Building Predictive Models with Machine Learning and Python [Video]
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.
|Course Length||2 hours 47 minutes|
|Date Of Publication||27 Sep 2018|