Udemy Complete Machine Learning Course With Python

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Udemy Complete Machine Learning Course With Python

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文件大小： 4.73G
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收录时间：2024-06-22
最近下载：2025-01-02

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文件列表

  • 1 - Introduction/6 - Basic Python Concepts.mp4 509.0 MB
  • 18 - KMeans Clusteringunsupervised model/48 - KMeans Clustering Program1.mp4 218.8 MB
  • 17 - Random Forest/46 - Random Forest Defination and its practice program1.mp4 204.5 MB
  • 12 - Logistic Regression/39 - What is Logistic Regression and program1.mp4 200.0 MB
  • 22 - Model Selection/57 - Model Selection Program1.mp4 199.9 MB
  • 20 - Principle Component AnalysisPCA/51 - Principle Component Analysis Program1.mp4 179.1 MB
  • 13 - Support Vector MachineSVM/40 - What is Support Vector Machine.mp4 171.4 MB
  • 15 - KNN Classifier/44 - KNN Classifer defination and its practice program1.mp4 160.9 MB
  • 9 - Ridge Regression/35 - Ridge RegressionProgram 1.mp4 151.6 MB
  • 14 - Naive Bayes Classification/43 - Naive Bayes Classification Program2.mp4 149.6 MB
  • 16 - Decision Trees/45 - Decision Trees Defination and its program1.mp4 143.8 MB
  • 3 - Exploratory Data Analysis/14 - Drawing Graphs.mp4 141.3 MB
  • 7 - One Hot Encoding/29 - One Hot EncodingProgram 1.mp4 135.1 MB
  • 10 - Lasso Regression/37 - What is Lasso regression and practice program1.mp4 133.5 MB
  • 11 - ElasticNet Regression/38 - what is ElasticNet Regression and practice program1.mp4 130.8 MB
  • 11 - ElasticNet Regression/38 - 10.ElasticNet-Regression.mp4 130.2 MB
  • 8 - Polynomial Linear Regression/32 - Polynomial Linear Regression Program1.mp4 130.0 MB
  • 4 - Outliers/18 - IQR and handling the outliers.mp4 126.3 MB
  • 6 - Multiple Linear Regression/25 - Multiple Linear Regression program 1.mp4 124.3 MB
  • 2 - Introduction to Machine Learning and Anaconda Installation/7 - Introduction to Machine Learning.mp4 119.8 MB
  • 5 - Simple Linear Regression/23 - Simple linear regression Program2train and test data.mp4 108.9 MB
  • 14 - Naive Bayes Classification/42 - Naive Bayes Classification Program1.mp4 102.2 MB
  • 5 - Simple Linear Regression/22 - Simple linear regression Program1.mp4 100.2 MB
  • 7 - One Hot Encoding/30 - One Hot EncodingProgram 2Third way.mp4 91.1 MB
  • 3 - Exploratory Data Analysis/13 - Statistical Information.mp4 87.1 MB
  • 6 - Multiple Linear Regression/24 - What is Multiple Linear Regression.mp4 86.2 MB
  • 19 - Apriori Algorithm/49 - What is Apriori Algorithm.mp4 77.5 MB
  • 2 - Introduction to Machine Learning and Anaconda Installation/8 - Anconda Installation.mp4 72.2 MB
  • 22 - Model Selection/56 - What is Model Selection.mp4 67.5 MB
  • 3 - Exploratory Data Analysis/11 - Modifying or removing unwanted data.mp4 56.1 MB
  • 3 - Exploratory Data Analysis/12 - Retrieving Data.mp4 53.5 MB
  • 4 - Outliers/17 - Finding the Outliers.mp4 53.0 MB
  • 21 - KFold Cross Validation/55 - KFold Cross Validation Program1.mp4 52.2 MB
