-
Book Overview & Buying
-
Table Of Contents
Getting Started with Machine Learning in Python
By :
Getting Started with Machine Learning in Python
By:
Overview of this book
Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills.
In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups.
You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with.
If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you!
All the code and the supporting files are available on GitHub at - https://github.com/PacktPublishing/Getting-Started-with-Machine-Learning-in-Python-
This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python.
Table of Contents (6 chapters)
Launching a Python Environment to Create Machine Learning Models
Prepare Your Datasets for Machine Learning with Data Cleaning
Put Data into Their Right Categories with Classification
Predict Numbers in the Future with Regression
Unsupervised Learning: Segmenting Groups and Detecting Outliers
Modeling Complex Relationships with Nonlinear Models
This video is locked
