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Building Predictive Models with Machine Learning and Python [Video]

More Information
Learn
  • Make each stage in building a Machine Learning based model easy and fast.
  • Write and run your code inside Jupyter Notebooks to make sharing, debugging, and iterating on your code an absolute breeze. 
  • Read, explore, clean, and prepare your data using Pandas, the most popular library for analyzing data tables. 
  • Use the Scikit-Learn library to deploy ready-built models, train them, and see results in just a few lines of code.
  • Evaluate your models to ensure they can be trusted! 
  • Cardinal rules you must follow to obtain a valid model you can rely on in the real world.
  • Use hyper-parameter optimization to get the best possible version of each model for your specific application.
About

Machine Learning is no longer the inaccessible domain it used to be. There are over 100,000 Python libraries you can download in one line of code!

This course will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist's toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems.

Once you master the content of the course, you can level-up your knowledge of the Python Data Analytics and Machine Learning stack by exploring these recommended libraries.

This course will guide you through the tools in the Python ecosystem that Data Scientists use to get results in a matter of hours - and with practice - in a matter of minutes. The best way to learn is through examples, and this course will guide you through all the steps needed to train and test your models by tackling several classifications and regression challenges.

By the end of the course, you will be able to take the Python Machine Learning toolkit we cover and apply it to your own projects to deploy models in just a few lines of code.

All the code and supporting files are available on GitHub at: https://github.com/PacktPublishing/Building-Predictive-Models-with-Machine-Learning-and-Python-

Style and Approach

The course is structured around many small projects that introduce the tools you need one at a time and showcase some of the most common pitfalls of working with real-world data. It's full of instructions, interesting and illustrative examples, and clear explanations. We share some useful tips and heuristics to help you diagnose problems you may encounter when applying Machine Learning to real-world projects, helping you plan a solution that overcomes the obstacle.

Features
  • Learn the tools that make each stage in building a Machine Learning-based model easy and fast. 
  • Master how to get models working and make predictions by building models over and over again in a project-based teaching style.
  • Use Jupyter Notebooks to write and test your code in an interactive way.
Course Length 2 hours 47 minutes
ISBN9781789132113
Date Of Publication 27 Sep 2018
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Authors

Rudy Lai

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 your 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 to learn deeply about reinforcement learning 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.