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Build Intelligent Applications - ML - University of Washington - Coursera

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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