Ajouter "Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)"

Practice your Python Pandas data science skills with problems on StrataScratch! https://www.stratascratch.com/?via=keith In this video we walk through a real world python machine learning project using the sci-kit learn library. In it we work our way to building a model that automatically classifies text as either having a positive or negative sentiment. We do this by using amazon reviews as our training data. Full video timeline in the comments! Link to Code & Data: https://github.com/keithgalli/sklearn Raw Data download: http://jmcauley.ucsd.edu/data/amazon/ Sci-kit learn documentation: https://scikit-learn.org/stable/documentation.html Make sure you have sci-kit learn downloaded! To do this either run "pip install sklearn" or use python through Anaconda. Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli --------------------------- Follow me on social media! Instagram: https://www.facebook.com/unsupportedbrowser Twitter: https://twitter.com/keithgalli To get one of the cool shirts I was wearing: https://www.facebook.com/unsupportedbrowser --------------------------- Video outline! 0:00 - What we will be doing! 3:40 - Sci-Kit Learn Overview 6:38 - How do we find training data? 9:33 - Download data 11:45 - Load our data into Jupyter Notebook 16:38 - Cleaning our code a bit (building data class) 20:13 - Using Enums 22:50 - Converting text to numerical vectors, bag of words (BOW) explanation 25:45 - Training/Test Split (make sure to "pip install sklearn" !) 33:45 - Bag of words in sklearn (CountVectorizer) 40:05 - fit_transform, fit, transform methods 42:05 - Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification 47:50 - predict method 53:35 - Analysis & Evaluation (using clf.score() method) 56:58 - F1 score 1:01:01 - Improving our model (evenly distributing positive & negative examples and loading in more data) 1:20:36 - Let's see our model in action! (qualitative testing) 1:22:24 - Tfidf Vectorizer 1:25:40 - GridSearchCv to automatically find the best parameters 1:31:30 - Further NLP improvement opportunities 1:32:50 - Saving our model (Pickle) and reloading it later 1:36:37 - Category Classifier 1:39:14 - Confusion Matrix --------------------- If you are curious to learn how I make my tutorials, check out this video: https://www.youtube.com/watch?v=LEO4igyXbLs *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.