Getting Started with Machine Learning in Python: Use Machine Learning to make your Python apps smarter

James Cross

$9.99 ~~$137.99~~

Video Sep 2018 2hrs 53mins 1st Edition

James Cross

$9.99 ~~$137.99~~

Video Sep 2018 2hrs 53mins 1st Edition

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Key benefits

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

Description

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.

Who is this book for?

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.

What you will learn

Core concepts of Machine Learning so you can understand fellow data scientists
Clean your data to optimize how it feeds into your Machine-Learning models.
Perform regression in a supervised learning setting, so that you can predict numbers, prices, and conversion rates.
Perform classification in a supervised-learning setting, teaching the model to distinguish between different plants, discussion topics, and objects.
Use decision tree models and random forests, creating models that are explainable but powerful.

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