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The Complete Ensemble Learning Course 2021 With Python
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[TutsNode.com] - The Complete Ensemble Learning Course 2021 With Python (Size: 6.75 GB) (Files: 342)
[TutsNode.com] - The Complete Ensemble Learning Course 2021 With Python
05 Stacking Method
004 Stacking for regression Implementation.mp4
004 Stacking for regression Implementation.en.srt
005 Stacking for classification Implementation.en.srt
005 Stacking for classification Implementation.mp4
040 Udemy_Stacking_for_regression_Implementation.ipynb
041 Udemy_Stacking_for_classification_Implementation.ipynb
001 Introduction to Stacking Method.en.srt
006 Summary of the section.en.srt
002 Introduction to Meta-Learning.en.srt
003 Selecting base learners and meta-learner.en.srt
003 Selecting base learners and meta-learner.mp4
002 Introduction to Meta-Learning.mp4
006 Summary of the section.mp4
001 Introduction to Stacking Method.mp4
01 Introduction
001 Course structure.en.srt
002 How To Make The Most Out Of This Course.en.srt
003 Who is this course for____.en.srt
005 IMPORTANT NOTE on tools.en.srt
004 IMPORTANT term.en.srt
004 IMPORTANT term.mp4
003 Who is this course for____.mp4
001 Course structure.mp4
002 How To Make The Most Out Of This Course.mp4
005 IMPORTANT NOTE on tools.mp4
03 Basic Ensemble Learning concept
024 Validation_Curves_Implementation.ipynb
025 Udemy_Learning_Curves_Implementation.ipynb
005 Validation Curves Implementation.en.srt
006 Learning Curves Implementation.en.srt
001 What is ensemble learning.en.srt
002 What is bias_.en.srt
008 Challenges in Ensemble Learning.en.srt
003 What is variance and Trade-off_.en.srt
004 What is Motivation_.en.srt
007 Methods of Ensemble Learning.en.srt
009 Summary of the section.en.srt
005 Validation Curves Implementation.mp4
006 Learning Curves Implementation.mp4
008 Challenges in Ensemble Learning.mp4
003 What is variance and Trade-off_.mp4
004 What is Motivation_.mp4
009 Summary of the section.mp4
007 Methods of Ensemble Learning.mp4
002 What is bias_.mp4
001 What is ensemble learning.mp4
10 Predicting Bitcoin Prices - REAL WORLD PROBLEMS
081 Bitcoin_data_analysis.ipynb
088 Udemy_Boosting_Implementation_for_bitcoin_price_Prediction.ipynb
081 BTC-USD.csv
086 Udemy_Stacking_Implementation_for_bitcoin_price_Prediction.ipynb
083 Udemy_Simple_Bitcoin_Prediction_Implementation.ipynb
085 Udemy_Voting_Implementation_for_bitcoin_price_Prediction.ipynb
089 Udemy_Random_Forest_Implementation_for_bitcoin_price_Prediction.ipynb
087 Udemy_Bagging_Implementation_for_bitcoin_price_Prediction.ipynb
008 Stacking Implementation.en.srt
005 Simple Bitcoin Prediction.en.srt
007 Voting Implementation.en.srt
006 Simulator Implementation.en.srt
003 Bitcoin data analysis Implementation Part 1.en.srt
004 Bitcoin data analysis Implementation Part 2.en.srt
011 Random Forest Implementation.en.srt
010 Boosting Implementation.en.srt
009 Bagging Implementation.en.srt
084 Udemy_Simulator_Implemetation.ipynb
002 Introduction to the time series.en.srt
012 Summary of the project.en.srt
001 Introduction to the project.en.srt
008 Stacking Implementation.mp4
005 Simple Bitcoin Prediction.mp4
006 Simulator Implementation.mp4
007 Voting Implementation.mp4
010 Boosting Implementation.mp4
003 Bitcoin data analysis Implementation Part 1.mp4
011 Random Forest Implementation.mp4
009 Bagging Implementation.mp4
004 Bitcoin data analysis Implementation Part 2.mp4
002 Introduction to the time series.mp4
012 Summary of the project.mp4
001 Introduction to the project.mp4
09 Clustering
076 Udemy_K_Means_Clustering_Implementation_with_Scikit_Learn.ipynb
077 Udemy_Voting_Example_Implementation.ipynb
005 K-Means Implementation by using Voting.en.srt
004 K-means Implementation Part 2.en.srt
003 K-means Implementation Part 1.en.srt
