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   001 Course Structure & Outline.en.srt

   002 READ ME_ Important Notes for New Students.html

   003 About this Series.mp4

   004 Machine Learning Part 2 - Classification.pdf

   005 Setting Expectations.en.srt

   001 Supervised vs. Unsupervised Learning.en.srt

   002 Classification vs. Regression.en.srt

   003 RECAP_ Key Concepts.en.srt

   004 Classification 101.en.srt

   005 Classification Workflow.en.srt

   006 Feature Engineering.en.srt

   007 Data Splitting.en.srt

   008 Overfitting.en.srt

   001 Common Classification Models.en.srt

   002 Intro to K-Nearest Neighbors (KNN).en.srt

   003 KNN Examples.en.srt

   004 CASE STUDY_ KNN.en.srt

   005 Intro to Naïve Bayes.en.srt

   006 Naïve Bayes _ Frequency Tables.en.srt

   007 Naïve Bayes _ Conditional Probability.en.srt

   008 CASE STUDY_ Naïve Bayes.en.srt

   009 Intro to Decision Trees.en.srt

   010 Decision Trees _ Entropy 101.en.srt

   011 Entropy & Information Gain.en.srt

   012 Decision Tree Examples.en.srt

   013 Random Forests.en.srt

   014 CASE STUDY_ Decision Trees.en.srt

   015 Intro to Logistic Regression.en.srt

   016 Logistic Regression Example.en.srt

   017 False Positives vs. False Negatives.en.srt

   018 Logistic Regression Equation.en.srt

   019 The Likelihood Function.en.srt

   020 Multivariate Logistic Regression.en.srt

   021 CASE STUDY_ Logistic Regression.en.srt

   022 Intro to Sentiment Analysis.en.srt

   023 Cleaning Text Data.en.srt

   024 _Bag of Words_ Analysis.en.srt

   025 CASE STUDY_ Sentiment Analysis.en.srt

   001 Intro to Selection & Tuning.en.srt

   002 Hyperparameters.en.srt

   003 Imbalanced Classes.en.srt

   004 Confusion Matrix.en.srt

   005 Accuracy, Precision & Recall.en.srt

   006 Multi-class Confusion Matrix.en.srt

   007 Multi-class Scoring.en.srt

   008 Model Selection.en.srt

   009 Model Drift.en.srt

   001 Looking Ahead to Part 3.en.srt

   002 BONUS LECTURE.html