Harry Potter
Machine Learning is one of the most fascinating topics in programming today. It's applications are far reaching and has quickly became an essential attribute for nearly all software projects.
This ongoing course will teach you how to do machine learning in the most practical way possible.
How does a spam filter work? How do you decide sentiment on a line of text? How do you categorize news headlines automatically?
These questions are all answered through the concepts of Machine Learning & Text Classification.
Natural language of all kinds has nuances that a computer has trouble picking up. Think about the simple sentence:
"While I ate, the dog and I laughed."
"While I ate the dog and I laughed."
Same words but one single comma and the entire context changes. This simple example sheds light on how difficult it is to get a computer to understand all the complexities of human language.
This section is dedicated to helping you parse through text and make more sense of it so you can make it actionable.
1 - Intro
2 - Sublime Text & Jupyter Notebooks
3 - Initialize Virtual Environment with Pipenv
4 - Bag of Words
5 - One Hot Array
6 - Bag of Words Function
7 - One Hot Array Function
8 - One Hot Array Back to Text
9 - Bag of Words with External Data File
10 - One Hot Array with External Data
11 - Training Data and Labels as Numpy Arrays
12 - Train and Predict with Sklearn SVM
13 - Text Prediction Recap
14 - Reusable Sklearn Classifier
15 - Missing BOW
16 - Pickles
17 - Good Data In, Good Data Out
18 - Dataset Resources
19 - Grab and Parse Dataset
20 - Prepare Training Model for Spam + Not Spam
21 - Train Spam Classifier
22 - Clean and Predict
23 - Scoring Classifier Accuracy
24 - One Hot Encoding Classification Recap
25 - Preprocessing with a Keras Tokenizer
26 - Pad Sequences
27 - Convert Our Text Data into Sequences
28 - Labels and LabelEncoder
29 - Reusable Text-Label Utility
30 - Split Training and Validation Data
31 - Tokenized Text Classifier
32 - Cross Validation
33 - Sensitivity vs Specificity
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