说明:
By: Rudy Lai
Released: Thursday, March 28, 2019 [New Release!]
Torrent Contains: 26 Files, 7 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/hands-problem-solving-machine-learning-video
Intuitive strategies to deal with messy data, weak models, and leaky machine-learning pipelines
Video Details
ISBN 9781789530087
Course Length 2 hours 40 minutes
Table of Contents
• WORKING WITH MACHINE LEARNING
• DATA WRANGLING
• LINEAR REGRESSION — PREDICT MEDIAN LIVING COSTS
• LOGISTIC REGRESSION CLASSIFY
• PREDICTING THE FUTURE
• DIAGNOSING ISSUES WITH MODELS
Video Description
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.
What You Will 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
Authors
Rudy Lai
Rudy Lai is the founder of QuantCopy, a sales acceleration start-up using AI to write sales emails to prospective customers. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first hand the frustrations of outbound sales and prospecting. Rudy has also spent more than 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. He holds a computer science degree from Imperial College London, where he was part of the Dean's list, and received awards including the Deutsche Bank Artificial Intelligence prize.
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  companies, helping each of them to better make sense of their data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.
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