  • 18 - KMeans Clusteringunsupervised model/47 - What is KMeans Clustering.mp4 48.3 MB
  • 20 - Principle Component AnalysisPCA/52 - Principle Component Analysis Program2.mp4 48.3 MB
  • 20 - Principle Component AnalysisPCA/50 - what is Principle Component AnalysisPCA.mp4 47.0 MB
  • 21 - KFold Cross Validation/54 - What is KFold Cross Validation.mp4 41.8 MB
  • 9 - Ridge Regression/34 - What is Regularization.mp4 41.0 MB
  • 14 - Naive Bayes Classification/41 - What is Naive Bayes Classification.mp4 38.9 MB
  • 5 - Simple Linear Regression/20 - What is simple liner regression model.mp4 35.5 MB
  • 3 - Exploratory Data Analysis/10 - knowing initial details of dataset.mp4 33.7 MB
  • 1 - Introduction/3 - Supervised learning vs Unsupervised Learning.mp4 32.0 MB
  • 8 - Polynomial Linear Regression/31 - What is Polynomial Linear Regression.mp4 27.6 MB
  • 3 - Exploratory Data Analysis/9 - What is Exploratory Data AnalysisEDA.mp4 26.0 MB
  • 4 - Outliers/16 - What is Outliers.mp4 24.1 MB
  • 9 - Ridge Regression/33 - What is Bias and Variance.mp4 22.4 MB
  • 7 - One Hot Encoding/27 - One Hot EncodingFirst way.mp4 19.0 MB
  • 5 - Simple Linear Regression/21 - What is rsquared Value.mp4 18.3 MB
  • 1 - Introduction/4 - Dependent Variable vs Independent Variable.mp4 16.1 MB
  • 7 - One Hot Encoding/26 - What Is One Hot Encoding.mp4 15.7 MB
  • 1 - Introduction/1 - What Is Machine learning.mp4 14.8 MB
  • 1 - Introduction/5 - What Does This Course Cover.mp4 10.5 MB
  • 7 - One Hot Encoding/28 - One Hot EncodingSecond way.mp4 10.2 MB
  • 9 - Ridge Regression/36 - Ridge RegressionAssignment.mp4 10.0 MB
  • 1 - Introduction/2 - Key Skills needed to learn Machine learning.mp4 9.0 MB
  • 5 - Simple Linear Regression/19 - What is Regression.mp4 7.8 MB
  • 3 - Exploratory Data Analysis/15 - EDA Assignment.mp4 5.2 MB
  • 20 - Principle Component AnalysisPCA/53 - Principle Component AnalysisAssignment.mp4 4.7 MB
  • 23 - Assignment Solutions/58 - Assignment Solutions.mp4 1.6 MB
  • 12 - Logistic Regression/39 - HR-comma.csv 566.8 kB
  • 14 - Naive Bayes Classification/43 - spam.csv 502.6 kB
  • 19 - Apriori Algorithm/49 - market.csv 431.1 kB
  • 1 - Introduction/6 - Complete-basic-Python-in-90-mins.docx 424.4 kB
  • 18 - KMeans Clusteringunsupervised model/47 - 17.K-Means-Clustering.docx 400.0 kB
  • 18 - KMeans Clusteringunsupervised model/48 - 17.K-Means-Clustering.docx 400.0 kB
  • 13 - Support Vector MachineSVM/40 - 12.Support-vector-machine-SVM.docx 327.3 kB
  • 17 - Random Forest/46 - 16.Random-Forest.docx 268.1 kB
  • 1 - Introduction/1 - what-is-machine-learning.docx 251.6 kB
  • 1 - Introduction/6 - Basic Python Concepts.srt 146.0 kB
  • 9 - Ridge Regression/33 - 8.Ridge-Regression.docx 125.5 kB
  • 2 - Introduction to Machine Learning and Anaconda Installation/7 - 1.Introduction-to-Machine-Learning.docx 111.8 kB
  • 16 - Decision Trees/45 - 15.Decision-Trees.docx 71.9 kB
  • 20 - Principle Component AnalysisPCA/50 - 19.Principal-Component-Analysis-PCA.docx 68.0 kB
