# Python Machine Learning Bootcamp Canonical URL: ## Overview 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 a very different theoretical basis, 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 gain tips for choosing effective features and algorithms. These skill sets are in high demand, as machine learning algorithms now power the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix. That's why this course will focuses 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 mathematics component. Entering students are expected to be comfortable writing Python programs, as well as using the NumPy and Pandas libraries. ## What you'll learn - Explore foundational techniques like linear and logistic regression for modeling numerical and categorical data - Understand the difference between regression and classification problems and when to apply each approach - Build and evaluate models using k-nearest neighbors, decision trees, and ensemble methods like random forest - Learn key concepts such as cross-validation, training vs. test sets, and performance metrics like mean squared error - Apply feature engineering techniques to improve model accuracy while managing overfitting and bias-variance tradeoffs - Use Python's essential data science libraries, NumPy, Pandas, and scikit-learn, to structure data and implement algorithms - Gain insights into how machine learning powers systems at companies like Netflix, Spotify, and Amazon - Complete a final portfolio project that demonstrates your ability to apply machine learning to solve real problems ## 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 ### 1. Course Kick‑off & Python Refresher - Data Science tool recap - Pandas and indexing - Exploratory data analysis (EDA): standard deviations and uniform vs. normal distributions using NumPy/Pandas - Hands‑on: loading CSVs, basic plotting with Matplotlib ### 2. Data Visualization & Simple Linear Regression - Crafting clear scatterplots: labels, grids, styling - Single‑variable linear regression (attendance → concessions) - Train‑test splitting and dealing with outliers - Evaluating models with R²; interpreting residuals - Extended example: car‑sales dataset, predicting price from one feature ### 3. Binary Classification & Logistic Regression - From regression to classification: why logistic vs. linear - Implementing logistic regression on an employee “stay/leave” dataset - Classification metrics deep dive: accuracy, precision, recall, F1 score, ROC curve - Understanding variability: train‑test ratios, data shuffling, sample size effects - Confusion matrix analysis ### 4. k‑Nearest Neighbors & the Iris Dataset - Introduction to k‑NN: distance metrics, choosing k - Dataset exploration: sepal/petal measurements, plotting clusters - Preprocessing: label encoding categorical data, feature scaling - Model training, hyperparameter tuning, evaluating with confusion matrix and classification report - Brief intro to decision‑tree logic (setting up for ensembles) ### 5. Ensemble Methods & Neural Networks - Random forest classifiers on the Titanic dataset: feature engineering, importance scores - Kaggle workflow: generating predictions, submitting to competition - Neural network primer: perceptron to multilayer architectures - Hands‑on MNIST digit classification with Keras/TensorFlow in Colab ## Schedule - May 26, 2026 – Jun 25, 2026 — NYC - Jun 15, 2026 – Jun 19, 2026 — NYC - Aug 3, 2026 – Aug 7, 2026 — NYC - Aug 30, 2026 – Oct 11, 2026 — NYC - Sep 8, 2026 – Oct 8, 2026 — NYC - Sep 8, 2026 – Oct 8, 2026 — NYC - Sep 22, 2026 – Sep 28, 2026 — NYC - Sep 22, 2026 – Sep 28, 2026 — NYC - Nov 9, 2026 – Nov 13, 2026 — NYC - Nov 9, 2026 – Nov 13, 2026 — NYC - Dec 29, 2026 – Feb 2, 2027 — NYC - Dec 29, 2026 – Feb 2, 2027 — NYC - Jan 17, 2027 – Feb 21, 2027 — NYC ## 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. ## FAQ ### How is this class structured? This class is an 18-hour class that starts by teaching forms of regression analysis and moves onto more industry-used algorithms such as k-nearest neighbors, decision trees, and random forest. Additionally, students will learn how to determine the accuracy of a predictive model. ### How many students are in a given class? Noble's typical class ranges from 8-12 students, but we allow up to 20 students to register for our course. ### How does this class prepare me for the job market? The classes will allow students to learn advanced topics in data science used by the most cutting edge companies such as Google, Facebook, and more. These topics will allow students to build, evaluate, and reassess forecasting models on all forms of data. ### Is there mandatory work outside of the classroom? Students are not required to complete any work outside of class. However, we provide students with bonus materials if they would like extra practice. ### What tangible skills do students leave with after the class? Students will leave with the ability to learn how to build a model from start to finish. Students will learn how to clean and balance data, apply a form of learning algorithm on the data, perform a bias test, and finally evaluate the accuracy of your model. ## Pricing **Tuition:** $1895