Getting Started with Machine Learning in Python [Video]

A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems

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-

Style and Approach

This extensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you. A practical, hands-on course packed with step-by-step instructions, working examples, and helpful advice. You will learn how Machine Learning can be used to create artificial intelligence.

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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.
  • Go past linear models with SVMs and polynomial regression, tackling relationships that are non-linear.
  • Measure and evaluate your Machine-Learning pipeline, so that you can improve your solution over time.

Authors

Rudy Lai

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, Machine Learning, and cloud computing. Over the past few years, they have worked with some of the world’s largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world’s most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed-back into how our AI generates content.

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Prior to founding QuantCopy, Rudy ran HighDimension.IO, a Machine Learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO’s Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.

In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and Machine Learning. Quantitative trading was also a great platform from which to learn about reinforcement learning in depth, and supervised learning topics in a commercial setting.

Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean’s List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

 

 

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