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- How do we find training data?
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Classroom Contents
Real-World Python Machine Learning Tutorial With Scikit Learn
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- 1 - What we will be doing!
- 2 - Sci-Kit Learn Overview
- 3 - How do we find training data?
- 4 - Download data
- 5 - Load our data into Jupyter Notebook
- 6 - Cleaning our code a bit building data class
- 7 - Using Enums
- 8 - Converting text to numerical vectors, bag of words BOW explanation
- 9 - Training/Test Split make sure to "pip install sklearn" !
- 10 - Bag of words in sklearn CountVectorizer
- 11 - fit_transform, fit, transform methods
- 12 - Model Selection SVM, Decision Tree, Naive Bayes, Logistic Regression & Classification
- 13 - predict method
- 14 - Analysis & Evaluation using clf.score method
- 15 - F1 score
- 16 - Improving our model evenly distributing positive & negative examples and loading in more data
- 17 - Let's see our model in action! qualitative testing
- 18 - Tfidf Vectorizer
- 19 - GridSearchCv to automatically find the best parameters
- 20 - Further NLP improvement opportunities
- 21 - Saving our model Pickle and reloading it later
- 22 - Category Classifier
- 23 - Confusion Matrix
