Course Outline

• Introduction to Machine Learning
  • What is ML?
  • Applications of ML
  • Why ML is the Future
  • Types of ML
  • Installing Python and Anaconda (MAC & Windows)
• Data Preprocessing
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming: classes & objects
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
• Regression
  • Simple Linear Regression
  • Dataset + Business Problem Description
  • Simple Linear Regression in Python
  • Multiple Linear Regression
  • Multiple Linear Regression in Python
  • Polynomial Regression
  • Polynomial Regression in Python
  • Support Vector Regression (SVR)
  • SVR in Python
  • Decision Tree Regression in Python
  • Random Forest Regression in Python
• Classification
  • Logistic Regression in Python
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Confusion Matrix
  • CAP Curve
• Clustering
  • K-Means Clustering in Python
  • Hierarchical Clustering in Python
• Association Rule Learning
  • Association Rule Learning in Python
  • Apriori
• Reinforcement Learning
  • Upper Confidence Bound (UCB)
  • Thompson Sampling
• Natural Language Processing
  • Natural Language Processing in Python
• Deep Learning
  • Artificial Neural Networks in Python
  • Convolutional Neural Networks in Python