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Best YouTube Channels To Learn:
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Blockchain - Telusko
AI/ML- Krish Naik
DBMS - edureka
Cybersecurity - John Hammond
Networking - David Bombal
Python - Code With Harry
React - Codevolution
UI/UX - GFXMentor
JavaScript - Traversy Media
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O Tribunal do Crime
Rotineiramente, viralizam nas redes sociais vรญdeos de jovens sendo torturados ou executados sumariamente por membros de facรงรตes criminosas.
Em muitos casos, as motivaรงรตes sรฃo banais: visitar parentes, participar de festas em comunidades rivais ou, atรฉ mesmo, fazer gestos supostamente alusivos a grupos rivais em fotografias.
Diante disso, o que mais choca รฉ a apatia das autoridades, principalmente dos polรญticos, que nรฃo se mobilizam para criar mecanismos legais capazes de frear esses atos de terror.
ร de extrema urgรชncia que haja uma intervenรงรฃo firme do Estado para cessar essa prรกtica.
Afinal, se os criminosos se sentem no direito de aplicar a pena de morte, por que nรฃo tornar essa puniรงรฃo recรญproca a quem a pratica?
https://t.co/6ODzxTbhr8
XGBoost for Regression, Predictive Modeling, and Time Series Analysis โ Learn how to build, evaluate, & deploy predictive models:
https://t.co/DsGwnEXyWF v/ @PacktDataML
โ
My review:
๐ XGBoost is definitely the focal point and central contribution of this book, along with all those other things: Regression Trees, Predictive Modeling, Time Series Analysis, and Forecasting.
๐ This bookโs added value comes in its thorough progression from start to finish with comprehensive coverage of all the major steps and issues in data science and machine learning modeling: data cleaning, imbalanced data, feature engineering, feature engineering pipeline, feature importance, categorical data encoding, model interpretability and explainability, ensembles, model training, model evaluation metrics, hyperparameter tuning, model deployment, and more - including a quick-start case study starter project that feeds into next-level projects.
๐ I highly recommend this broadly useful book for all levels of practitioners.
Data Cleaning and Exploration with Machine Learning: https://t.co/nqRGSXscoO v/ @PacktDataML@PacktPublishing
+
Visit the book's GitHub code repository: https://t.co/3nZVhNGNnj