Buy and Download - Pluralsight Understanding Machine Learning with Python

2016
pluralsight.com
Jerry Kurata
01:54:00
English

Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.
When working with data, machine learning can be used to do incredible things, including predicting future events. Its ease of use combined with the power of scikit-learn is causing Python to become the preferred development language for many machine learning practitioners. In this course, Understanding Machine Learning with Python, you will learn how Python developers and data scientists use machine learning to predict the likelihood of events based on data. Throughout this course, you will use Python and the scikit-learn library. Specifically, you will learn how to format your problem to be solvable, how to prepare your data for use in a prediction, and finally how to combine that data with algorithms to create models that can predict the future, all performed in the Jupyter Notebook environment. By the end of this course, you will have a better understanding of how machine learning can help you put your data to good use in predicting future events, and you'll also know how to use Python to make it happen.

Course Overview
Getting Started in Machine Learning
Introduction
What Is Machine Learning?
Types of Machine Learning
Course Overview
Why This Course?
Installing Python and Jupyter Notebook
Python and Jupyter Notebook Demo
Understanding the Machine Learning Workflow
Machine Learning Workflow Overview
Asking the Right Question
From Question to Solution Statement
Preparing Your Data
Introduction to Data Preparation
Getting Data
The GitHub Repository
Loading, Cleaning, and Inspecting Data
Molding Data
Selecting Your Algorithm
The Role of the Algorithm
Narrowing the Selection
Selecting Our Initial Algorithm
Training the Model
Introduction to Training
The Training Process
Python Training Tools
Splitting Data and Training the Algorithm
Testing Your Model's Accuracy
Introduction to Evaluating the Model
Evaluating the Naive Bayes Model
Performance Improvement, Take 1
Why Overfitting Is Bad
Performance Improvement, Take 2
Understanding and Fixing Unbalanced Classes
What Is Cross Validation?
Implementing and Evaluating Cross Validation
Summarizing the Evaluation
Summary
The Journey so Far
Guides for Your Journey
The Journey from Here

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