Machine Learning

→ INTRODUCTION

  • Machine Learning Introduction
  • Supervised Learning concepts
  • Unsupervised Learning concepts

→ REGRESSION

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Linear Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Models Performance

→ CLASSIFICATION

  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Na├»ve Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classification Models Performance

→ CLUSTERING

  • K-Means Clustering
  • Hierarchical Clustering

→ ASSOCIATION RULE LEARNING

  • Apriori
  • ECLAT

→ REINFORCEMENT LEARNING

  • Upper Confidence Bound (UCB)
  • Thompson Sampling

→NATURALLANGUAGEPROCESSING

→DEEP LEARNING

  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Self-Organizing Maps
  • Boltzmann Machines
  • Auto Encoders

→DIMENSIONALITY REDUCTION

  • Principal Component Analysis(PCA)
  • Linear Discriminant Analysis(LDA)
  • Kernel PCA

→Model Selection & Boosting

  • Model selection
  • XGBoost

→TIME SERIES ANALYSIS

  • Trends & Seasonality
  • ARIMA,GARCH
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