Understanding Machine Learning with Python 3
Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.
What you'll learn
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 use Python for Machine Learning. 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
- How to combine that data with algorithms to create models that can predict the future
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.
Table of contents
- Introduction to Evaluating the Model 1m
- Evaluating the Naive Bayes Model 4m
- Performance Improvement, Take 1 2m
- Why Overfitting Is Bad 4m
- Performance Improvement, Take 2 3m
- Understanding and Fixing Unbalanced Classes 2m
- What Is Cross Validation? 5m
- Implementing and Evaluating Cross Validation 2m
- Summarizing the Evaluation 1m
Absolutely! Many developers use Python for machine learning purposes. Python is easier to learn and implement than many other programming languages, like C or Java, and it has several helpful libraries. It also has amazing data handling capabilities, and when added to all the other benefits that is why Python is a preferred language for teaching and learning ML (machine learning).
Scikit-learn is a free and extremely useful machine learning library for Python, providing access to several tools for statistical modeling, regression, clustering, classifcation and much more.
This course will teach you how to create a machine learning prediction solution through Python. Some of the main topics covered include:
- An introduction to Machine Learning
- Installing and using Jupyter Notebook
- The Machine Learning workflow
- Preparing your data for Machine Learning
- Selecting an initial algorithm
- Training your ML model
- Testing your machine learning model's accuracy
- Much more
Before taking this course you will want to already be familiar with software development in general and basic statistics. No prior Python experience is required.
This course is for anyone who wants to learn the basics of machine learning and how to use Python to accomplish it. If you want to learn how to prepare data for use in predicting solutions for your business or other endeavors, this course is for you.