002 Hierarchical and K-means clustering and strengths and weaknesses of K-means.en.srt
006 Summary of the section.en.srt
001 Introduction to the clustering.en.srt
004 K-means Implementation Part 2.mp4
003 K-means Implementation Part 1.mp4
005 K-Means Implementation by using Voting.mp4
002 Hierarchical and K-means clustering and strengths and weaknesses of K-means.mp4
006 Summary of the section.mp4
001 Introduction to the clustering.mp4
02 Basic Machine Learning concept
001 What is machine learning.en.srt
018 Udemy_K_Means_Implementation.ipynb
006 How to measure performance.en.srt
012 What is K-Nearest Neighbors.en.srt
014 Summary of the section.en.srt
013 K-means Implementation.en.srt
007 Linear Regression Implementation.en.srt
008 Logistic Regression Implementation.en.srt
002 Introduction to learning from data.en.srt
011 What is Decision trees.en.srt
010 What is Neural networks.en.srt
013 Udemy_Logistic_Regression_Implementation.ipynb
004 What is Supervised learning_.en.srt
005 What is Unsupervised learning and Dimensionality reduction_.en.srt
003 Some popular machine learning dataset.en.srt
012 Udemy_Linear_Regression_Model_Implementation.ipynb
009 Support vector machines.en.srt
013 K-means Implementation.mp4
007 Linear Regression Implementation.mp4
006 How to measure performance.mp4
008 Logistic Regression Implementation.mp4
002 Introduction to learning from data.mp4
010 What is Neural networks.mp4
004 What is Supervised learning_.mp4
011 What is Decision trees.mp4
005 What is Unsupervised learning and Dimensionality reduction_.mp4
003 Some popular machine learning dataset.mp4
009 Support vector machines.mp4
012 What is K-Nearest Neighbors.mp4
001 What is machine learning.mp4
014 Summary of the section.mp4
04 Voting Method
032 Udemy_Hard_Voting_Implementation (2).ipynb
035 Udemy_Soft_voting_implementation_by_Using_scikit_learn.ipynb
007 Analysing our results.en.srt
006 Soft voting implementation by Using scikit-learn.en.srt
002 Custom hard voting implementation Part 1.en.srt
004 Analysing our results.en.srt
031 Udemy_Hard_Voting_Implementation (1).ipynb
005 Hard voting implementation by Using scikit-learn.en.srt
008 Summary.en.srt
001 What is hard and soft voting.en.srt
030 Udemy_Hard_Voting_Implementation.ipynb
033 Udemy_Hard_voting_implementation_by_Using_scikit_learn.ipynb
034 Udemy_Soft_voting_implementation_by_Using_scikit_learn.ipynb
007 Analysing our results.mp4
003 Custom hard voting implementation Part 2.en.srt
002 Custom hard voting implementation Part 1.mp4
006 Soft voting implementation by Using scikit-learn.mp4
004 Analysing our results.mp4
005 Hard voting implementation by Using scikit-learn.mp4
003 Custom hard voting implementation Part 2.mp4
001 What is hard and soft voting.mp4
008 Summary.mp4
06 Bagging Method
045 Udemy_Bootstrapping_Implementation.ipynb
006 Bagging Implementation Method 1.en.srt
008 Bagging Implementation Method 2 for regression.en.srt
003 Bootstrapping Implementation.en.srt
007 Bagging Implementation Method 2 for classification.en.srt
048 Udemy_Bagging_implementatio_Method_1.ipynb
001 Introduction to Bagging.en.srt
002 Bootstrapping Introduction.en.srt
004 Creating base learners for bagging.en.srt
005 Strengths and weaknesses of bagging.en.srt
050 Udemy_Bagging_implementation_Method_2_for_regression.ipynb
049 Udemy_Bagging_implementation_Method_2_for_classification.ipynb
009 Summary of the section.en.srt
006 Bagging Implementation Method 1.mp4
008 Bagging Implementation Method 2 for regression.mp4
003 Bootstrapping Implementation.mp4
007 Bagging Implementation Method 2 for classification.mp4
002 Bootstrapping Introduction.mp4
009 Summary of the section.mp4
005 Strengths and weaknesses of bagging.mp4
004 Creating base learners for bagging.mp4
001 Introduction to Bagging.mp4
07 Boosting Method
003 AdaBoost Implementation Method 1.en.srt
008 Gradient boosting Implementation Method 1.en.srt
