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| [CourseClub.Me].url |
122B |
| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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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 |
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| 001 Introduction to DBSCAN Section.mp4 |
1.80MB |
| 001 Introduction to Hierarchical Clustering__en.srt |
1.17KB |
| 001 Introduction to Hierarchical Clustering.mp4 |
1.67MB |
| 001 Introduction to K-Means Clustering Section__en.srt |
3.50KB |
| 001 Introduction to K-Means Clustering Section.mp4 |
3.55MB |
| 001 Introduction to KNN Section__en.srt |
3.63KB |
| 001 Introduction to KNN Section.mp4 |
3.65MB |
| 001 Introduction to Linear Regression Section__en.srt |
2.68KB |
| 001 Introduction to Linear Regression Section.mp4 |
2.58MB |
| 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 |
3.97KB |
| 001 Introduction to Principal Component Analysis.mp4 |
5.08MB |
| 001 Introduction to Random Forests Section__en.srt |
2.81KB |
| 001 Introduction to Random Forests Section.mp4 |
2.87MB |
| 001 Introduction to Seaborn__en.srt |
6.51KB |
| 001 Introduction to Seaborn.mp4 |
5.74MB |
| 001 Introduction to Supervised Learning Capstone Project__en.srt |
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| 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 |
15.79KB |
| 001 Machine Learning Pathway.mp4 |
14.10MB |
| 001 Model Deployment Section Overview__en.srt |
3.49KB |
| 001 Model Deployment Section Overview.mp4 |
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| 001 OPTIONAL_ Python Crash Course.html |
472B |
| 001 Section Overview and Introduction__en.srt |
5.05KB |
| 001 Section Overview and Introduction.mp4 |
5.61MB |
| 001 Unsupervised Learning Overview__en.srt |
12.86KB |
| 001 Unsupervised Learning Overview.mp4 |
13.75MB |
| 001 Welcome to the Course_.html |
1.64KB |
| 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 |
16.50KB |
| 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 |
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| 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 |
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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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31.07MB |
| 002 Model Deployment Considerations__en.srt |
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18.31MB |
| 002 Naive Bayes Algorithm - Part One - Bayes Theorem__en.srt |
11.85KB |
| 002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 |
22.04MB |
| 002 NumPy Arrays__en.srt |
31.91KB |
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| 002 PCA Theory and Intuition - Part One__en.srt |
15.60KB |
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29.72MB |
| 002 Python Crash Course - Part One__en.srt |
24.63KB |
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29.74MB |
| 002 Random Forests - History and Motivation__en.srt |
17.22KB |
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24.00MB |
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29.72KB |
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111.30MB |
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106.10MB |
| 002 Why Machine Learning___en.srt |
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21.04MB |
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28.95KB |
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41.53MB |
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84.53MB |
| 003 Capstone Project Solutions - Part Two__en.srt |
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| 003 Cross Validation - Test _ Validation _ Train Split__en.srt |
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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 |
103.32MB |
| 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 |
15.00KB |
| 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 K-Means Clustering Theory__en.srt |
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| 003 KNN Coding with Python - Part One__en.srt |
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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 |
22.53KB |
| 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 Naive Bayes Algorithm - Part Two - Model Algorithm__en.srt |
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48.61MB |
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| 003 PCA Theory and Intuition - Part Two__en.srt |
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19.04MB |
| 003 Python Crash Course - Part Two__en.srt |
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| 003 Random Forests - Key Hyperparameters__en.srt |
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| 003 Series - Part Two__en.srt |
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| 003 Types of Machine Learning Algorithms__en.srt |
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| 004 Capstone Project Solutions - Part Three__en.srt |
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97.48MB |
| 004 DBSCAN - Hyperparameter Theory__en.srt |
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| 004 Dealing with Missing Data _ Part One - Evaluation of Missing Data__en.srt |
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| 004 Decision Tree - Understanding Gini Impurity__en.srt |
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| 004 Distribution Plots - Part Two - Coding with Seaborn__en.srt |
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| 004 Feature Extraction from Text - Part One - Theory and Intuition__en.srt |
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| 004 Hierarchical Clustering - Coding Part Two - Scikit-Learn__en.srt |
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| 004 K-Means Clustering - Coding Part One__en.srt |
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| 004 Linear Regression - Cost Functions__en.srt |
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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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34.86MB |
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17.48MB |
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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 |
5.11MB |
| 005 Constructing Decision Trees with Gini Impurity - Part One__en.srt |
