scikit-learn Recipes

Data Pre-Processing with scikit-learn 6 lectures 20:21 The Course Overview This video will give you an overview about the course. Loading Data The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn Building Binary Features by Creating Thresholds The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms Imputing Missing Values Using sklearn Impute The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values Building Linear Model with Outliers The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables Putting It All Together with sklearn Pipelines The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model Data Pre-Processing with scikit-learn 6 lectures 20:21 The Course Overview This video will give you an overview about the course. Loading Data The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn Building Binary Features by Creating Thresholds The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms Imputing Missing Values Using sklearn Impute The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values Building Linear Model with Outliers The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables Putting It All Together with sklearn Pipelines The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. This video will give you an overview about the course. This video will give you an overview about the course. Loading Data The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn Loading Data The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn Loading Data The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn Loading Data The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn The aim of this video is to load data. • Download data from the GitHub repo • Load data in Jupyter notebook • Import Panda and sk-learn Building Binary Features by Creating Thresholds The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms Building Binary Features by Creating Thresholds The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms Building Binary Features by Creating Thresholds The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms Building Binary Features by Creating Thresholds The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms The aim of this video is to build binary features of the continuous variable by creating thresholds values. • Understand binarizer • Group-by Opportunity result • Implement thresholding algorithms Imputing Missing Values Using sklearn Impute The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values Imputing Missing Values Using sklearn Impute The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values Imputing Missing Values Using sklearn Impute The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values Imputing Missing Values Using sklearn Impute The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values The aim of this video is to impute missing value in the data-frame using sklearn Impute. • Use pima indians diabetes dataset • Find the value of 0 • Impute the missing values Building Linear Model with Outliers The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables Building Linear Model with Outliers The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables Building Linear Model with Outliers The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables Building Linear Model with Outliers The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables The aim of this video is to build a linear model on a data that has outliers. • Build linear models for data outliers • Encode the categorical variables Putting It All Together with sklearn Pipelines The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model Putting It All Together with sklearn Pipelines The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model Putting It All Together with sklearn Pipelines The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model Putting It All Together with sklearn Pipelines The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model The aim of this video is to put together everything learnt in the section, using scikit-learn pipelines. • Use multi-column label encoder • Finalize your data • Build pipeline for logic regression model Dimensionality Reduction 4 lectures 14:02 Principal Components Analysis The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy t-SNE The aim of this video is to look at another dimensionality technique called t-SNE. • Get a brief introduction to t-SNE • Apply t-SNE to your model • Explore visualization of PCA Factor Analysis The aim of this video is to look at another dimensionality technique called factor analysis. • Learn about the use of factor analysis to reduce dimensionality • Plot your figure using scatterplot • Compare PCA and FA Kernel PCA The aim of this video is to look at another dimensionality technique called Kernel PCA. • Understand the utility of Kernel PCA • Use KCPA to reduce dimensions • Plot the figure size using scatterplot Dimensionality Reduction. 4 lectures 14:02 Principal Components Analysis The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy t-SNE The aim of this video is to look at another dimensionality technique called t-SNE. • Get a brief introduction to t-SNE • Apply t-SNE to your model • Explore visualization of PCA Factor Analysis The aim of this video is to look at another dimensionality technique called factor analysis. • Learn about the use of factor analysis to reduce dimensionality • Plot your figure using scatterplot • Compare PCA and FA Kernel PCA The aim of this video is to look at another dimensionality technique called Kernel PCA. • Understand the utility of Kernel PCA • Use KCPA to reduce dimensions • Plot the figure size using scatterplot Principal Components Analysis The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy Principal Components Analysis The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy Principal Components Analysis The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy Principal Components Analysis The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy The video aims to cover dimensionality reduction. • Explore dimensionality reduction using PCA • See PCA and Pipeline log-reg model • Explore the prediction of model accuracy t-SNE The aim of this video is to look at another dimensionality technique called t-SNE. • Get a brief introduction to t-SNE • Apply t-SNE to your model • Explore visualization of PCA t-SNE The aim of this video is to look at another dimensionality technique called t-SNE. • Get a brief introduction to t-SNE • Apply t-SNE to your model • Explore visualization of PCA t-SNE The aim of this video is to look at another dimensionality technique called t-SNE ression with outliers Logistic Regression ...