Section 1 : Introduction to Course
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Lecture 1 | EARLY BIRD INFO | |
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Lecture 2 | COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! copy | 00:03:36 Duration |
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Lecture 3 | Anaconda Python and Jupyter Install and Setup | 00:13:43 Duration |
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Lecture 4 | Note on Environment Setup - Please read me! | |
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Lecture 5 | Environment Setup |
Section 2 : OPTIONAL Python Crash Course
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Lecture 1 | OPTIONAL Python Crash Course | |
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Lecture 2 | Python Crash Course - Part One | 00:15:54 Duration |
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Lecture 3 | Python Crash Course - Part Two | |
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Lecture 4 | Python Crash Course - Part Three | 00:11:12 Duration |
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Lecture 5 | Python Crash Course - Exercise Questions | 00:01:22 Duration |
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Lecture 6 | Python Crash Course - Exercise Solutions | 00:09:16 Duration |
Section 3 : Machine Learning Pathway Overview
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Lecture 1 | Machine Learning Pathway | 00:10:08 Duration |
Section 4 : NumPy
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Lecture 1 | Introduction to NumPy | 00:02:06 Duration |
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Lecture 2 | NumPy Arrays | 00:20:46 Duration |
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Lecture 3 | NumPy Indexing and Selection | 00:10:56 Duration |
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Lecture 4 | NumPy Operations | 00:08:05 Duration |
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Lecture 5 | NumPy Exercises | 00:01:07 Duration |
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Lecture 6 | Numpy Exercises - Solutions | 00:06:58 Duration |
Section 5 : Pandas
Section 6 : Matplotlib
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Lecture 1 | Introduction to Matplotlib | 00:03:59 Duration |
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Lecture 2 | Matplotlib Basics | 00:12:26 Duration |
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Lecture 3 | Matplotlib - Understanding the Figure Object | 00:07:24 Duration |
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Lecture 4 | Matplotlib - Implementing Figures and Axes | 00:14:22 Duration |
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Lecture 5 | Matplotlib - Figure Parameters | 00:04:47 Duration |
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Lecture 6 | Matplotlib - Subplots Functionality | 00:19:07 Duration |
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Lecture 7 | Matplotlib Styling - Legends | 00:06:55 Duration |
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Lecture 8 | Matplotlib Styling - Colors and Styles | 00:14:21 Duration |
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Lecture 9 | Advanced Matplotlib Commands (Optional) | 00:03:42 Duration |
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Lecture 10 | Matplotlib Exercise Questions Overview | 00:05:34 Duration |
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Lecture 11 | Matplotlib Exercise Questions - Solutions | 00:16:25 Duration |
Section 7 : Seaborn Data Visualizations
Section 8 : Data Analysis and Visualization Capstone Project Exercise
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Lecture 1 | Capstone Project Overview | 00:12:35 Duration |
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Lecture 2 | Capstone Project Solutions - Part One | 00:17:07 Duration |
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Lecture 3 | Capstone Project Solutions - Part Two | 00:14:41 Duration |
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Lecture 4 | Capstone Project Solutions - Part Three | 00:19:38 Duration |
Section 9 : Machine Learning Concepts Overview
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Lecture 1 | Introduction to Machine Learning Overview Section | 00:05:03 Duration |
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Lecture 2 | Why Machine Learning | 00:09:06 Duration |
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Lecture 3 | Types of Machine Learning Algorithms | 00:07:39 Duration |
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Lecture 4 | Supervised Machine Learning Process | 00:13:32 Duration |
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Lecture 5 | Companion Book - Introduction to Statistical Learning | 00:02:42 Duration |
Section 10 : Linear Regression
Section 11 : Feature Engineering and Data Preparation
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Lecture 1 | A note from Jose on Feature Engineering and Data Preparation | |
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Lecture 2 | Introduction to Feature Engineering and Data Preparation | 00:15:20 Duration |
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Lecture 3 | Dealing with Outliers | 00:26:23 Duration |
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Lecture 4 | Dealing with Missing Data Part One - Evaluation of Missing Data | 00:10:36 Duration |
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Lecture 5 | Dealing with Missing Data Part Two - Filling or Dropping data based on Rows | 00:20:34 Duration |
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Lecture 6 | Dealing with Missing Data Part 3 - Fixing data based on Columns | 00:23:08 Duration |
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Lecture 7 | Dealing with Categorical Data - Encoding Options | 00:12:41 Duration |
Section 12 : Cross Validation , Grid Search, and the Linear Regression Project
