Machine-Learning

Basic Machine Learning Tutorial using Python and R.

Supervised Learning

Part 1 - Data Preprocessing

1. categorical_data
2. data_preprocessing

Part 2 - Regression

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. SVR
5. Decision Tree Regression
6. Random Forest Regression
7. Regression Template

Part 3 - Classification

1. Logistic Regression
2. KNN
3. SVM
4. Kernel - SVM
5. Naive Bayes
6. Decision Tree Classification
7. Random Forest Classification
8. Classification Template

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Un-supervised Learning

Part 4 - Clustering

1. K-means Clustering
2. Hierarchical Clustering

Part 5 - Association Rule Learning

1. Apriori
2. Eclat

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Reinforcement Learning

Part 6 - Reinforcement Learning

1. Upper Confidence Bound (UCB)
2. Thompson Sampling

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Deep Learning

Part 7 - Natural Language Processing

1. NPL
2. NPL using google's BERT model

Part 8 - Deep Learning

1. Artificial Neural Network (ANN)
2. Convolution Neural Network (CNN)

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Part 9 - Dimensionality Reduction

1. Principle Component Analysis (PCA)
2. Linear Discriminant Analysis (LDA)
3. Kernel-PCA

Part 10 - Model Selection & Boosting

1. Comparing performance of regression models
2. Comparing performance of classification models
3. k-fold CrossValidation
4. Grid Search
5. XG Boost
6. CatBoost