• Key Points
• Supervised Machine Learning
• Unsupervised Machine Learning
• Reinforcement Machine Learning

• Introduction
• Introduction to the Data
• K-nearest Neighbors
• Euclidean Distance
• Calculate Distance for All Observations
• Randomizing and Sorting
• Average Price
• Functions for Prediction

• Testing Quality of Predictions
• Error Metrics
• Mean Squared Error
• Training Another Model
• Root Mean Squared Error
• Comparing MAE and RMSE

• Recap
• Removing Features
• Handling Missing Values
• Normalize Columns
• Euclidean Distance for Multivariate Case
• Introduction to Scikit-learn
• Fitting a Model and Making Predictions
• Calculating MSE using Scikit-Learn
• Using More Features
• Using All Features

• Recap
• Hyperparameter Optimization
• Expanding Grid Search
• Visualizing Hyperparameter Values

• Concept
• Holdout Validation
• K-Fold Cross Validation

Predicting Car Prices

• Calculus For Machine Learning
• Understanding Linear and Nonlinear Functions
• Understanding Limits
• Finding Extreme Points

• Linear Algebra For Machine Learning
• Linear Systems
• Vectors
• Matrix Algebra
• Solution Sets

• Linear Regression For Machine Learning
• The Linear Regression Model
• Feature Selection
• Gradient Descent
• Ordinary Least Squares
• Processing And Transforming Features
• Guided Project: Predicting House Sale Prices

• Logistic Regression
• Introduction to Evaluating Binary Classifiers
• Multiclass Classification
• Overfitting
• Clustering Basics
• K-means Clustering
• Guided Project: Predicting the Stock Market

• Decision Tree
• Why use Decision Trees?
• Decision Tree Terminologies
• How Does the Decision Tree Algorithm Work
• Pruning: Getting an Optimal Decision Tree
• Advantages of the Decision Tree
• Disadvantages of the Decision Tree
• Python Implementation of Decision Tree
• Guided Project: Predicting Bike Rentals
• References and Bibliography