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Lecture 1 | Section Overview and Introduction | 00:03:07 Duration |
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Lecture 2 | Cross Validation - Test Train Split | 00:11:12 Duration |
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Lecture 3 | Cross Validation - Test Validation Train Split | 00:14:37 Duration |
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Lecture 4 | Cross Validation - cross_val_score | 00:11:32 Duration |
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Lecture 5 | Cross Validation - cross_validate | 00:06:48 Duration |
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Lecture 6 | Grid Search | 00:12:06 Duration |
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Lecture 7 | Linear Regression Project Overview | 00:02:40 Duration |
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Lecture 8 | Linear Regression Project - Solutions | 00:12:00 Duration |
Section 13 : Logistic Regression
Section 14 : KNN - K Nearest Neighbors
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Lecture 1 | Introduction to KNN Section | 00:02:03 Duration |
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Lecture 2 | KNN Classification - Theory and Intuition | 00:11:11 Duration |
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Lecture 3 | KNN Coding with Python - Part One | 00:13:32 Duration |
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Lecture 4 | KNN Coding with Python - Part Two - Choosing K | 00:23:16 Duration |
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Lecture 5 | KNN Classification Project Exercise Overview | 00:03:09 Duration |
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Lecture 6 | KNN Classification Project Exercise Solutions | 00:14:05 Duration |
Section 15 : Support Vector Machines
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Lecture 1 | Introduction to Support Vector Machines | 00:01:24 Duration |
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Lecture 2 | History of Support Vector Machines | 00:04:34 Duration |
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Lecture 3 | SVM - Theory and Intuition - Hyperplanes and Margins | 00:13:17 Duration |
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Lecture 4 | SVM - Theory and Intuition - Kernel Intuition | 00:04:51 Duration |
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Lecture 5 | SVM - Theory and Intuition - Kernel Trick and Mathematics | 00:20:42 Duration |
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Lecture 6 | SVM with Scikit-Learn and Python - Classification Part One | 00:10:52 Duration |
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Lecture 7 | SVM with Scikit-Learn and Python - Classification Part Two | 00:15:57 Duration |
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Lecture 8 | SVM with Scikit-Learn and Python - Regression Tasks | 00:20:52 Duration |
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Lecture 9 | Support Vector Machine Project Overview | 00:04:20 Duration |
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Lecture 10 | Support Vector Machine Project Solutions | 00:18:24 Duration |
Section 16 : Tree Based Methods Decision Tree Learning
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Lecture 1 | Introduction to Tree Based Methods | 00:01:16 Duration |
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Lecture 2 | Decision Tree - History | 00:08:58 Duration |
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Lecture 3 | Decision Tree - Terminology | 00:04:05 Duration |
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Lecture 4 | Decision Tree - Understanding Gini Impurity | 00:07:46 Duration |
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Lecture 5 | Constructing Decision Trees with Gini Impurity - Part One | 00:07:26 Duration |
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Lecture 6 | Constructing Decision Trees with Gini Impurity - Part Two | 00:11:17 Duration |
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Lecture 7 | Coding Decision Trees - Part One - The Data | 00:19:10 Duration |
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Lecture 8 | Coding Decision Trees - Part Two -Creating the Model | 00:20:50 Duration |
Section 17 : Random Forests
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Lecture 1 | Introduction to Random Forests Section | 00:01:44 Duration |
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Lecture 2 | Random Forests - History and Motivation | 00:11:38 Duration |
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Lecture 3 | Random Forests - Key Hyperparameters | 00:03:00 Duration |
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Lecture 4 | Random Forests - Number of Estimators and Fea | 00:10:53 Duration |
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Lecture 5 | Random Forests - Bootstrapping and Out-of-Bag | 00:12:48 Duration |
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Lecture 6 | Coding Classification with Random Forest Clas | 00:12:58 Duration |
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Lecture 7 | Coding Classification with Random Forest Clas | 00:23:35 Duration |
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Lecture 8 | Coding Regression with Random Forest Regresso | 00:04:31 Duration |
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Lecture 9 | Coding Regression with Random Forest Regresso | |
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Lecture 10 | Coding Regression with Random Forest Regresso | 00:21:08 Duration |
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Lecture 11 | Coding Regression with Random Forest Regresso | 00:11:00 Duration |
Section 18 : Boosting Methods
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Lecture 1 | Introduction to Boosting Section | 00:01:59 Duration |
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Lecture 2 | Boosting Methods - Motivation and History | 00:06:09 Duration |
