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In this video clip we walk by a actual world python machine learning project employing the sci-kit master library. In it we do the job our way to making a model that quickly classifies text as either acquiring a beneficial or negative sentiment. We do this by making use of amazon reviews as our teaching knowledge. Complete movie timeline in the opinions!
Link to Code & Info:
https://github.com/keithgalli/sklearn
Raw Facts down load:
http://jmcauley.ucsd.edu/info/amazon/
Make guaranteed you have sci-kit find out downloaded! To do this either run “pip install sklearn” or use python as a result of Anaconda.
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Online video outline!
:00 – What we will be carrying out!
3:40 – Sci-Package Find out Overview
6:38 – How do we discover education facts?
9:33 – Obtain knowledge
11:45 – Load our information into Jupyter Notebook
16:38 – Cleaning our code a bit (creating data class)
20:13 – Employing Enums
22:50 – Converting text to numerical vectors, bag of terms (BOW) clarification
25:45 – Training/Check Break up (make confident to “pip put in sklearn” !)
33:45 – Bag of text in sklearn (CountVectorizer)
40:05 – healthy_rework, in shape, change procedures
42:05 – Design Collection (SVM, Determination Tree, Naive Bayes, Logistic Regression) & Classification
47:50 – forecast process
53:35 – Evaluation & Evaluation (making use of clf.score() technique)
56:58 – F1 score
1:01:01 – Improving our model (evenly distributing favourable & detrimental examples and loading in much more information)
1:20:36 – Let’s see our model in motion! (qualitative screening)
1:22:24 – Tfidf Vectorizer
1:25:40 – GridSearchCv to routinely come across the most effective parameters
1:31:30 – More NLP enhancement prospects
1:32:50 – Saving our product (Pickle) and reloading it later
1:36:37 – Classification Classifier
1:39:14 – Confusion Matrix
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