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[GigaCourse.Com].url |
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001 A note from Jose on Feature Engineering and Data Preparation.html |
990B |
001 Capstone Project Overview__en.srt |
20.60KB |
001 Capstone Project Overview.mp4 |
31.11MB |
001 Early Bird Note on Downloading .zip for Logistic Regression Notes.html |
523B |
001 Introduction to Boosting Section__en.srt |
2.67KB |
001 Introduction to Boosting Section.mp4 |
2.99MB |
001 Introduction to DBSCAN Section__en.srt |
1.34KB |
001 Introduction to DBSCAN Section.mp4 |
1.80MB |
001 Introduction to Hierarchical Clustering__en.srt |
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001 Introduction to Hierarchical Clustering.mp4 |
1.67MB |
001 Introduction to K-Means Clustering Section__en.srt |
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001 Introduction to K-Means Clustering Section.mp4 |
3.55MB |
001 Introduction to KNN Section__en.srt |
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001 Introduction to KNN Section.mp4 |
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001 Introduction to Linear Regression Section__en.srt |
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001 Introduction to Linear Regression Section.mp4 |
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001 Introduction to Machine Learning Overview Section__en.srt |
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001 Introduction to Machine Learning Overview Section.mp4 |
13.17MB |
001 Introduction to Matplotlib__en.srt |
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001 Introduction to Matplotlib.mp4 |
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001 Introduction to NLP and Naive Bayes Section__en.srt |
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001 Introduction to NLP and Naive Bayes Section.mp4 |
4.22MB |
001 Introduction to NumPy__en.srt |
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001 Introduction to NumPy.mp4 |
3.37MB |
001 Introduction to Pandas__en.srt |
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001 Introduction to Pandas.mp4 |
6.70MB |
001 Introduction to Principal Component Analysis__en.srt |
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001 Introduction to Principal Component Analysis.mp4 |
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001 Introduction to Random Forests Section__en.srt |
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001 Introduction to Random Forests Section.mp4 |
2.87MB |
001 Introduction to Seaborn__en.srt |
6.51KB |
001 Introduction to Seaborn.mp4 |
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001 Introduction to Supervised Learning Capstone Project__en.srt |
25.69KB |
001 Introduction to Supervised Learning Capstone Project.mp4 |
29.84MB |
001 Introduction to Support Vector Machines__en.srt |
2.30KB |
001 Introduction to Support Vector Machines.mp4 |
2.79MB |
001 Introduction to Tree Based Methods__en.srt |
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001 Introduction to Tree Based Methods.mp4 |
2.33MB |
001 Machine Learning Pathway__en.srt |
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001 Machine Learning Pathway.mp4 |
14.10MB |
001 Model Deployment Section Overview__en.srt |
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001 Model Deployment Section Overview.mp4 |
4.16MB |
001 OPTIONAL_ Python Crash Course.html |
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001 Section Overview and Introduction__en.srt |
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001 Section Overview and Introduction.mp4 |
5.61MB |
001 Unsupervised Learning Overview__en.srt |
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001 Unsupervised Learning Overview.mp4 |
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001 Welcome to the Course_.html |
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002 Boosting Methods - Motivation and History__en.srt |
8.96KB |
002 Boosting Methods - Motivation and History.mp4 |
21.98MB |
002 Capstone Project Solutions - Part One__en.srt |
26.84KB |
002 Capstone Project Solutions - Part One.mp4 |
110.61MB |
002 Clustering General Overview__en.srt |
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002 Clustering General Overview.mp4 |
24.86MB |
002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP___en.srt |
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002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP_.mp4 |
7.22MB |
002 Cross Validation - Test _ Train Split__en.srt |
17.43KB |
002 Cross Validation - Test _ Train Split.mp4 |
46.86MB |
002 DBSCAN - Theory and Intuition__en.srt |
26.51KB |
002 DBSCAN - Theory and Intuition.mp4 |
109.09MB |
002 Decision Tree - History__en.srt |
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002 Decision Tree - History.mp4 |
35.58MB |
002 Hierarchical Clustering - Theory and Intuition__en.srt |
17.29KB |
002 Hierarchical Clustering - Theory and Intuition.mp4 |
52.07MB |
002 History of Support Vector Machines__en.srt |
6.53KB |
002 History of Support Vector Machines.mp4 |
15.54MB |
002 Introduction to Feature Engineering and Data Preparation__en.srt |
24.10KB |
002 Introduction to Feature Engineering and Data Preparation.mp4 |
36.11MB |
002 Introduction to Logistic Regression Section__en.srt |
8.39KB |
002 Introduction to Logistic Regression Section.mp4 |
13.93MB |
002 KNN Classification - Theory and Intuition__en.srt |
16.93KB |
002 KNN Classification - Theory and Intuition.mp4 |
23.55MB |
002 Linear Regression - Algorithm History__en.srt |
13.09KB |
002 Linear Regression - Algorithm History.mp4 |
54.82MB |
002 Matplotlib Basics__en.srt |
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002 Matplotlib Basics.mp4 |
31.07MB |
002 Model Deployment Considerations__en.srt |
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002 Model Deployment Considerations.mp4 |
18.31MB |
002 Naive Bayes Algorithm - Part One - Bayes Theorem__en.srt |
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002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 |
22.04MB |
002 NumPy Arrays__en.srt |
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002 NumPy Arrays.mp4 |
99.45MB |
002 PCA Theory and Intuition - Part One__en.srt |
