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What you’ll learn
Understand and implement K-Nearest Neighbors in Python
Understand the limitations of KNN
User KNN to solve several binary and multiclass classification problems
Understand and implement Naive Bayes and General Bayes Classifiers in Python
Understand the limitations of Bayes Classifiers
Understand and implement a Decision Tree in Python
Understand and implement the Perceptron in Python
Understand the limitations of the Perceptron
Understand hyperparameters and how to apply cross-validation
Understand the concepts of feature extraction and feature selection
Understand the pros and cons between classic machine learning methods and deep learning
Use Sci-Kit Learn
Implement a machine learning web service

“Introduction and Review
Introduction and Outline
Review of Important Concepts
Where to get the Code and Data
How to Succeed in this Course

“K-Nearest Neighbor
K-Nearest Neighbor Intuition
K-Nearest Neighbor Concepts
KNN in Code with MNIST
When KNN Can Fail
KNN for the XOR Problem
KNN for the Donut Problem
Effect of K
KNN Exercise

“Naive Bayes and Bayes Classifiers
Bayes Classifier Intuition (Continuous)
Bayes Classifier Intuition (Discrete)
Naive Bayes
Naive Bayes Handwritten Example
Naive Bayes in Code with MNIST
Non-Naive Bayes
Bayes Classifier in Code with MNIST
Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
Generative vs Discriminative Models

“Decision Trees
Decision Tree Intuition
Decision Tree Basics
Information Entropy
Maximizing Information Gain
Choosing the Best Split
Decision Tree in Code

Perceptron Concepts
Perceptron in Code
Perceptron for MNIST and XOR
Perceptron Loss Function

“Practical Machine Learning
Hyperparameters and Cross-Validation
Feature Extraction and Feature Selection
Comparison to Deep Learning
Multiclass Classification
Sci-Kit Learn
Regression with Sci-Kit Learn is Easy

“Building a Machine Learning Web Service
Building a Machine Learning Web Service Concepts
Building a Machine Learning Web Service Code

What™s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)

“Appendix / FAQ
What is the Appendix?
Where to get Udemy coupons and FREE deep learning material
Windows-Focused Environment Setup 2018
How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
How to Succeed in this Course (Long Version)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Proof that using Jupyter Notebook is the same as not using it