HLONIPANI L. MADONDO @hlonipanie - Twitter Profile

@centrekop I wish Virgil to retire at Liverpool. Never seen such a defender who hascsi much influence in a team. Wish he could be our own Maldini and go on playing till 40. Classy and Boss Captain #

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HLONIPANI L. MADONDO @hlonipanie

15 days ago

@AhmadPrimex @grok , please name the movie

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HLONIPANI L. MADONDO @hlonipanie

15 days ago

@LiverpoolFCW Always, appreciate improved and exciting levels of performance by the ladies this season. Keep it up!

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HLONIPANI L. MADONDO @hlonipanie

18 days ago

@DougColtart Well said Doug, very balanced article

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hlonipanie retweeted

Kirk Borne

@KirkDBorne

21 days ago

This book is gold for Data Analysts and Data Scientists! Mastering Data Analysis With Python — Wrangle, Clean, Analyze, and Visualize Data from Scratch: https://t.co/IntydhyAEY

KirkDBorne's tweet photo. This book is gold for Data Analysts and Data Scientists!

Mastering Data Analysis With Python — Wrangle, Clean, Analyze, and Visualize Data from Scratch: https://t.co/IntydhyAEY https://t.co/qdD1mVqWSt

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HLONIPANI L. MADONDO @hlonipanie

22 days ago

@JamesWillliam_ Give us a break with your nonsense. The club is trying to address such issues long-term. Respect the present players filling those roles. Stop crying for a RW we may not even need.

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hlonipanie retweeted

Kirk Borne

@KirkDBorne

24 days ago

"Machine Learning Foundations, Volume 1: Supervised Learning" - available at https://t.co/8nD3jQvCuh 𝓑𝓮𝓷𝓮𝓯𝓲𝓽𝓼: 💠Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques 💠Implement the full supervised learning pipeline, from data preprocessing and feature engineering to model selection, training, and evaluation 💠Understand key learning tasks, including classification, regression, multi-label, and multi-output problems 💠Implement foundational algorithms from scratch, including linear and logistic regression, decision trees, gradient boosting, and SVMs 💠Gain hands-on experience with industry-standard tools such as Scikit-Learn, XGBoost, and NLTK 💠Refine and optimize your models using techniques such as hyperparameter tuning, cross-validation, and calibration 💠Work with diverse data types, including tabular data, text, and images 💠Address real-world challenges such as imbalanced datasets, missing data, and high-dimensional inputs

KirkDBorne's tweet photo. "Machine Learning Foundations, Volume 1: Supervised Learning" - available at https://t.co/8nD3jQvCuh

𝓑𝓮𝓷𝓮𝓯𝓲𝓽𝓼:

💠Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques

💠Implement the full supervised learning pipeline, from data preprocessing and feature engineering to model selection, training, and evaluation

💠Understand key learning tasks, including classification, regression, multi-label, and multi-output problems

💠Implement foundational algorithms from scratch, including linear and logistic regression, decision trees, gradient boosting, and SVMs

💠Gain hands-on experience with industry-standard tools such as Scikit-Learn, XGBoost, and NLTK

💠Refine and optimize your models using techniques such as hyperparameter tuning, cross-validation, and calibration

💠Work with diverse data types, including tabular data, text, and images

💠Address real-world challenges such as imbalanced datasets, missing data, and high-dimensional inputs

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HLONIPANI L. MADONDO

@hlonipanie

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