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001 Common Classification Models.en.srt
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001 Common Classification Models.mp4
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001 Course Structure & Outline.en.srt
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001 Course Structure & Outline.mp4
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001 Intro to Selection & Tuning.en.srt
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001 Intro to Selection & Tuning.mp4
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001 Looking Ahead to Part 3.en.srt
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001 Looking Ahead to Part 3.mp4
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001 Supervised vs. Unsupervised Learning.en.srt
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001 Supervised vs. Unsupervised Learning.mp4
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002 BONUS LECTURE.html
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002 Classification vs. Regression.en.srt
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002 Classification vs. Regression.mp4
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002 Hyperparameters.en.srt
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002 Hyperparameters.mp4
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002 Intro to K-Nearest Neighbors (KNN).en.srt
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002 Intro to K-Nearest Neighbors (KNN).mp4
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002 READ ME_ Important Notes for New Students.html
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003 About this Series.en.srt
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003 About this Series.mp4
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003 Imbalanced Classes.en.srt
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003 Imbalanced Classes.mp4
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003 KNN Examples.en.srt
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003 KNN Examples.mp4
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003 RECAP_ Key Concepts.en.srt
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003 RECAP_ Key Concepts.mp4
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004 CASE STUDY_ KNN.en.srt
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004 CASE STUDY_ KNN.mp4
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004 Classification 101.en.srt
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004 Classification 101.mp4
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004 Confusion Matrix.en.srt
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004 Confusion Matrix.mp4
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004 DOWNLOAD_ Course Resources.html
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004 Machine Learning Part 2 - Classification.pdf
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3.5 MB
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004 Maven_ML_Demos_Part_2.xlsx
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005 Accuracy, Precision & Recall.en.srt
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005 Accuracy, Precision & Recall.mp4
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005 Classification Workflow.en.srt
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005 Classification Workflow.mp4
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005 Intro to Naïve Bayes.en.srt
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005 Intro to Naïve Bayes.mp4
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005 Setting Expectations.en.srt
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005 Setting Expectations.mp4
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006 Feature Engineering.en.srt
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006 Feature Engineering.mp4
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006 Multi-class Confusion Matrix.en.srt
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006 Multi-class Confusion Matrix.mp4
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006 Naïve Bayes _ Frequency Tables.en.srt
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006 Naïve Bayes _ Frequency Tables.mp4
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007 Data Splitting.en.srt
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007 Data Splitting.mp4
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007 Multi-class Scoring.en.srt
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007 Multi-class Scoring.mp4
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007 Naïve Bayes _ Conditional Probability.en.srt
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007 Naïve Bayes _ Conditional Probability.mp4
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008 CASE STUDY_ Naïve Bayes.en.srt
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008 CASE STUDY_ Naïve Bayes.mp4
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008 Model Selection.en.srt
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008 Model Selection.mp4
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008 Overfitting.en.srt
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008 Overfitting.mp4
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009 Intro to Decision Trees.en.srt
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009 Intro to Decision Trees.mp4
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009 Model Drift.en.srt
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009 Model Drift.mp4
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010 Decision Trees _ Entropy 101.en.srt
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010 Decision Trees _ Entropy 101.mp4
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011 Entropy & Information Gain.en.srt
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011 Entropy & Information Gain.mp4
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012 Decision Tree Examples.en.srt
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012 Decision Tree Examples.mp4
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013 Random Forests.en.srt
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013 Random Forests.mp4
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014 CASE STUDY_ Decision Trees.en.srt
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014 CASE STUDY_ Decision Trees.mp4
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015 Intro to Logistic Regression.en.srt
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015 Intro to Logistic Regression.mp4
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016 Logistic Regression Example.en.srt
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016 Logistic Regression Example.mp4
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017 False Positives vs. False Negatives.en.srt
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017 False Positives vs. False Negatives.mp4
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018 Logistic Regression Equation.en.srt
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018 Logistic Regression Equation.mp4
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019 The Likelihood Function.en.srt
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019 The Likelihood Function.mp4
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020 Multivariate Logistic Regression.en.srt
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020 Multivariate Logistic Regression.mp4
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021 CASE STUDY_ Logistic Regression.en.srt
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021 CASE STUDY_ Logistic Regression.mp4
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022 Intro to Sentiment Analysis.en.srt
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022 Intro to Sentiment Analysis.mp4
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023 Cleaning Text Data.en.srt
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023 Cleaning Text Data.mp4
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024 _Bag of Words_ Analysis.en.srt
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024 _Bag of Words_ Analysis.mp4
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025 CASE STUDY_ Sentiment Analysis.en.srt
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025 CASE STUDY_ Sentiment Analysis.mp4
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Bonus Resources.txt
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TXT
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307.2 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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