# Python Machine Learning Course Online Canonical URL: ## Overview Machine learning skills are highly sought after, as algorithms now drive much of the trading on Wall Street and product recommendations for major companies like Amazon, Spotify, and Netflix. This course begins with foundational machine learning concepts such as linear and logistic regression, which are essential tools for solving machine learning problems. You’ll then explore more advanced algorithms, including k-nearest neighbors, decision trees, and random forests. Along the way, important statistical concepts like bias, variance, and overfitting will be introduced. You’ll also learn how to evaluate the performance of your models and gain insight into selecting the most effective features and algorithms for your work. The course focuses on practical skills, equipping you with the tools needed to solve real-world problems using machine learning. While the mathematical foundations of each algorithm will be explained visually, no formal math component is included. Students should be comfortable with Python programming and libraries such as NumPy and Pandas. If you're new to Python, it's recommended that you complete our [Python for Data Science Bootcamp](https://www.nobledesktop.com/topics/python-bootcamps-nyc) before enrolling. ## Prerequisites This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course. ## Curriculum ### Fundamentals #### Basic Regression Analysis - Linear Regression - Mean squared error - Training set vs Test set - Cross validation #### Advanced Regression Analysis - Multi-linear regression - Feature engineering - Overfitting ### Classification #### Logistic Regression - Regression vs Classification - Logistic Regression - Sigmoid function #### K-nearest Neighbors - K-nearest neighbors - Model-based vs memory-based - Parametric vs non-parametric - Evaluating performance ### Decision Trees #### Decision Trees - Decision tree - Interpretability - Bias-variance tradeoff #### Random forest - Random forest - Ensemble methods - Hyperparameters ### Final Portfolio Project ## Instructors ### Garfield Stinvil — Instructor Garfield is an experienced software trainer with over 16 years of real-world professional experience. He started as a data analyst with a Wall Street real estate investment company & continued working in the professional development department at New York Road Runners Organization. He enjoys bringing humor to whatever he teaches and loves conveying ideas in novel ways that help others learn more efficiently. Since starting his professional training career in 2016, he has worked with several corporate clients including Adobe, HBO, Amazon, Yelp, Mitsubishi, WeWork, Michael Kors, Christian Dior, and Hermès. Outside of work, his hobbies include rescuing & archiving at-risk artistic online media using his database management skills. ### Colin Jaffe — Instructor Colin Jaffe is a programmer, writer, and teacher with a passion for creative code, customizable computing environments, and simple puns. He loves teaching code, from the fundamentals of algorithmic thinking to the business logic and user flow of application building—he particularly enjoys teaching JavaScript, Python, API design, and front-end frameworks. Colin has taught code to a diverse group of students since learning to code himself, including young men of color at All-Star Code, elementary school kids at The Coding Space, and marginalized groups at Pursuit. Colin lives in Brooklyn with his wife, two kids, and many intricate board games. ### Brian McClain — Instructor Brian McClain is an experienced instructor, curriculum developer, and web developer. Brian served as Director for a coding bootcamp, where he is now a lead instructor and course developer for both JavaScript and Python. He teaches Web Development, JavaScript, Python for Data Science, Machine Learning, and AI. He taught Python Data Science and Machine Learning as an Adjunct Professor of Computer Science at Westchester County College. Brian is also an active industry professional in the field of generative AI app development. His website and iOS app, Artmink, provide appraisals of art and antiques from user-uploaded images. ## FAQ ### Are there any additional fees or expenses? There are no extra fees or taxes for our courses. The price you see on this page is the maximum you’ll pay us. ## Pricing **Tuition:** $1895