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002 PCA Theory and Intuition - Part One.mp4 |
29.72MB |
002 Python Crash Course - Part One__en.srt |
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002 Python Crash Course - Part One.mp4 |
29.74MB |
002 Random Forests - History and Motivation__en.srt |
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002 Random Forests - History and Motivation.mp4 |
24.00MB |
002 Scatterplots with Seaborn__en.srt |
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002 Scatterplots with Seaborn.mp4 |
111.30MB |
002 Series - Part One__en.srt |
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002 Series - Part One.mp4 |
28.62MB |
002 Solution Walkthrough - Supervised Learning Project - Data and EDA__en.srt |
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002 Solution Walkthrough - Supervised Learning Project - Data and EDA.mp4 |
106.10MB |
002 Why Machine Learning___en.srt |
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002 Why Machine Learning_.mp4 |
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003 AdaBoost Theory and Intuition__en.srt |
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003 AdaBoost Theory and Intuition.mp4 |
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003 Anaconda Python and Jupyter Install and Setup__en.srt |
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003 Anaconda Python and Jupyter Install and Setup.mp4 |
84.53MB |
003 Capstone Project Solutions - Part Two__en.srt |
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003 Capstone Project Solutions - Part Two.mp4 |
106.18MB |
003 Cross Validation - Test _ Validation _ Train Split__en.srt |
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003 Cross Validation - Test _ Validation _ Train Split.mp4 |
59.41MB |
003 DBSCAN versus K-Means Clustering__en.srt |
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003 DBSCAN versus K-Means Clustering.mp4 |
66.64MB |
003 Dealing with Outliers__en.srt |
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003 Dealing with Outliers.mp4 |
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003 Decision Tree - Terminology__en.srt |
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003 Decision Tree - Terminology.mp4 |
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003 Distribution Plots - Part One - Understanding Plot Types__en.srt |
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003 Distribution Plots - Part One - Understanding Plot Types.mp4 |
15.03MB |
003 Hierarchical Clustering - Coding Part One - Data and Visualization__en.srt |
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003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4 |
114.98MB |
003 K-Means Clustering Theory__en.srt |
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003 K-Means Clustering Theory.mp4 |
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003 KNN Coding with Python - Part One__en.srt |
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003 KNN Coding with Python - Part One_en.vtt |
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003 KNN Coding with Python - Part One.mp4 |
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003 Linear Regression - Understanding Ordinary Least Squares__en.srt |
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003 Linear Regression - Understanding Ordinary Least Squares.mp4 |
86.37MB |
003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function__en.srt |
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003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp4 |
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003 Matplotlib - Understanding the Figure Object__en.srt |
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003 Matplotlib - Understanding the Figure Object.mp4 |
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003 Model Persistence__en.srt |
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003 Model Persistence_en.vtt |
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003 Model Persistence.mp4 |
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003 Naive Bayes Algorithm - Part Two - Model Algorithm__en.srt |
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003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp4 |
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003 NumPy Indexing and Selection__en.srt |
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003 NumPy Indexing and Selection.mp4 |
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003 PCA Theory and Intuition - Part Two__en.srt |
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003 PCA Theory and Intuition - Part Two.mp4 |
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003 Python Crash Course - Part Two__en.srt |
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003 Python Crash Course - Part Two.mp4 |
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003 Random Forests - Key Hyperparameters__en.srt |
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003 Random Forests - Key Hyperparameters.mp4 |
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003 Series - Part Two__en.srt |
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003 Series - Part Two.mp4 |
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003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis__en.srt |
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003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis.mp4 |
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003 SVM - Theory and Intuition - Hyperplanes and Margins__en.srt |
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003 SVM - Theory and Intuition - Hyperplanes and Margins.mp4 |
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003 Types of Machine Learning Algorithms__en.srt |
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003 Types of Machine Learning Algorithms.mp4 |
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004 AdaBoost Coding Part One - The Data__en.srt |
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004 AdaBoost Coding Part One - The Data.mp4 |
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004 Capstone Project Solutions - Part Three__en.srt |
