Applied Supervised Learning with Python

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Applied Supervised Learning with Python provides you a rich understanding of machine learning, one of the most pursued topics in information science, and Python, one of the most popular scripting languages. Through this course, you'll learn Jupyter Notebooks, the technology used in academic and commercial circles with in-line code running support.

Machine learning—the ability of a machine to give correct answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques to your data science projects using Python. You'll explore Jupyter notebooks, a technology widely used in academic and commercial circles with support for running inline code.

With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.

By the end of this course, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.

This course does not cover the basics of Python. This course focuses heavily on real-world implementation of supervised learning.

After completing this course, you will be able to:

Understand the concept of supervised learning and its applications
Implement common supervised learning algorithms using machine learning Python libraries
Validate models using the k-fold technique
Build your models with decision trees to get results effortlessly
Use ensemble modeling techniques to improve the performance of your model
Apply a variety of metrics to compare machine learning models