11.48KB |
| 005 Constructing Decision Trees with Gini Impurity - Part One.mp4 |
17.69MB |
| 005 Cross Validation - cross_validate__en.srt |
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| 005 DataFrames - Part Two - Basic Properties__en.srt |
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40.28MB |
| 005 DBSCAN - Hyperparameter Tuning Methods__en.srt |
32.66KB |
| 005 DBSCAN - Hyperparameter Tuning Methods.mp4 |
105.08MB |
| 005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows__en.srt |
31.42KB |
| 005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4 |
117.56MB |
| 005 Environment Setup__en.srt |
14.49KB |
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35.71MB |
| 005 Feature Extraction from Text - Coding Count Vectorization Manually__en.srt |
27.22KB |
| 005 Feature Extraction from Text - Coding Count Vectorization Manually.mp4 |
62.89MB |
| 005 K-Means Clustering Coding Part Two__en.srt |
26.55KB |
| 005 K-Means Clustering Coding Part Two.mp4 |
80.85MB |
| 005 KNN Classification Project Exercise Overview__en.srt |
5.23KB |
| 005 KNN Classification Project Exercise Overview.mp4 |
21.12MB |
| 005 Linear Regression - Gradient Descent__en.srt |
16.73KB |
| 005 Linear Regression - Gradient Descent.mp4 |
29.21MB |
| 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 PCA - SciKit-Learn__en.srt |
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| 005 Python Crash Course - Exercise Questions__en.srt |
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3.41MB |
| 005 Random Forests - Bootstrapping and Out-of-Bag Error__en.srt |
17.97KB |
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32.72MB |
| 005 SVM - Theory and Intuition - Kernel Trick and Mathematics__en.srt |
29.30KB |
| 005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 |
52.62MB |
| 006 Categorical Plots - Statistics within Categories - Coding with Seaborn__en.srt |
14.61KB |
| 006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 |
51.65MB |
| 006 Coding Classification with Random Forest Classifier - Part One__en.srt |
9.92KB |
| 006 Coding Classification with Random Forest Classifier - Part One_en.vtt |
15.78KB |
| 006 Coding Classification with Random Forest Classifier - Part One.mp4 |
52.10MB |
| 006 Constructing Decision Trees with Gini Impurity - Part Two__en.srt |
16.42KB |
| 006 Constructing Decision Trees with Gini Impurity - Part Two.mp4 |
52.35MB |
| 006 DataFrames - Part Three - Working with Columns__en.srt |
20.61KB |
| 006 DataFrames - Part Three - Working with Columns.mp4 |
84.08MB |
| 006 DBSCAN - Outlier Project Exercise Overview__en.srt |
9.96KB |
| 006 DBSCAN - Outlier Project Exercise Overview.mp4 |
50.27MB |
| 006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns__en.srt |
36.75KB |
| 006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4 |
105.22MB |
| 006 Feature Extraction from Text - Coding with Scikit-Learn__en.srt |
16.67KB |
| 006 Feature Extraction from Text - Coding with Scikit-Learn.mp4 |
50.39MB |
| 006 Gradient Boosting Theory__en.srt |
16.11KB |
| 006 Gradient Boosting Theory.mp4 |
22.96MB |
| 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 |
21.38KB |
| 006 K-Means Clustering Coding Part Three.mp4 |
59.77MB |
| 006 KNN Classification Project Exercise Solutions__en.srt |
8.62KB |
| 006 KNN Classification Project Exercise Solutions_en.vtt |
18.55KB |
| 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 |
28.63KB |
| 006 Matplotlib - Subplots Functionality.mp4 |
96.57MB |
| 006 Model API - Creating the Script__en.srt |
26.06KB |
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67.27MB |
| 006 Numpy Exercises - Solutions__en.srt |
10.87KB |
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34.88MB |
| 006 PCA - Project Exercise Overview__en.srt |
11.87KB |
| 006 PCA - Project Exercise Overview.mp4 |
52.77MB |
| 006 Python coding Simple Linear Regression__en.srt |
28.14KB |
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70.14MB |
| 006 Python Crash Course - Exercise Solutions__en.srt |
13.43KB |
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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 |
20.10KB |
| 007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 |
44.96MB |
| 007 Coding Classification with Random Forest Classifier - Part Two__en.srt |
20.04KB |
| 007 Coding Classification with Random Forest Classifier - Part Two_en.vtt |
27.90KB |
| 007 Coding Classification with Random Forest Classifier - Part Two.mp4 |
130.37MB |
| 007 Coding Decision Trees - Part One - The Data__en.srt |
29.28KB |
| 007 Coding Decision Trees - Part One - The Data.mp4 |
98.72MB |
| 007 DataFrames - Part Four - Working with Rows__en.srt |
21.09KB |
| 007 DataFrames - Part Four - Working with Rows.mp4 |
72.59MB |
| 007 DBSCAN - Outlier Project Exercise Solutions__en.srt |
38.12KB |
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127.93MB |
| 007 Dealing with Categorical Data - Encoding Options__en.srt |
20.10KB |
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58.87MB |
| 007 Gradient Boosting Coding Walkthrough__en.srt |
8.90KB |
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17.50KB |
| 007 Gradient Boosting Coding Walkthrough.mp4 |
57.91MB |
| 007 K-Means Color Quantization - Part One__en.srt |
20.38KB |
| 007 K-Means Color Quantization - Part One.mp4 |
80.57MB |
| 007 Linear Regression Project Overview__en.srt |
5.82KB |
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23.63MB |
| 007 Logistic Regression with Scikit-Learn - Part One - EDA__en.srt |
21.90KB |
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62.45MB |
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10.36KB |
| 007 Matplotlib Styling - Legends.mp4 |
16.19MB |
| 007 Natural Language Processing - Classification of Text - Part One__en.srt |
16.42KB |
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28.26MB |
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10.14KB |
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10.96KB |
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31.44MB |
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25.72KB |
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119.45MB |
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20.73KB |
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84.57MB |
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