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004 Capstone Project Solutions - Part Three.mp4 |
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004 Cross Validation - cross_val_score__en.srt |
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004 Cross Validation - cross_val_score_en.vtt |
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004 Cross Validation - cross_val_score.mp4 |
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004 DataFrames - Part One - Creating a DataFrame__en.srt |
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004 DataFrames - Part One - Creating a DataFrame.mp4 |
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004 DBSCAN - Hyperparameter Theory__en.srt |
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004 DBSCAN - Hyperparameter Theory.mp4 |
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004 Dealing with Missing Data _ Part One - Evaluation of Missing Data__en.srt |
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004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp4 |
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004 Decision Tree - Understanding Gini Impurity__en.srt |
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004 Decision Tree - Understanding Gini Impurity.mp4 |
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004 Distribution Plots - Part Two - Coding with Seaborn__en.srt |
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004 Distribution Plots - Part Two - Coding with Seaborn.mp4 |
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004 Feature Extraction from Text - Part One - Theory and Intuition__en.srt |
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004 Feature Extraction from Text - Part One - Theory and Intuition.mp4 |
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004 Hierarchical Clustering - Coding Part Two - Scikit-Learn__en.srt |
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004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4 |
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004 K-Means Clustering - Coding Part One__en.srt |
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004 K-Means Clustering - Coding Part One.mp4 |
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004 KNN Coding with Python - Part Two - Choosing K__en.srt |
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004 KNN Coding with Python - Part Two - Choosing K_en.vtt |
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004 KNN Coding with Python - Part Two - Choosing K.mp4 |
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004 Linear Regression - Cost Functions__en.srt |
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004 Linear Regression - Cost Functions.mp4 |
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004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic__en.srt |
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004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp4 |
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004 Matplotlib - Implementing Figures and Axes__en.srt |
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004 Matplotlib - Implementing Figures and Axes.mp4 |
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004 Model Deployment as an API - General Overview__en.srt |
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004 Model Deployment as an API - General Overview.mp4 |
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004 Note on Environment Setup - Please read me_.html |
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004 NumPy Operations__en.srt |
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004 NumPy Operations.mp4 |
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004 PCA - Manual Implementation in Python__en.srt |
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004 PCA - Manual Implementation in Python.mp4 |
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004 Python Crash Course - Part Three__en.srt |
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004 Python Crash Course - Part Three.mp4 |
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004 Random Forests - Number of Estimators and Features in Subsets__en.srt |
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004 Random Forests - Number of Estimators and Features in Subsets.mp4 |
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004 Solution Walkthrough - Supervised Learning Project - Tree Models__en.srt |
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004 Solution Walkthrough - Supervised Learning Project - Tree Models_en.vtt |
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004 Solution Walkthrough - Supervised Learning Project - Tree Models.mp4 |
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004 Supervised Machine Learning Process__en.srt |
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004 Supervised Machine Learning Process.mp4 |
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004 SVM - Theory and Intuition - Kernel Intuition__en.srt |
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004 SVM - Theory and Intuition - Kernel Intuition.mp4 |
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005 AdaBoost Coding Part Two - The Model__en.srt |
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005 AdaBoost Coding Part Two - The Model.mp4 |
63.11MB |
005 Categorical Plots - Statistics within Categories - Understanding Plot Types__en.srt |
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005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 |
15.98MB |
005 Companion Book - Introduction to Statistical Learning__en.srt |
4.66KB |
005 Companion Book - Introduction to Statistical Learning.mp4 |
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005 Constructing Decision Trees with Gini Impurity - Part One__en.srt |
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005 Constructing Decision Trees with Gini Impurity - Part One.mp4 |
17.69MB |
005 Cross Validation - cross_validate__en.srt |
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005 Cross Validation - cross_validate.mp4 |
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005 DataFrames - Part Two - Basic Properties__en.srt |
