A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems
About This Video
- Learn about supervised learning: how to classify data points and predict future numbers
- Practical exercises on unsupervised learning: how to segment clients and cluster documents
- Intuition-driven practical tour through Machine Learning, packed with step-by-step instructions, working examples, and helpful advice
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!
This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python.
This course is for anyone, with a little coding experience and basic numerical skills, who wants to go beyond hardcoded, rule-based programming and use their datasets to automatically learn new algorithms that solve problems. From developers to analysts, this course aims to bring Machine Learning to everyone. It uses intuition as a base from which to explain the theory behind Machine Learning and its algorithms. Basic Python skills are assumed.
Table of contents
- Chapter 1 : Launching a Python Environment to Create Machine Learning Models
- Chapter 2 : Prepare Your Datasets for Machine Learning with Data Cleaning
- Chapter 3 : Put Data into Their Right Categories with Classification
- Chapter 4 : Predict Numbers in the Future with Regression
- Chapter 5 : Unsupervised Learning: Segmenting Groups and Detecting Outliers
- Chapter 6 : Modeling Complex Relationships with Nonlinear Models
- Title: Getting Started with Machine Learning in Python
- Release date: September 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788477437
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