059 Udemy_Gradient_Boosting_Introduction_and_implementation (1).ipynb
004 AdaBoost Implementation Method 2 for classification.en.srt
011 XGBoost Introduction and Implementation for Regression.en.srt
054 Udemy_AdaBoosting_Implementation.ipynb
009 Gradient boosting Implementation Method 2 For Regression Problem.en.srt
002 Introduction to AdaBoost.en.srt
007 Introduction to Gradient boosting.en.srt
055 Udemy_AdaBoost_Method_2_Implementation_for_classification.ipynb
001 Introduction to Boosting.en.srt
062 Udemy_XGBoost_Implementation_for_Regression.ipynb
010 Gradient boosting Implementation Method 2 For Classification Problem.en.srt
012 XGBoost Introduction and Implementation for Classification.en.srt
005 AdaBoost Implementation Method 2 for Regression Solution.en.srt
063 Udemy_XGBoost_Implementation_for_Classification.ipynb
006 Strengths and weaknesses of AdaBoost.en.srt
013 Summary.en.srt
058 Udemy_Gradient_Boosting_Introduction_and_implementation.ipynb
056 Udemy_AdaBoost_Method_2_Implementation_for_Regression.ipynb
061 Udemy_Gradient_Boosting_implementation_Method_2_for_Classification.ipynb
003 AdaBoost Implementation Method 1.mp4
008 Gradient boosting Implementation Method 1.mp4
007 Introduction to Gradient boosting.mp4
011 XGBoost Introduction and Implementation for Regression.mp4
004 AdaBoost Implementation Method 2 for classification.mp4
009 Gradient boosting Implementation Method 2 For Regression Problem.mp4
012 XGBoost Introduction and Implementation for Classification.mp4
005 AdaBoost Implementation Method 2 for Regression Solution.mp4
002 Introduction to AdaBoost.mp4
010 Gradient boosting Implementation Method 2 For Classification Problem.mp4
013 Summary.mp4
006 Strengths and weaknesses of AdaBoost.mp4
001 Introduction to Boosting.mp4
11 Movie Recommendation system -REAL WORLD PROBLEMS
005 Creating the dot model.en.srt
097 Creating_a_stacking_ensemble_for_Movie_Recommendation_system.ipynb
096 Creating_a_dense_model_for_Movie_Recommendation_system.ipynb
095 Creating_a_dot_model_for_Movie_Recommendation_system.ipynb
094 Udemy_Exploratory_data_for_Movie_Recommendation_system.ipynb
007 Creating a stacking ensemble.en.srt
006 Creating the dense model.en.srt
002 Demystifying recommendation systems.en.srt
004 Exploratory analysis.en.srt
003 Neural recommendation systems.en.srt
008 Summary.en.srt
001 Introduction to the project.en.srt
005 Creating the dot model.mp4
007 Creating a stacking ensemble.mp4
006 Creating the dense model.mp4
004 Exploratory analysis.mp4
002 Demystifying recommendation systems.mp4
003 Neural recommendation systems.mp4
008 Summary.mp4
001 Introduction to the project.mp4
08 Random Forests
002 Understanding random forest trees.en.srt
003 Creating and analysing forests and strengths and weaknesses of Random Forest.en.srt
004 Random forests Implementation for classification.en.srt
005 Random forests Implementation for regression.en.srt
069 Udemy_Random_forests_Implementation_for_Regression.ipynb
068 Udemy_Random_forests_Implementation_for_classification.ipynb
070 Udemy_Extra_Trees_Implementation_for_classification.ipynb
007 Extra trees Implementation for regression.en.srt
071 Udemy_Extra_Trees_Implementation_for_Regression.ipynb
008 Summary of the section.en.srt
006 Extra trees Implementation for classification.en.srt
001 Introduction to the Random Forest.en.srt
005 Random forests Implementation for regression.mp4
004 Random forests Implementation for classification.mp4
002 Understanding random forest trees.mp4
007 Extra trees Implementation for regression.mp4
003 Creating and analysing forests and strengths and weaknesses of Random Forest.mp4
006 Extra trees Implementation for classification.mp4
008 Summary of the section.mp4
001 Introduction to the Random Forest.mp4
12 Thank you
001 Thank you.en.srt
001 Thank you.mp4
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