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005 DataFrames - Part Two - Basic Properties.mp4 |
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005 DBSCAN - Hyperparameter Tuning Methods__en.srt |
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005 DBSCAN - Hyperparameter Tuning Methods.mp4 |
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005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows__en.srt |
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005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4 |
117.56MB |
005 Environment Setup__en.srt |
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005 Environment Setup.mp4 |
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005 Feature Extraction from Text - Coding Count Vectorization Manually__en.srt |
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005 Feature Extraction from Text - Coding Count Vectorization Manually.mp4 |
62.89MB |
005 K-Means Clustering Coding Part Two__en.srt |
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005 K-Means Clustering Coding Part Two.mp4 |
80.85MB |
005 KNN Classification Project Exercise Overview__en.srt |
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005 KNN Classification Project Exercise Overview.mp4 |
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005 Linear Regression - Gradient Descent__en.srt |
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005 Linear Regression - Gradient Descent.mp4 |
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005 Logistic Regression - Theory and Intuition - Linear to Logistic Math__en.srt |
24.81KB |
005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 |
36.04MB |
005 Matplotlib - Figure Parameters__en.srt |
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005 Matplotlib - Figure Parameters.mp4 |
13.06MB |
005 Note on Upcoming Video.html |
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005 NumPy Exercises__en.srt |
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005 NumPy Exercises.mp4 |
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005 PCA - SciKit-Learn__en.srt |
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005 PCA - SciKit-Learn.mp4 |
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005 Python Crash Course - Exercise Questions__en.srt |
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005 Python Crash Course - Exercise Questions.mp4 |
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005 Random Forests - Bootstrapping and Out-of-Bag Error__en.srt |
17.97KB |
005 Random Forests - Bootstrapping and Out-of-Bag Error.mp4 |
32.72MB |
005 SVM - Theory and Intuition - Kernel Trick and Mathematics__en.srt |
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005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 |
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006 Categorical Plots - Statistics within Categories - Coding with Seaborn__en.srt |
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006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 |
51.65MB |
006 Coding Classification with Random Forest Classifier - Part One__en.srt |
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006 Coding Classification with Random Forest Classifier - Part One_en.vtt |
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006 Coding Classification with Random Forest Classifier - Part One.mp4 |
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006 Constructing Decision Trees with Gini Impurity - Part Two__en.srt |
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006 Constructing Decision Trees with Gini Impurity - Part Two.mp4 |
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006 DataFrames - Part Three - Working with Columns__en.srt |
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006 DataFrames - Part Three - Working with Columns.mp4 |
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006 DBSCAN - Outlier Project Exercise Overview__en.srt |
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006 DBSCAN - Outlier Project Exercise Overview.mp4 |
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006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns__en.srt |
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006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4 |
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006 Feature Extraction from Text - Coding with Scikit-Learn__en.srt |
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006 Feature Extraction from Text - Coding with Scikit-Learn.mp4 |
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006 Gradient Boosting Theory__en.srt |
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006 Gradient Boosting Theory.mp4 |
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006 Grid Search__en.srt |
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006 Grid Search.mp4 |
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006 K-Means Clustering Coding Part Three__en.srt |
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006 K-Means Clustering Coding Part Three.mp4 |
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006 KNN Classification Project Exercise Solutions__en.srt |
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006 KNN Classification Project Exercise Solutions_en.vtt |
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006 KNN Classification Project Exercise Solutions.mp4 |
105.03MB |
006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood__en.srt |
22.96KB |
006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 |
54.91MB |
006 Matplotlib - Subplots Functionality__en.srt |
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006 Matplotlib - Subplots Functionality.mp4 |
96.57MB |
006 Model API - Creating the Script__en.srt |
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006 Model API - Creating the Script.mp4 |
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006 Numpy Exercises - Solutions__en.srt |
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006 Numpy Exercises - Solutions.mp4 |
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006 PCA - Project Exercise Overview__en.srt |
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006 PCA - Project Exercise Overview.mp4 |
