Understanding Machine Learning with Python 3

This course will walk you through creating a machine learning prediction solution with Python, the Scikit-learn library, and the Jupyter Notebook environment.

Hello! My name is Jerry Kurata, and welcome to Understanding Machine Learning with Python. In this course, you will gain an understanding of how to perform Machine Learning with Python 3. You will get there by covering major topics like how to format your problem to be solvable, how to prepare your data for use in a prediction, and how to combine that data with algorithms to create models that can predict the future. By the end of this course, you will be able to use Python and the scikit-learn library to create Machine Learning solutions. And you will understand how to evaluate and improve the performance of the solutions you create. Before you begin, make sure you are already familiar with software development and basic statistics. However, your software experience does not have to be in Python, since you will learn the basics in this course. When you use Python together with scikit-learn, you will see why this is the preferred development environment for many Machine Learning practitioners. You will do all the demos using the Jupyter Notebook environment. This environment combines live code with narrative text to create a document with can be executed and presented as a web page. I hope you’ll join me, and I look forward to helping you on your learning journey here at Pluralsight.

• Course Overview 1min
• Getting Started in Machine Learning 26mins
• Understanding the Machine Learning Workflow 4mins
• Asking the Right Question 5mins
• Preparing Your Data 18mins
• Selecting Your Algorithm 11mins
• Training the Model 16mins
• Testing Your Model's Accuracy 24mins
• Summary 5mins