Hands-On Problem Solving for Machine Learning [Video]

More Information
Learn
  • Acquire a toolbox for machine learning in Python in just 30 minutes.
  • Clean messy datasets from the real world and use them in Python
  • Fix linear models that predicted wrong numbers
  • Resolve issues with classification models that mislabel data points
  • Deal with overfitting and making sure models generalize to the future
  • Future-proof your machine-learning pipeline
About

Machine learning is all the rage, and you have been tasked with creating models for your business. What looked simple on the surface quickly becomes a nightmare of messy data and non-performing models. What do you do?

Hands-On Problem Solving for Machine Learning is packed with intuitive explanations of how machine learning works so that you can fix your models when they break. It presents a wide array of practical solutions for your machine learning pipeline, whether you are working with images, text, or numbers. You'll get a real feel for how to tackle challenges posed during regression and classification tasks.

If you want to move past calling simple machine learning libraries, and start solving machine learning problems with real-world messy data, this course is for you!

All the code and supporting files for this course are available on GitHub at - https://github.com/PacktPublishing/Machine-Learning-Problems-Solved-V-

Style and Approach

This fast-paced, solution-focused course quickly brings you to the heart of any machine learning problem; it supplies streamlined explanations around what is wrong, how it is wrong, and what needs to be done to solve it, and also hands-on demonstrations of the solution implemented.

Features
  • Resolve challenges in supervised learning: misbehaving classifiers and wrong regressors.
  • Practical solutions for building production-ready machine-learning pipelines that don't break
  • Intuition-driven practical tour through machine learning, packed with step-by-step instructions, working examples, and helpful advice.
Course Length 2 hours 40 minutes
ISBN 9781789530087
Date Of Publication 28 Mar 2019

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 better make 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 for 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.

Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first-hand 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 you can learn about reinforcement learning and supervised learning topics in depth and 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.