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day15 - working with csv files
day15 - working with csv files
 
 
day16 - working-with-json-and-sql
day16 - working-with-json-and-sql
 
 
day17-api-to-dataframe
day17-api-to-dataframe
 
 
day18-pandas-dataframe-using-web-scraping
day18-pandas-dataframe-using-web-scraping
 
 
day19-understanding-your-data-descriptive-stats
day19-understanding-your-data-descriptive-stats
 
 
day20-univariate-analysis
day20-univariate-analysis
 
 
day21-bivariate-analysis
day21-bivariate-analysis
 
 
day22-pandas-profiling
day22-pandas-profiling
 
 
day24-standardization
day24-standardization
 
 
day25-normalization
day25-normalization
 
 
day26-ordinal-encoding
day26-ordinal-encoding
 
 
day27-one-hot-encoding
day27-one-hot-encoding
 
 
day28-column-transformer
day28-column-transformer
 
 
day29-sklearn-pipelines
day29-sklearn-pipelines
 
 
day30-function-transformer
day30-function-transformer
 
 
day31-power-transformer
day31-power-transformer
 
 
day32-binning-and-binarization
day32-binning-and-binarization
 
 
day33-handling-mixed-variables
day33-handling-mixed-variables
 
 
day34-handling-date-and-time
day34-handling-date-and-time
 
 
day35-complete-case-analysis
day35-complete-case-analysis
 
 
day36-imputing-numerical-data
day36-imputing-numerical-data
 
 
day37-handling-missing-categorical-data
day37-handling-missing-categorical-data
 
 
day38-missing-indicator
day38-missing-indicator
 
 
day39-knn-imputer
day39-knn-imputer
 
 
day40-iterative-imputer
day40-iterative-imputer
 
 
day42-outlier-removal-using-zscore
day42-outlier-removal-using-zscore
 
 
day43-outlier-removal-using-iqr-method
day43-outlier-removal-using-iqr-method
 
 
day44-outlier-detection-using-percentiles
day44-outlier-detection-using-percentiles
 
 
day45-feature-construction-and-feature-splitting
day45-feature-construction-and-feature-splitting
 
 
day47-pca
day47-pca
 
 
day48-simple-linear-regression
day48-simple-linear-regression
 
 
day49-regression-metrics
day49-regression-metrics
 
 
day50-multiple-linear-regression
day50-multiple-linear-regression
 
 
day51-gradient-descent
day51-gradient-descent
 
 
day52-types-of-gradient-descent
day52-types-of-gradient-descent
 
 
day53-polynomial-regression
day53-polynomial-regression
 
 
day55-regularized-linear-models
day55-regularized-linear-models
 
 
day56-lasso-regression
day56-lasso-regression
 
 
day57-elasticnet-regression
day57-elasticnet-regression
 
 
day58-logistic-regression
day58-logistic-regression
 
 
day59-classification-metrics
day59-classification-metrics
 
 
day60-logistic-regression-contd
day60-logistic-regression-contd
 
 
day65-random-forest
day65-random-forest
 
 
day66-adaboost
day66-adaboost
 
 
day68-stacking-and-blending
day68-stacking-and-blending
 
 
gradient-boosting
gradient-boosting
 
 
kmeans
kmeans
 
 
README.md
README.md
 
 
adaboost_demo.ipynb
adaboost_demo.ipynb
 
 
day68-stacking
day68-stacking
 
 

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This is very famous repository and made by campusx. it gives hands on experinece to learn ml.

It gives proper roadmap and fullfledged code to start journey to explore ml.

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