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Machine Learning with Python
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A hands-on machine learning guide that moves from Python data handling to model training, evaluation, deployment, monitoring, and portfolio-ready projects.
Level: intermediateLanguage: EN EnglishRating: 0Reviews: 0
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Book contents
01Chapter 1: Introduction to Machine Learning02Chapter 2: Python for Machine Learning03Chapter 3: Mathematics for Machine Learning, Practical04Chapter 4: Data Collection and Loading05Chapter 5: Data Cleaning06Chapter 6: Exploratory Data Analysis, EDA07Chapter 7: Feature Engineering08Chapter 8: Regression Models09Chapter 9: Classification Models10Chapter 10: Ensemble Methods11Chapter 11: Model Evaluation Metrics12Chapter 12: Overfitting and Underfitting13Chapter 13: Hyperparameter Tuning14Chapter 14: Clustering15Chapter 15: Dimensionality Reduction16Chapter 16: Machine Learning with Scikit-Learn17Chapter 17: Model Pipelines and Automation18Chapter 18: Model Deployment Basics19Chapter 19: Model Monitoring20Chapter 20: Scaling ML Systems21Chapter 21: End-to-End Project 1: House Price Prediction22Chapter 22: End-to-End Project 2: Customer Segmentation23Chapter 23: End-to-End Project 3: Spam Detection24Chapter 24: Introduction to Deep Learning25Chapter 25: Natural Language Processing, NLP26Chapter 26: Time Series Forecasting27Chapter 27: ML Best Practices28Chapter 28: Building a Portfolio29Chapter 29: Machine Learning Career Guide30Appendix E: Deeper Practice and Extra Examples
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