English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 04m | 0.97 GB
An overview of machine learning with hands-on implementation of classification models
This 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.
Hands-on course to Introduction to ML Classification Models using scikit-learn
What You Will Learn
- Have a broad understanding of ML and hands-on experience with building classification models using support vector machines, decision trees, and random forests in Python’s scikit-learn
Table of Contents
Introduction
1 Install Anaconda
2 You, This Course and Us
What is ML
3 What is Machine Learning
4 Types of Machine Learning – Supervised Learning and Linear Regression
5 Types of Machine Learning – Logistic Regression and Unsupervised Learning
Support Vector Machines (SVMs)
6 What is an SVM How do they work
7 SVM Lab (1) – Loading and examining our data set
8 SVM Lab (2) – Building and tweaking our SVM classification model
Decision Trees
9 What is a Decision Tree
10 Building a Decision Tree – Decision Tree Learning
11 Building a Decision Tree – Information Gain and Gini Impurity
12 Decision Trees Lab (1) – Building our first Decision Tree
13 Decision Trees Lab (2) – Viewing and tweaking our Decision Tree
Overfitting – the Bane of Machine Learning#
14 What is Overfitting And why is it a Problem
15 Avoiding Overfitted Models – Cross Validation and Regularization
Ensemble Learning and Random Forests
16 Teamwork – How Ensembles like Random Forest Mitigate the Problem of Overfitting
17 Random Forest Lab – Use an Ensemble of Decision Trees to Get Better Results
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