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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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| [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 |
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| 001 Capstone Project Overview__en.srt |
20.60Кб |
| 001 Capstone Project Overview.mp4 |
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| 001 Early Bird Note on Downloading .zip for Logistic Regression Notes.html |
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| 001 Introduction to Boosting Section__en.srt |
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| 001 Introduction to Boosting Section.mp4 |
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| 001 Introduction to DBSCAN Section__en.srt |
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| 001 Introduction to DBSCAN Section.mp4 |
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| 001 Introduction to Hierarchical Clustering__en.srt |
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| 001 Introduction to Hierarchical Clustering.mp4 |
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| 001 Introduction to K-Means Clustering Section__en.srt |
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| 001 Introduction to K-Means Clustering Section.mp4 |
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| 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 |
2.58Мб |
| 001 Introduction to Machine Learning Overview Section__en.srt |
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| 001 Introduction to Machine Learning Overview Section.mp4 |
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| 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 |
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| 001 Introduction to NumPy__en.srt |
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| 001 Introduction to NumPy.mp4 |
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| 001 Introduction to Pandas__en.srt |
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| 001 Introduction to Pandas.mp4 |
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| 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 |
2.81Кб |
| 001 Introduction to Random Forests Section.mp4 |
2.87Мб |
| 001 Introduction to Seaborn__en.srt |
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| 001 Introduction to Seaborn.mp4 |
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| 001 Introduction to Supervised Learning Capstone Project__en.srt |
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| 001 Introduction to Supervised Learning Capstone Project.mp4 |
29.84Мб |
| 001 Introduction to Support Vector Machines__en.srt |
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| 001 Introduction to Support Vector Machines.mp4 |
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| 001 Introduction to Tree Based Methods__en.srt |
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| 001 Introduction to Tree Based Methods.mp4 |
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| 001 Machine Learning Pathway__en.srt |
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| 001 Machine Learning Pathway.mp4 |
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| 001 Model Deployment Section Overview__en.srt |
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| 001 Model Deployment Section Overview.mp4 |
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| 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 |
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| 001 Unsupervised Learning Overview__en.srt |
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| 001 Welcome to the Course_.html |
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| 002 Boosting Methods - Motivation and History__en.srt |
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| 002 Boosting Methods - Motivation and History.mp4 |
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| 002 Capstone Project Solutions - Part One__en.srt |
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| 002 Capstone Project Solutions - Part One.mp4 |
110.61Мб |
| 002 Clustering General Overview__en.srt |
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| 002 Clustering General Overview.mp4 |
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| 002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP___en.srt |
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| 002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP_.mp4 |
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| 002 Cross Validation - Test _ Train Split__en.srt |
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| 002 Cross Validation - Test _ Train Split.mp4 |
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| 002 DBSCAN - Theory and Intuition__en.srt |
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| 002 DBSCAN - Theory and Intuition.mp4 |
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| 002 Decision Tree - History__en.srt |
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| 002 Decision Tree - History.mp4 |
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| 002 Hierarchical Clustering - Theory and Intuition__en.srt |
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| 002 Hierarchical Clustering - Theory and Intuition.mp4 |
52.07Мб |
| 002 History of Support Vector Machines__en.srt |
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| 002 History of Support Vector Machines.mp4 |
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| 002 Introduction to Feature Engineering and Data Preparation__en.srt |
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| 002 Introduction to Feature Engineering and Data Preparation.mp4 |
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| 002 Introduction to Logistic Regression Section__en.srt |
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| 002 KNN Classification - Theory and Intuition__en.srt |
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| 002 KNN Classification - Theory and Intuition.mp4 |
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| 002 Linear Regression - Algorithm History__en.srt |
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| 002 Matplotlib Basics__en.srt |
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| 002 Naive Bayes Algorithm - Part One - Bayes Theorem__en.srt |
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| 002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 |
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| 002 NumPy Arrays__en.srt |
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| 002 PCA Theory and Intuition - Part One__en.srt |
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| 002 Python Crash Course - Part One__en.srt |
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| 002 Random Forests - History and Motivation__en.srt |
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| 002 Why Machine Learning___en.srt |
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| 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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| 003 Cross Validation - Test _ Validation _ Train Split.mp4 |
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| 003 DBSCAN versus K-Means Clustering__en.srt |
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| 003 DBSCAN versus K-Means Clustering.mp4 |
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| 003 Dealing with Outliers__en.srt |
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| 003 Decision Tree - Terminology__en.srt |
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| 003 Distribution Plots - Part One - Understanding Plot Types__en.srt |
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| 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 Linear Regression - Understanding Ordinary Least Squares__en.srt |
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| 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 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 Solution Walkthrough - Supervised Learning Project - Cohort Analysis__en.srt |
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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 Capstone Project Solutions - Part Three__en.srt |
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| 004 Cross Validation - cross_val_score__en.srt |
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| 004 DataFrames - Part One - Creating a DataFrame__en.srt |
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| 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 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 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 Note on Environment Setup - Please read me_.html |
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| 005 Companion Book - Introduction to Statistical Learning__en.srt |
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| 005 Constructing Decision Trees with Gini Impurity - Part One__en.srt |
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| 005 DBSCAN - Hyperparameter Tuning Methods__en.srt |
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| 005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows__en.srt |
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| 006 Categorical Plots - Statistics within Categories - Coding with Seaborn__en.srt |
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| 006 Coding Classification with Random Forest Classifier - Part One__en.srt |
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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 Feature Extraction from Text - Coding with Scikit-Learn__en.srt |
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| 006 Gradient Boosting Theory__en.srt |
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| 006 K-Means Clustering Coding Part Three__en.srt |
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| 006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood__en.srt |
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| 006 Matplotlib - Subplots Functionality__en.srt |
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| 006 Python coding Simple Linear Regression__en.srt |
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| 006 Python Crash Course - Exercise Solutions__en.srt |
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| 006 SVM with Scikit-Learn and Python - Classification Part One__en.srt |
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| 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 |
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| 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 DataFrames - Part Four - Working with Rows__en.srt |
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| 007 DBSCAN - Outlier Project Exercise Solutions__en.srt |
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| 007 K-Means Color Quantization - Part One__en.srt |
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| 007 Linear Regression Project Overview__en.srt |
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