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Data Science Projects with Python - Second Edition
The Book provide a comprehensive guide to data science with Python, focusing on machine learning techniques for building predictive models. The book covers data exploration, cleaning, and visualization, along with core functionalities of scikit-learn for training and evaluating models. It delves into the details of logistic regression, including its assumptions and limitations, and explores techniques for handling issues such as overfitting and multicollinearity. The text then introduces decision trees and random forests, highlighting their advantages and disadvantages, and provides guidance on hyperparameter tuning using techniques like GridSearchCV. The final chapter focuses on gradient boosting and XGBoost, showcasing its power and demonstrating how to interpret model predictions using SHAP values. The sources also include activities and exercises for hands-on learning and real-world applications using the case study data.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Projects-Python-approach/dp/1800564481?&linkCode=ll1&tag=cvthunderx-20&linkId=8e3421d8258f684e39134194d83c0eb8&language=en_US&ref_=as_li_ss_tl
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Projects-Python-approach/dp/1800564481?&linkCode=ll1&tag=cvthunderx-20&linkId=8e3421d8258f684e39134194d83c0eb8&language=en_US&ref_=as_li_ss_tl
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By CyberSecurity SummaryThe Book provide a comprehensive guide to data science with Python, focusing on machine learning techniques for building predictive models. The book covers data exploration, cleaning, and visualization, along with core functionalities of scikit-learn for training and evaluating models. It delves into the details of logistic regression, including its assumptions and limitations, and explores techniques for handling issues such as overfitting and multicollinearity. The text then introduces decision trees and random forests, highlighting their advantages and disadvantages, and provides guidance on hyperparameter tuning using techniques like GridSearchCV. The final chapter focuses on gradient boosting and XGBoost, showcasing its power and demonstrating how to interpret model predictions using SHAP values. The sources also include activities and exercises for hands-on learning and real-world applications using the case study data.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Projects-Python-approach/dp/1800564481?&linkCode=ll1&tag=cvthunderx-20&linkId=8e3421d8258f684e39134194d83c0eb8&language=en_US&ref_=as_li_ss_tl
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Data-Science-Projects-Python-approach/dp/1800564481?&linkCode=ll1&tag=cvthunderx-20&linkId=8e3421d8258f684e39134194d83c0eb8&language=en_US&ref_=as_li_ss_tl