Author: Rodrigo de Alvarenga Mattos
February 9, 2022
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
University of Washington Machine Learning Specialization Website
- 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
- Machine Learning: Regression
- Simple Linear Regression
- Multiple Regression
- Assessing Performance
- Ridge Regression
- Feature Selection & Lasso
- Nearest Neighbors & Kernel Regression
- Machine Learning: Classification
- Linear Classifiers & Logistic Regression
- Learning Linear Classifiers
- Overfitting & Regularization in Logistic Regression
- Decision Trees
- Preventing Overfitting in Decision Trees
- Handling Missing Data
- Machine Learning: Clustering & Retrieval
- Nearest Neighbor Search
- Clustering with k-means
- Mixture Models
- Mixed Membership Modeling via Latent Dirichlet Allocation
- Hierarchical Clustering & Closing Remarks