by Loonycorn | Released April 2018 | ISBN: 9781789345926

An overview of machine learning with hands-on implementation of classification modelsAbout This VideoA hands-on course of two hoursVery clear understanding and concise topicsIn DetailThis course will give you a fundamental understanding of machine learning with a focus on building classification models. The basic concepts of machine learning (ML) are explained, including supervised and unsupervised learning; regression and classification; and overfitting. There are three lab sections which focus on building classification models using support vector machines, decision trees, and random forests using real data sets. The implementation will be performed using the scikit-learn library for Python. Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Introduction
   • You, This Course and Us 00:01:56
   • Install Anaconda 00:02:22
2. Chapter 2 : What is ML?
   • What is Machine Learning? 00:10:43
   • Types of Machine Learning - Supervised Learning and Linear Regression 00:10:30
   • Types of Machine Learning - Logistic Regression and Unsupervised Learning 00:08:23
3. Chapter 3 : Support Vector Machines (SVMs)
   • What is an SVM? How do they work? 00:06:39
   • SVM Lab (1): Loading and examining our data set 00:09:11
   • SVM Lab (2): Building and tweaking our SVM classification model 00:09:08
4. Chapter 4 : Decision Trees
   • What is a Decision Tree? 00:06:13
   • Building a Decision Tree - Decision Tree Learning 00:07:44
   • Building a Decision Tree - Information Gain and Gini Impurity 00:09:17
   • Decision Trees Lab (1): Building our first Decision Tree 00:05:21
   • Decision Trees Lab (2): Viewing and tweaking our Decision Tree 00:05:52
5. Chapter 5 : Overfitting - the Bane of Machine Learning
   • What is Overfitting? And why is it a Problem? 00:09:26
   • Avoiding Overfitted Models - Cross Validation and Regularization 00:08:18
6. Chapter 6 : Ensemble Learning and Random Forests
   • Teamwork: How Ensembles like Random Forest Mitigate the Problem of Overfitting 00:09:04
   • Random Forest Lab: Use an Ensemble of Decision Trees to Get Better Results 00:04:49
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