Kirk Borne

"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