  • 3 - Exploratory Data Analysis/15 - titanic.csv 61.2 kB
  • 5 - Simple Linear Regression/19 - 4.Simple-Linear-Regression.docx 56.5 kB
  • 23 - Assignment Solutions/58 - Principle-component-analysis-Assignment-solution.docx 49.5 kB
  • 15 - KNN Classifier/44 - 14.KNN-Classifier.docx 45.2 kB
  • 3 - Exploratory Data Analysis/9 - 2.Exploratory-Data-Analysis.docx 43.5 kB
  • 17 - Random Forest/46 - Random Forest Defination and its practice program1.srt 40.8 kB
  • 1 - Introduction/1 - cars.csv 39.0 kB
  • 12 - Logistic Regression/39 - What is Logistic Regression and program1.srt 38.1 kB
  • 20 - Principle Component AnalysisPCA/51 - Principle Component Analysis Program1.srt 36.1 kB
  • 13 - Support Vector MachineSVM/40 - What is Support Vector Machine.srt 36.1 kB
  • 18 - KMeans Clusteringunsupervised model/48 - KMeans Clustering Program1.srt 35.4 kB
  • 9 - Ridge Regression/36 - boston-houses.csv 35.0 kB
  • 10 - Lasso Regression/37 - boston-houses.csv 35.0 kB
  • 11 - ElasticNet Regression/38 - boston-houses.csv 35.0 kB
  • 23 - Assignment Solutions/58 - K-Means-Clustering-Assignment-Solution.docx 34.6 kB
  • 2 - Introduction to Machine Learning and Anaconda Installation/7 - Introduction to Machine Learning.srt 33.5 kB
  • 23 - Assignment Solutions/58 - Logistic-regression-Assignment-solution.docx 31.6 kB
  • 8 - Polynomial Linear Regression/31 - 7.Polynomial-Linear-Regression.docx 31.0 kB
  • 6 - Multiple Linear Regression/25 - Housing.csv 30.0 kB
  • 22 - Model Selection/57 - Model Selection Program1.srt 28.3 kB
  • 9 - Ridge Regression/35 - Ridge RegressionProgram 1.srt 28.2 kB
  • 7 - One Hot Encoding/29 - One Hot EncodingProgram 1.srt 28.0 kB
  • 23 - Assignment Solutions/58 - Random-Forest-Assignment-solution.docx 27.8 kB
  • 15 - KNN Classifier/44 - KNN Classifer defination and its practice program1.srt 27.2 kB
  • 14 - Naive Bayes Classification/41 - 13.Naive-Bayes-Classification.docx 26.6 kB
  • 8 - Polynomial Linear Regression/32 - Polynomial Linear Regression Program1.srt 25.5 kB
  • 16 - Decision Trees/45 - Decision Trees Defination and its program1.srt 25.2 kB
  • 14 - Naive Bayes Classification/43 - Naive Bayes Classification Program2.srt 25.1 kB
  • 6 - Multiple Linear Regression/25 - Multiple Linear Regression program 1.srt 24.8 kB
  • 5 - Simple Linear Regression/23 - Simple linear regression Program2train and test data.srt 24.8 kB
  • 11 - ElasticNet Regression/38 - 10.ElasticNet-Regression.srt 24.6 kB
  • 10 - Lasso Regression/37 - What is Lasso regression and practice program1.srt 24.4 kB
  • 11 - ElasticNet Regression/38 - diabetes-Assignment.csv 23.9 kB
  • 15 - KNN Classifier/44 - diabetes-Assignment.csv 23.9 kB
  • 11 - ElasticNet Regression/38 - what is ElasticNet Regression and practice program1.srt 23.8 kB
  • 22 - Model Selection/56 - 21.Model-Selection.docx 23.4 kB
  • 4 - Outliers/18 - IQR and handling the outliers.srt 23.0 kB
  • 5 - Simple Linear Regression/22 - Simple linear regression Program1.srt 21.7 kB
  • 6 - Multiple Linear Regression/24 - 5.Mutiple-Linear-Regression.docx 21.2 kB
