11 sections • 91 lectures • 19h 00m total length
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10min
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10min
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10min
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Measure of Variability Continued
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10min
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Measures of Variable Relationship
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10min
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10min
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10min
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10min
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SVM with Linear Dataset (Iris)
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20min
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What is Exactly is Probability
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10min
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10min
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10min
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Hypothesis Testing Overview
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10min
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Data Visualization Overview
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10min
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Different Data Visualization Libraries in Python
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10min
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Python Data Visualization Implementation
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10min
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Introduction To Machine Learning
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10min
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Exploratory Data Analysis
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10min
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10min
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10min
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10\min
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10min
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Linear Regression + Correlation Methods
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10min
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Linear Regression Implementation
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10min
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10min
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10min
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parametric vs non-parametric models
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10min
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10min
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10min
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Implement the KNN algorithm from scratch
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10min
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Compare the result with the sklearn library
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10min
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Hyperparameter tuning using the cross-validation
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10min
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The decision boundary visualization
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10min
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Manhattan vs Euclidean Distance
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10min
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10min
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10min
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10min
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10min
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Decision Trees Section Overview
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10min
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10min
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What is Entropy and Information Gain
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20min
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The Decision Tree ID3 algorithm from scratch Part 1
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20min
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https://www.youtube.com/watch?v=w65O7CYRNXQ
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20min
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The Decision Tree ID3 algorithm from scratch Part 3
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20min
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ID3 - Putting Everything Together
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20min
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Evaluating our ID3 implementation
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20min
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Compare with Sklearn implementation
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20min
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20min
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Plot the features importance
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10min
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Decision Trees Hyper-parameters
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10min
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20min
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10min
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Decision Trees Pros and Cons
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20min
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[Project] Predict whether income exceeds $50Kyr - Overview
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10min
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Ensemble Learning Section Overview
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10min
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What is Ensemble Learning
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10min
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https://res.cloudinary.com/de7eatgd8/video/upload/v1699003332/Esemble%20Learning%20and%20Random%20Foreset/3._What_is_Bootstrap_Sampling_d1q9pw.mp4
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10min
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10min
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Out-of-Bag Error (OOB Error)
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10min
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Implementing Random Forests from scratch Part 1
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Implementing Random Forests from scratch Part 2
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10min
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Compare with sklearn implementation
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10min
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Random Forests Hyper-Parameters
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10min
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Random Forests Pros and Cons
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10min
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10min
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10min
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10min
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20min
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20min
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20min
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20min
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20min
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20min
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SVM with Non-linear Dataset
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20min
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20min
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20min
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Unsupervised Machine Learning Intro
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20min
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Unsupervised Machine Learning Continued
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10min
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20min
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10min
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10min
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10min
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PCA Algorithm Steps (Mathematics)
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10min
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10min
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10min
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20min
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20min
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PCA - Biplot and the Screen Plot
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20min
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PCA - Feature Scaling and Screen Plot
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10min
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PCA - Supervised vs Unsupervised
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10min
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