Python Machine Learning Advanced
Extend Python machine-learning work into text analysis and recommendation systems. Noble Desktop's advanced course covers preparing text, building classification pipelines, and evaluating models, with applications such as sentiment analysis. The curriculum connects these techniques to more complete projects, including ways to make model-based results available through an application.
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- Live or Self-paced
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Is this course right for you?
Choose this course for specialized work beyond introductory predictive models. Compare the Python Machine Learning Bootcamp if you need a foundation in regression, classification, model evaluation, and the common problems that affect machine-learning results.
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Summary
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.
Topics included
Curriculum
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.
Course syllabus
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
What's included
- Free course retake within one year to refresh the material and gain practice.
- Class recordings
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Schedule & enrollment
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All class times are Eastern (ET).
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Class time
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Final session ends at 5pm ET
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Dates
–
Class time
Starts at 10am ET
Final session ends at 5pm ET
Attend
In-person or live online
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Dates
–
Class time
Starts at 10am ET
Final session ends at 5pm ET
Attend
In-person or live online
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Dates
–
Class time
Starts at 10am ET
Final session ends at 5pm ET
Attend
In-person or live online
-
Dates
–
Class time
Starts at 10am ET
Final session ends at 5pm ET
Attend
In-person or live online
-
Dates
–
Class time
Starts at 10am ET
Final session ends at 5pm ET
Attend
In-person or live online
Confirm current dates and availability on Noble Desktop before booking. Enrollment opens in a new tab.
Self-paced course
Learn through recorded lessons on your own schedule.
This course is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
- Tuition
- $1,895
- Course length
- 30 hours
- Schedule
- On your schedule
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This hands-on course covers Natural Language Processing (NLP) and the practical skills needed to build Flask web apps that apply machine learning in real scenarios. You’ll clean and structure text using RegEx and lemmatization, run sentiment analysis with Naive Bayes, TextBlob, and Vader, and create a movie recommendation system with TF–IDF and cosine similarity. You’ll also learn Flask basics, dynamic HTML templating, and API integration to pull in live data. By the end, you’ll deploy a complete Movie Recommender App, gaining practical experience in applied machine learning and web development.
Self-paced curriculum
What you'll learn self-paced
- 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.
Self-paced syllabus
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