Python for Machine Learning & Data Science Course

Data scientists and software developers are growing at a rate significantly faster than the average, according to the U.S. Bureau of Labor Statistics, with an average of 200,000 combined annual job openings projected through 2033. Paired with six-figure salaries, now is a great time to join this in-demand industry.

This Python course is designed for those who want to learn how to work with data from the ground up with no prior coding experience. The course starts by covering the basics of Python programming before moving into more core data science skills like statistical analysis and regression modeling. From there, you will gain hands-on experience building classification models, tuning machine learning algorithms, and even training neural networks using Keras and TensorFlow.

During this Python training program, you will complete practical projects that mirror real-world data workflows, such as employee retention prediction, car pricing models, and image recognition using the MNIST dataset. These projects help reinforce skills and serve as portfolio pieces for job applications. The data science and machine learning training curriculum is structured to help you build confidence in using Python for data analysis and machine learning, even if you have no technical background.

Brian McClain

Brian McClain is a senior instructor and program director at Noble Desktop, where he teaches Python, data science, machine learning, and AI courses. His expertise covers several programming languages (including Python, R, and SwiftUI) and advanced frameworks (SQL, Flask, and OpenAI technologies). Brian holds certifications as a New York State Instructor of Computer Applications and a Licensed Private Career School Teacher. He earned his Bachelor's degree in Political Science from Duke University.

Colin Jaffe

Colin Jaffe is a programmer and curriculum developer at Noble Desktop. He has taught coding and software development at Noble Desktop as well as various other educational institutions. Colin's teaching focuses on algorithmic thinking, application logic, and practical frameworks such as React and Python. His professional background includes front-end development and data analytics, and he brings creativity to technical instruction, shaped by his experience as a self-taught programmer.

Requirements:

Hardware Requirements:

• This course can be taken on either a PC or Mac. Chromebooks are not compatible.

Software Requirements:

• PC: Windows 10 or later.
• Mac: macOS 12 or later.
• Browser: The latest version of Google Chrome or Mozilla Firefox is preferred. Microsoft Edge and Safari are also compatible.
• Microsoft Word Online
• Adobe Acrobat Reader
• Google Colab
• Software must be installed and fully operational before the course begins.

Other:

• Email capabilities and access to a personal email account.

Instructional Material Requirements:

The instructional materials required for this course are included in enrollment and will be available online.