# Python Machine Learning Course Online (Self-Paced) Canonical URL: ## Overview This skill set is in high demand, as machine learning algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix. This course will begin with linear and logistic regression, the most time-tested and reliable tools for approaching a machine learning problem. The course will then progress to algorithms with very different theoretical foundations, such as k-nearest neighbors, decision trees, and random forests. This will bring important statistical concepts to the forefront such as bias, variance, and overfitting. You’ll also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms. The course will be focused on the practical skills needed to solve real-world problems with machine learning. The mathematical foundations for each machine learning algorithm will be explained visually, but there will not be a formal math component. Entering students are expected to be comfortable with writing Python programs, as well as the NumPy and Pandas libraries. This course does require 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. ## What you'll learn - How to clean and balance your data using the Pandas library - Applying machine learning algorithms such as logistic regression and random forests using the scikit-learn library - Choosing good features to use as input for your algorithms - Properly splitting data into training, testing, and cross-validation sets - Important theoretical concepts like overfitting, variance, and bias - Evaluating the performance of your machine learning models ## 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](/classes/python-data-science-bootcamp-nyc)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 ### Art Yudin — Program Director & Senior Instructor Art Yudin is a FinTech enthusiast who has a great passion for coding and teaching. Art is the founder and CEO of Practical Programming (a Noble Desktop partner company), a leading training company for aspiring developers and data scientists. Currently, Art develops financial services software and leads classes and workshops at Practical Programming in New York and Chicago. He is the author of several coding publications including "Building Versatile Mobile Apps with Python and REST with React and Django." ### Brian McClain — Program Director & Senior Instructor Brian McClain is an experienced instructor, curriculum developer, and web developer. Brian served as Director for a coding bootcamp before joining Noble Desktop in 2022, 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. Prior to Noble, 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, provides appraisals of art and antiques from user-uploaded images. ### 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. ### Kash Sudhakar — Instructor Kash specializes in full-stack web development, data analysis & visualization, machine learning, artificial intelligence, and applied computer science. With over 6 years of teaching, curriculum, and leadership experience across coding boot camps and other educational institutions, combined with over 3 years of professional software engineering and data science expertise, he's driven to help shape the next generation of technologists and creative coders. ### Chett Tiller — Instructor Chett Tiller is an experienced web developer who has brought his expertise with React, Node, and full-stack development to multiple companies over his career. After transitioning six years ago to an instructor, first at the Flatiron School and now at Noble Desktop, Chett has brought his passion for full-stack engineering to hundreds of students and guided them on their journeys from fledgling developers to their first job offers. When Chett isn't busy teaching students or writing curriculum, he builds online products for local volunteer organizations and dabbles in game development. ## Pricing **Tuition:** $1895