# Python Machine Learning Advanced Course Online Canonical URL: ## Overview This hands-on course introduces Natural Language Processing (NLP) through real-world machine learning applications built with Flask. Students learn how to clean and structure text using RegEx and lemmatization, perform sentiment analysis with Naive Bayes, TextBlob, and VADER, and develop a movie recommendation system using TF–IDF and cosine similarity. The course also covers Flask fundamentals, dynamic HTML templating, and API integration for working with live data. By the end of the course, students deploy a complete Movie Recommender App, gaining practical experience in applied machine learning and web application 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 ## 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. ## Pricing **Tuition:** $1895