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种子名称： [FreeCourseSite.com] Udemy - Machine Learning & Data Science A-Z Hands-on Python 2021
文件类型： 视频
文件数目： 72个文件
文件大小： 6.75 GB
收录时间： 2021-6-30 07:51
已经下载： 3次
资源热度： 320
最近下载： 2024-12-29 17:24

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[FreeCourseSite.com] Udemy - Machine Learning & Data Science A-Z Hands-on Python 2021.torrent

01 Introduction/001 Course Content.mp417.07MB

01 Introduction/002 What is Machine Learning_ Some Basic Terms.mp425.82MB

01 Introduction/004 Python IDE.mp47.51MB

01 Introduction/005 IDE Installation.mp422.28MB

01 Introduction/006 Installation of Required Libraries.mp470.78MB

01 Introduction/007 Spyder Interface.mp446.36MB

02 Machine Learning Useful Packages (Libraries)/002 NumPy1.mp437.49MB

02 Machine Learning Useful Packages (Libraries)/003 NumPy2.mp456.91MB

02 Machine Learning Useful Packages (Libraries)/004 NumPy3.mp484.52MB

02 Machine Learning Useful Packages (Libraries)/005 NumPy4.mp456.56MB

02 Machine Learning Useful Packages (Libraries)/006 NumPy5.mp4152.64MB

02 Machine Learning Useful Packages (Libraries)/007 NumPy6.mp4134.5MB

02 Machine Learning Useful Packages (Libraries)/008 Pandas1.mp495.61MB

02 Machine Learning Useful Packages (Libraries)/009 Pandas2.mp4116.94MB

02 Machine Learning Useful Packages (Libraries)/010 Pandas3.mp4117.83MB

02 Machine Learning Useful Packages (Libraries)/011 Pandas4.mp4203.04MB

02 Machine Learning Useful Packages (Libraries)/012 Visualization with Matplotlib1.mp499.45MB

02 Machine Learning Useful Packages (Libraries)/013 Visualization with Matplotlib2.mp4205.24MB

02 Machine Learning Useful Packages (Libraries)/014 Visualization with Matplotlib3.mp4188.84MB

02 Machine Learning Useful Packages (Libraries)/015 Visualization with Matplotlib4.mp4142.99MB

02 Machine Learning Useful Packages (Libraries)/016 Visualization with Matplotlib5.mp4129.25MB

03 Data Preprocessing/001 Reading and Modifying a Dataset.mp4154.55MB

03 Data Preprocessing/002 Statistics1.mp434.04MB

03 Data Preprocessing/003 Statistics2.mp4207.49MB

03 Data Preprocessing/004 Statistics3 - Covariance.mp4107.48MB

03 Data Preprocessing/005 Missing Values1.mp4129.64MB

03 Data Preprocessing/006 Missing Values2.mp4219.38MB

03 Data Preprocessing/007 Outlier Detection1.mp473.2MB

03 Data Preprocessing/008 Outlier Detection2.mp4130.66MB

03 Data Preprocessing/009 Outlier Detection3.mp431.01MB

03 Data Preprocessing/010 Concatenation.mp465.88MB

03 Data Preprocessing/011 Dummy Variable.mp457.59MB

03 Data Preprocessing/012 Normalization.mp4186.87MB

04 Machine Learning Introduction/001 Learning Types.mp445.44MB

05 Supervised Learning - Classification/001 Supervised Learning Models - Introduction and Understanding the Data.mp4233.75MB

05 Supervised Learning - Classification/002 k-NN Concepts.mp448.03MB

05 Supervised Learning - Classification/003 k-NN Model Development.mp4140.65MB

05 Supervised Learning - Classification/004 k-NN Training-Set and Test-Set Creation.mp4228.42MB

05 Supervised Learning - Classification/005 Decision Tree Concepts.mp425.62MB

05 Supervised Learning - Classification/006 Decision Tree Model Development.mp466.84MB

05 Supervised Learning - Classification/007 Decision Tree - Cross Validation.mp454.64MB

05 Supervised Learning - Classification/008 Naive Bayes Concepts.mp459.23MB

05 Supervised Learning - Classification/009 Naive Bayes Model Development.mp458.95MB

05 Supervised Learning - Classification/010 Logistic Regression Concepts.mp410.85MB

05 Supervised Learning - Classification/011 Logistic Regression Model Development.mp4112.12MB

05 Supervised Learning - Classification/012 Model Evaluation Concepts.mp483.47MB

05 Supervised Learning - Classification/013 Model Evaluation - Calculating with Python.mp4174.04MB

06 Supervised Learning - Regression/001 Simple and Multiple Linear Regression Concepts.mp4212.19MB

06 Supervised Learning - Regression/002 Multiple Linear Regression - Model Development.mp475.59MB

06 Supervised Learning - Regression/003 Evaluation Metrics - Concepts.mp449.47MB

06 Supervised Learning - Regression/004 Evaluation Metrics - Implementation.mp4159.9MB

06 Supervised Learning - Regression/005 Polynomial Linear Regression Concepts.mp426.39MB

06 Supervised Learning - Regression/006 Polynomial Linear Regression Model Development.mp4219.12MB

06 Supervised Learning - Regression/007 Random Forest Concepts.mp430.23MB

06 Supervised Learning - Regression/008 Random Forest Model Development.mp4246.24MB

06 Supervised Learning - Regression/009 Support Vector Regression Concepts.mp426.97MB

06 Supervised Learning - Regression/010 Support Vector Regression Model Development.mp4121.01MB

07 Unsupervised Learning - Clustering Techniques/001 Introduction.mp438.11MB

07 Unsupervised Learning - Clustering Techniques/002 K-means Concepts1.mp444.53MB

07 Unsupervised Learning - Clustering Techniques/003 K-means Concepts2.mp421.28MB

07 Unsupervised Learning - Clustering Techniques/004 K-means Model Development1.mp435.98MB

07 Unsupervised Learning - Clustering Techniques/005 K-means Model Development2.mp4103.83MB

07 Unsupervised Learning - Clustering Techniques/006 K-means - Model Evaluation.mp4102.4MB

07 Unsupervised Learning - Clustering Techniques/007 DBSCAN Concepts.mp426.85MB

07 Unsupervised Learning - Clustering Techniques/008 DBSCAN Model Development.mp486.87MB

07 Unsupervised Learning - Clustering Techniques/009 Hierarchical Clustering Concepts.mp424.29MB

07 Unsupervised Learning - Clustering Techniques/010 Hierarchical Clustering Model Development.mp4145.89MB

08 Hyper Parameter Optimization (Model Tuning)/001 Introduction.mp417.02MB

08 Hyper Parameter Optimization (Model Tuning)/002 Support Vector Regression - Model Tuning.mp4125.61MB

08 Hyper Parameter Optimization (Model Tuning)/003 K-Means - Model Tuning.mp415.3MB

08 Hyper Parameter Optimization (Model Tuning)/004 k-NN - Model Tuning.mp4133.64MB

08 Hyper Parameter Optimization (Model Tuning)/005 Overfitting and Underfitting.mp472.09MB