• Assessing Performance
• Ridge Regression
• Feature Selection and Lasso
• Nearest Neighbors and Kernel Regression
• 🔗 Coursera

Machine Learning: Classification

• Linear Classifiers and Logistic Regression
• Learning Linear Classifiers
• Overfitting and Regularization in Logistic Regression
• Decision Trees
• Preventing Overfitting in Decision Trees
• Handling Missing Data
• Boosting
• Precision-Recall
• Scaling to Huge Datasets and Online Learning
• 🔗 Coursera

Machine Learning: Clustering & Retrieval

• Neareat Neighbor Search
• Clustering with k-means
• Mixture Models
• Latent Dirichlet Allocations
• Hierarchical Clustering
• 🔗 Coursera