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TABLE OF CONTENTS16 sections

Machine Learning for Data Science

01Machine Learning for Data Science 02Foundations Of Machine Learning And Data Science 03Mathematical And Statistical Foundations 04Machine Learning Lifecycle And Problem Framing 05Data Preparation And Feature Engineering 06Regression Models 07Classification Models 08Model Evaluation Validation And Tuning 09Ensemble Learning 10Clustering And Unsupervised Learning 11Dimensionality Reduction And Anomaly Detection 12Applied Machine Learning For Complex Data 13Neural Networks And Contemporary Ai 14Explainable Ethical And Responsible Machine Learning 15Deployment Mlops And Model Governance 16Machine Learning Strategy And Future Developments

Chapter 1

Foundations Of Machine Learning And Data Science

PREVIOUS SECTIONMachine Learning for Data Science UP NEXTMathematical And Statistical Foundations

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