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.
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. 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
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.
Is Python good for machine learning?
Absolutely! 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).
What is scikit-learn?
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.
What will I learn in this course?
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
Are there prerequisites to this course?
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.
Who is this course for?
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.
Course Overview Hi. My name is Jerry Kurata, and welcome to my course, Understanding Machine Learning with Python. These days, machine learning is all around us from helping doctors diagnose patients to assisting us in driving our cars. As we go about our day, we may be utilizing machine learning applications and not even realize it. It silently scans our email inbox for spam emails and ensures that stores are stocked with the goods we want to buy when we need them. This course will introduce you to machine learning and the technology behind it. You will see why companies are in such a rush to use machine learning to grow their business and increase profits. You will learn how developers and data scientists use machine learning to predict events based on data. Specifically, you will learn how to format a problem to be solvable, where to get the data, and how to combine that data with algorithms to create models that can predict the future. Throughout this course, we'll utilize Python and a number of its libraries to make creating machine learning solutions easy. However, you do not need prior experience with Python. In this course, we learn by doing, and the code we will use will be explained as we create our solution. By the end of this course, you will know the how, when, and why of building a machine learning solution with Python. You will have the skills you need to transform one‑line problem statement into a tested prediction model that solves the problem. I look forward to you joining me on this journey of Understanding Machine Learning with Python from Pluralsight.