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006 Python coding Simple Linear Regression__en.srt |
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006 Python coding Simple Linear Regression.mp4 |
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006 Python Crash Course - Exercise Solutions__en.srt |
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006 Python Crash Course - Exercise Solutions.mp4 |
48.70MB |
006 SVM with Scikit-Learn and Python - Classification Part One__en.srt |
16.39KB |
006 SVM with Scikit-Learn and Python - Classification Part One.mp4 |
46.28MB |
007 Categorical Plots - Distributions within Categories - Understanding Plot Types__en.srt |
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007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 |
44.96MB |
007 Coding Classification with Random Forest Classifier - Part Two__en.srt |
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007 Coding Classification with Random Forest Classifier - Part Two_en.vtt |
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007 Coding Classification with Random Forest Classifier - Part Two.mp4 |
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007 Coding Decision Trees - Part One - The Data__en.srt |
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007 Coding Decision Trees - Part One - The Data.mp4 |
98.72MB |
007 DataFrames - Part Four - Working with Rows__en.srt |
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007 DataFrames - Part Four - Working with Rows.mp4 |
72.59MB |
007 DBSCAN - Outlier Project Exercise Solutions__en.srt |
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007 DBSCAN - Outlier Project Exercise Solutions.mp4 |
127.93MB |
007 Dealing with Categorical Data - Encoding Options__en.srt |
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007 Dealing with Categorical Data - Encoding Options.mp4 |
58.87MB |
007 Gradient Boosting Coding Walkthrough__en.srt |
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007 Gradient Boosting Coding Walkthrough_en.vtt |
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007 Gradient Boosting Coding Walkthrough.mp4 |
57.91MB |
007 K-Means Color Quantization - Part One__en.srt |
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007 K-Means Color Quantization - Part One.mp4 |
80.57MB |
007 Linear Regression Project Overview__en.srt |
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007 Linear Regression Project Overview.mp4 |
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007 Logistic Regression with Scikit-Learn - Part One - EDA__en.srt |
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007 Logistic Regression with Scikit-Learn - Part One - EDA.mp4 |
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007 Matplotlib Styling - Legends__en.srt |
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007 Matplotlib Styling - Legends.mp4 |
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007 Natural Language Processing - Classification of Text - Part One__en.srt |
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007 Natural Language Processing - Classification of Text - Part One.mp4 |
28.26MB |
007 Overview of Scikit-Learn and Python__en.srt |
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007 Overview of Scikit-Learn and Python_en.vtt |
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007 Overview of Scikit-Learn and Python.mp4 |
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007 PCA - Project Exercise Solution__en.srt |
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007 PCA - Project Exercise Solution.mp4 |
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007 SVM with Scikit-Learn and Python - Classification Part Two__en.srt |
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007 SVM with Scikit-Learn and Python - Classification Part Two_en.vtt |
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007 SVM with Scikit-Learn and Python - Classification Part Two.mp4 |
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007 Testing the API__en.srt |
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007 Testing the API.mp4 |
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008 Categorical Plots - Distributions within Categories - Coding with Seaborn__en.srt |
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008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 |
84.57MB |
008 Coding Decision Trees - Part Two -Creating the Model__en.srt |
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008 Coding Decision Trees - Part Two -Creating the Model.mp4 |
115.80MB |
008 Coding Regression with Random Forest Regressor - Part One - Data__en.srt |
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008 Coding Regression with Random Forest Regressor - Part One - Data.mp4 |
13.68MB |
008 K-Means Color Quantization - Part Two__en.srt |
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008 K-Means Color Quantization - Part Two.mp4 |
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008 Linear Regression Project - Solutions__en.srt |
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008 Linear Regression Project - Solutions_en.vtt |
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008 Linear Regression Project - Solutions.mp4 |
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008 Linear Regression - Scikit-Learn Train Test Split__en.srt |
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008 Linear Regression - Scikit-Learn Train Test Split.mp4 |
61.42MB |
008 Logistic Regression with Scikit-Learn - Part Two - Model Training__en.srt |
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008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 |
32.57MB |
008 Matplotlib Styling - Colors and Styles__en.srt |
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008 Matplotlib Styling - Colors and Styles.mp4 |
44.27MB |
008 Natural Language Processing - Classification of Text - Part Two__en.srt |
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008 Natural Language Processing - Classification of Text - Part Two.mp4 |
34.77MB |
008 Pandas - Conditional Filtering__en.srt |
27.14KB |
008 Pandas - Conditional Filtering.mp4 |
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