Machine Learning with Python & R = Data Science

A brief description of what this project

Features

Features

• Part 1: Data Preprocessing in R

• Part 2: Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Models Performance
  • Regression Model Selection in Python
  • Regression Model Selection in R

• Part 3: Classification

  • Logistic Regression i
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Classification Model Selection in Python
  • Evaluating Classification Models Performance

• Part 4: Clustering

  • K - Means Clustering
  • Hierarchical Clustering

• Part 5: Association Rule Learning

  • Apriori
  • Eclat

• Part 6: Reinforcement Learning

  • Upper Confidence Bound (UCB)
  • Thompson Sampling

• Part 7: Natural Language Processing

  • Part 8: Deep Learning
  • Artificial Neural Networks
  • Convolutional Neural Networks

• Part 9: Dimensionality Reduction

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

• Part 10: Model Selection & Boosting

  • Model Selection
  • XGBoost
  • Bonus Lectures