# Python Machine Learning Advanced Canonical URL: ## Overview This hands-on course explores Natural Language Processing (NLP) and building Flask web apps that put machine learning into action. You’ll clean and process text with RegEx and lemmatization, perform sentiment analysis with Naive Bayes, TextBlob, and Vader, and build a movie recommendation system using TF–IDF and cosine similarity. Along the way, you’ll learn Flask fundamentals, dynamic HTML templating, and API integration to bring in live data. By the end, you’ll deploy a full Movie Recommender App, giving you real-world experience in applied ML and web development. ## What you'll learn - Build a complete NLP pipeline, including cleaning with RegEx, removing stopwords, lemmatizing, and vectorizing text. - Train and evaluate a Naive Bayes machine learning model to classify movie reviews as positive or negative. - Compare and apply pre-trained sentiment scoring systems such as TextBlob and Vader. - Develop a recommendation engine that suggests similar products using NLP techniques. - Learn Flask fundamentals by creating search apps, integrating APIs, and serving ML models in the browser. - Complete a capstone project by building a Flask-powered Movie Recommender App that brings together NLP, machine learning, and web development. ## Curriculum ### 1. NLP & Sentiment Analysis #### Environment Setup & NLP Fundamentals - VS Code environment configuration, NLP libraries installation - Tokenization, stopword removal, stemming, lemmatization - Text representation with Bag of Words and TF-IDF #### Sentiment Analysis Project - Logistic Regression for sentiment classification - Data splitting, model evaluation metrics (accuracy, precision, recall, confusion matrix) ### 2. Recommendation Systems #### Collaborative Filtering - User-based and item-based filtering - Cosine similarity for personalized recommendations #### Content-Based Movie Recommender - Vectorizing text using TF-IDF - Implementing content similarity algorithms ### 3. Flask App for Recommendations #### Building an ML-Powered Web App - Flask basics and web serving - Developing a recommendation system Flask app ### 4. Forecasting & Deep Learning #### Time Series with Facebook Prophet - Trend forecasting and visualization (e.g., market prices) #### Deep Learning with PyTorch - CNN basics, image classification using the CIFAR-10 dataset - Model training, accuracy assessment, and confusion matrix interpretation ### 5. Object Detection #### Real-Time Object Detection with YOLO - Image detection and labeling with pretrained models - Adapting YOLO models to video streams and real-time webcam input ## Schedule - Aug 17, 2026 – Aug 21, 2026 — NYC - Oct 15, 2026 – Oct 21, 2026 — NYC - Dec 7, 2026 – Dec 11, 2026 — 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. ## Pricing **Tuition:** $1895