ML-University.of.Washington ✅

Build Intelligent Applications - ML - University of Washington - Coursera

Course 1 - Machine Learning Foundations: A Case Study Approach

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

Course 2 - Machine Learning: Regression

• Welcome
• Simple Linear Regression
• Multiple Regression
• Assessing Performance
• Ridge Regression
• Feature Selection & Lasso
• Nearest Neighbors & Kernel Regression
• Closing Remarks

Course 3 - Machine Learning: Classification

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

Course 4 - Machine Learning: Clustering & Retrieval

• Welcome
• Nearest Neighbor Search
• Clustering with k-means
• Mixture Models
• Mixed Membership Modeling via Latent Dirichlet Allocation
• Hierarchical Clustering & Closing Remarks

Taught by:

• Carlos Guestrin, Amazon Professor of Machine Learning
• Emily Fox, Amazon Professor of Machine Learning

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Difficulty 🌕🌕🌕🌕🌕🌕🌑🌑🌑🌑

Created By Bilal Cagiran | E-Mail | Github | LinkedIn | CodePen | Blog/Site | FreeCodeCamp