  • 4 - Outliers/16 - 3.Outliers.docx 20.9 kB
  • 7 - One Hot Encoding/26 - 6.One-Hot-Encoding.docx 20.8 kB
  • 3 - Exploratory Data Analysis/14 - Drawing Graphs.srt 20.5 kB
  • 15 - KNN Classifier/44 - breast-cancer.csv 20.3 kB
  • 12 - Logistic Regression/39 - 11.Logistic-Regression.docx 19.1 kB
  • 14 - Naive Bayes Classification/42 - Naive Bayes Classification Program1.srt 18.8 kB
  • 6 - Multiple Linear Regression/24 - What is Multiple Linear Regression.srt 18.2 kB
  • 10 - Lasso Regression/37 - 9.Lasso-Regression.docx 17.9 kB
  • 7 - One Hot Encoding/30 - One Hot EncodingProgram 2Third way.srt 16.9 kB
  • 19 - Apriori Algorithm/49 - 18.Apriori-Algorithm.docx 16.4 kB
  • 22 - Model Selection/56 - What is Model Selection.srt 15.9 kB
  • 3 - Exploratory Data Analysis/13 - Statistical Information.srt 15.4 kB
  • 21 - KFold Cross Validation/54 - 20.K-Fold-Cross-Validation.docx 15.2 kB
  • 19 - Apriori Algorithm/49 - What is Apriori Algorithm.srt 14.7 kB
  • 23 - Assignment Solutions/58 - Outlier-Assignment-solution.docx 14.3 kB
  • 23 - Assignment Solutions/58 - OneHotEncoding-Assignment-solution.docx 14.2 kB
  • 23 - Assignment Solutions/58 - KNN-Classifier-Assignment-Solution.docx 14.2 kB
  • 23 - Assignment Solutions/58 - Polynomial-Linear-Regression-Assignment-solution.docx 14.0 kB
  • 23 - Assignment Solutions/58 - EDA-assignment-solution.docx 13.8 kB
  • 23 - Assignment Solutions/58 - Simple-Linear-Regression-Assignment-Solution.docx 13.8 kB
  • 23 - Assignment Solutions/58 - Ridge-regression-assignment-solution.docx 13.6 kB
  • 23 - Assignment Solutions/58 - support-Vector-Machine-SVM-Assignment-solution.docx 13.4 kB
  • 23 - Assignment Solutions/58 - Lasso-regression-Assignment-Solution.docx 13.4 kB
  • 23 - Assignment Solutions/58 - ElasticNet-Regression-Assignment-solution.docx 12.9 kB
  • 23 - Assignment Solutions/58 - Naive-Bayes-classification-Assignment-solution.docx 12.7 kB
  • 2 - Introduction to Machine Learning and Anaconda Installation/8 - Anaconda-installation-machine-learning.docx 12.4 kB
  • 23 - Assignment Solutions/58 - Decision-Trees-Assignment-solution.docx 12.3 kB
  • 23 - Assignment Solutions/58 - Multiple-linear-regression-Assignment-solution.docx 12.3 kB
  • 18 - KMeans Clusteringunsupervised model/47 - What is KMeans Clustering.srt 12.0 kB
  • 23 - Assignment Solutions/58 - K-Fold-Cross-Validation-Assignment-solution.docx 11.9 kB
  • 21 - KFold Cross Validation/55 - KFold Cross Validation Program1.srt 11.9 kB
  • 4 - Outliers/18 - Outliers-Assignment.docx 11.5 kB
  • 22 - Model Selection/57 - processed.csv 11.3 kB
  • 3 - Exploratory Data Analysis/11 - Modifying or removing unwanted data.srt 11.3 kB
  • 4 - Outliers/17 - Finding the Outliers.srt 11.2 kB
  • 5 - Simple Linear Regression/20 - What is simple liner regression model.srt 9.7 kB
  • 14 - Naive Bayes Classification/41 - What is Naive Bayes Classification.srt 9.1 kB
  • 3 - Exploratory Data Analysis/12 - Retrieving Data.srt 9.0 kB
  • 20 - Principle Component AnalysisPCA/52 - Principle Component Analysis Program2.srt 8.4 kB