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Lecture 3 | AdaBoost Theory and Intuition | 00:19:53 Duration |
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Lecture 4 | AdaBoost Coding Part One - The Data | 00:11:11 Duration |
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Lecture 5 | AdaBoost Coding Part Two - The Model | 00:18:18 Duration |
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Lecture 6 | Gradient Boosting Theory | 00:12:33 Duration |
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Lecture 7 | Gradient Boosting Coding Walkthrough | 00:16:19 Duration |
Section 19 : Supervised Learning Capstone Project
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Lecture 1 | Introduction to Supervised Learning Capstone | 00:15:50 Duration |
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Lecture 2 | Solution Walkthrough - Supervised Learning Pr | 00:23:02 Duration |
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Lecture 3 | Solution Walkthrough - Supervised Learning Pr | 00:31:05 Duration |
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Lecture 4 | Solution Walkthrough - Supervised Learning | 00:24:58 Duration |
Section 20 : Naive Bayes Classification and Natural Language Processing
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Lecture 1 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 2 | About Proctor Testing | |
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Lecture 3 | Naive Bayes Algorithm - Part Two - Model Algo | 00:19:54 Duration |
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Lecture 4 | Feature Extraction from Text - Part One - The | 00:13:17 Duration |
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Lecture 5 | Feature Extraction from Text - Coding Count V | 00:23:07 Duration |
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Lecture 6 | Feature Extraction from Text - Coding with Sc | 00:19:24 Duration |
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Lecture 7 | Natural Language Processing - Classification | 00:17:20 Duration |
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Lecture 8 | Natural Language Processing - Classification | 00:12:53 Duration |
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Lecture 9 | Text Classification Project Exercise Overview | 00:07:09 Duration |
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Lecture 10 | Text Classification Project Exercise Solution | 00:21:52 Duration |
Section 21 : Unsupervised Learning
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Lecture 1 | Unsupervised Learning Overview | 00:11:13 Duration |
Section 22 : K-Means Clustering
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Lecture 1 | Introduction to K-Means Clustering Section | 00:03:02 Duration |
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Lecture 2 | Clustering General Overview | 00:12:05 Duration |
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Lecture 3 | K-Means Clustering Theory | 00:11:29 Duration |
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Lecture 4 | K-Means Clustering - Coding Part One | 00:33:57 Duration |
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Lecture 5 | K-Means Clustering Coding Part Two | 00:18:01 Duration |
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Lecture 6 | K-Means Clustering Coding Part Three | 00:17:28 Duration |
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Lecture 7 | K-Means Color Quantization - Part One | 00:14:16 Duration |
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Lecture 8 | K-Means Color Quantization - Part Two | 00:15:21 Duration |
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Lecture 9 | K-Means Clustering Exercise Overview | 00:11:40 Duration |
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Lecture 10 | K-Means Clustering Exercise Solution - Part | 00:13:04 Duration |
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Lecture 11 | -Means Clustering Exercise Solution - Part | 00:16:23 Duration |
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Lecture 12 | K-Means Clustering Exercise Solution - Part T | 00:09:00 Duration |
Section 23 : Hierarchical Clustering
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Lecture 1 | Introduction to Hierarchical Clustering | 00:01:08 Duration |
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Lecture 2 | Hierarchical Clustering - Theory and Intuitio | 00:14:06 Duration |
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Lecture 3 | Hierarchical Clustering - Coding Part One | 00:19:59 Duration |
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Lecture 4 | Hierarchical Clustering - Coding Part Two - S | 00:32:31 Duration |
Section 24 : DBSCAN Density-based spatial clustering of applications
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Lecture 1 | Introduction to DBSCAN Section | 00:01:15 Duration |
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Lecture 2 | DBSCAN - Theory and Intuition | 00:19:30 Duration |
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Lecture 3 | DBSCAN versus K-Means Clustering | 00:16:20 Duration |
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Lecture 4 | DBSCAN - Hyperparameter Theory | 00:08:16 Duration |
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Lecture 5 | DBSCAN - Hyperparameter Tuning Methods | 00:25:58 Duration |
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Lecture 6 | DBSCAN - Outlier Project Exercise Overview | 00:06:52 Duration |
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Lecture 7 | DBSCAN - Outlier Project Exercise Solutions | 00:25:40 Duration |
Section 25 : PCA - Principal Component Analysis and Manifold Learning
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Lecture 1 | Introduction to Principal Component Analysis. | 00:02:45 Duration |
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Lecture 2 | PCA Theory and Intuition - Part One | 00:02:45 Duration |
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Lecture 3 | PCA Theory and Intuition - Part Two | 00:18:13 Duration |