Y. Emre Yildiz

Notes - intro. to machine learning

Introduction
1. Why machine learning? [Notebook] [Shqip]
2. A First Application: Classifying iris species [Notebook] [Shqip]
Supervised Learning
1. Classification and Regression [Notebook] [Shqip]
2. Generalization, Overfitting and Underfitting [Notebook] [Shqip]
3. Supervised Machine Learning Algorithms [Notebook] [Shqip]
4. k-Nearest Neighbor [Notebook] [Shqip]
5. Linear models [Notebook] [Shqip]
6. Naive Bayes Classifiers [Notebook] [Shqip]
7. Decision trees [Notebook] [Shqip] [Practice]
8. Ensembles of Decision Trees [Notebook] [Shqip][Practice]
9. Kernelized Support Vector Machines [Notebook] [Shqip]
10. Neural Networks (Deep Learning) [Notebook] [Shqip]
11. Uncertainty estimates from classifiers [Notebook] [Shqip]
Unsupervised Learning and Preprocessing
1. Preprocessing and Scaling [Notebook] [Shqip]
2. Dimensionality Reduction, Feature Extraction and Manifold Learning [Notebook] [Shqip]
3. Clustering [Notebook] [Shqip]
Representing Data and Engineering Features
1. Categorical Variables
2. Binning, Discretization, Linear Models and Trees
3. Interactions and Polynomials
4. Univariate Non-linear transformations
5. Automatic Feature Selection
6. Utilizing Expert Knowledge
Model evaluation and improvement
1. Cross-validation
2. Grid Search
3. Evaluation Metrics and scoring
Algorithm Chains and Pipelines
1. Parameter Selection with Preprocessing
Working with Text Data
1. Types of data represented as strings
2. Topic Modeling and Document Clustering

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