  • 1 - Introduction/3 - Supervised learning vs Unsupervised Learning.srt 8.2 kB
  • 3 - Exploratory Data Analysis/10 - knowing initial details of dataset.srt 8.2 kB
  • 21 - KFold Cross Validation/54 - What is KFold Cross Validation.srt 8.1 kB
  • 20 - Principle Component AnalysisPCA/50 - what is Principle Component AnalysisPCA.srt 7.8 kB
  • 8 - Polynomial Linear Regression/31 - What is Polynomial Linear Regression.srt 6.2 kB
  • 9 - Ridge Regression/34 - What is Regularization.srt 5.9 kB
  • 9 - Ridge Regression/33 - What is Bias and Variance.srt 5.8 kB
  • 3 - Exploratory Data Analysis/9 - What is Exploratory Data AnalysisEDA.srt 5.5 kB
  • 4 - Outliers/16 - What is Outliers.srt 4.9 kB
  • 10 - Lasso Regression/37 - Advertising-Assignment.csv 4.8 kB
  • 5 - Simple Linear Regression/21 - What is rsquared Value.srt 4.5 kB
  • 1 - Introduction/1 - What Is Machine learning.srt 4.4 kB
  • 7 - One Hot Encoding/26 - What Is One Hot Encoding.srt 4.3 kB
  • 7 - One Hot Encoding/27 - One Hot EncodingFirst way.srt 4.2 kB
  • 1 - Introduction/4 - Dependent Variable vs Independent Variable.srt 3.8 kB
  • 1 - Introduction/2 - Key Skills needed to learn Machine learning.srt 2.7 kB
  • 9 - Ridge Regression/36 - Ridge RegressionAssignment.srt 2.7 kB
  • 7 - One Hot Encoding/28 - One Hot EncodingSecond way.srt 2.4 kB
  • 1 - Introduction/5 - What Does This Course Cover.srt 2.2 kB
  • 5 - Simple Linear Regression/19 - What is Regression.srt 2.2 kB
  • 20 - Principle Component AnalysisPCA/53 - Principle Component AnalysisAssignment.srt 1.2 kB
  • 3 - Exploratory Data Analysis/15 - EDA Assignment.srt 917 Bytes
  • 5 - Simple Linear Regression/23 - canada-percapita-Assignment.csv 860 Bytes
  • 16 - Decision Trees/45 - salaries-Assignment.csv 638 Bytes
  • 4 - Outliers/16 - solid-waste.csv 618 Bytes
  • 4 - Outliers/18 - solid-waste.csv 618 Bytes
  • 8 - Polynomial Linear Regression/32 - salary-experience-Assignment.csv 445 Bytes
  • 17 - Random Forest/46 - salary-experience-Assignment.csv 445 Bytes
  • 14 - Naive Bayes Classification/42 - cricket.csv 438 Bytes
  • 16 - Decision Trees/45 - cricket.csv 438 Bytes
  • 18 - KMeans Clusteringunsupervised model/48 - income.csv 381 Bytes
  • 7 - One Hot Encoding/30 - carprices-Assignment.csv 378 Bytes
  • 7 - One Hot Encoding/29 - homeprices-3.csv 366 Bytes
  • 7 - One Hot Encoding/30 - homeprices-3.csv 366 Bytes
  • 8 - Polynomial Linear Regression/32 - salary-position.csv 308 Bytes
  • 23 - Assignment Solutions/58 - Assignment Solutions.srt 293 Bytes
  • 5 - Simple Linear Regression/23 - salary-data.csv 172 Bytes
  • 12 - Logistic Regression/39 - insurance-data-Assignment.csv 155 Bytes
  • 2 - Introduction to Machine Learning and Anaconda Installation/8 - Anconda Installation.srt 139 Bytes
  • 6 - Multiple Linear Regression/25 - homeprices-2.csv 130 Bytes
  • 5 - Simple Linear Regression/22 - homeprices.csv 71 Bytes
  • 9 - Ridge Regression/35 - train.csv 59 Bytes
  • 9 - Ridge Regression/35 - test.csv 55 Bytes

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