{"product_id":"new-product-11","title":"Machine Learning A-Z: Python, Statistics \u0026 Real Projects","description":"\u003cp\u003e\u003cstrong\u003eMost machine learning books either drown you in mathematical formalism or hand you library calls with no explanation of what they do. This one does neither.\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eMachine Learning A-Z\u003c\/em\u003e is built on a simple conviction: you need both the intuition and the implementation. Every concept is developed from first principles, and every concept is then written out in working Python — not pseudocode, not fragments, but code you can run.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSix parts, twenty-three chapters, four appendices:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eFoundations\u003c\/strong\u003e — what machine learning actually is and how it differs from traditional programming, a reproducible Python environment, and the statistics and probability you genuinely need\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupervised learning\u003c\/strong\u003e — linear and logistic regression, decision trees and random forests, support vector machines, neural networks and deep learning, gradient boosting with XGBoost, LightGBM and CatBoost, plus k-nearest neighbours and naive Bayes\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUnsupervised learning\u003c\/strong\u003e — k-means, hierarchical clustering and DBSCAN, dimensionality reduction with PCA, t-SNE and UMAP, and autoencoders\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReinforcement learning\u003c\/strong\u003e — fundamentals, Q-learning and deep Q-networks, and policy gradient methods\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFour complete projects\u003c\/strong\u003e — house price prediction, customer churn prediction, customer segmentation, and a stock trading RL agent, each carried end to end\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePractice and frontier\u003c\/strong\u003e — model evaluation, validation and selection, and a chapter on transformers and large language models\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eAppendices cover Python environment setup, mathematical notation, further reading, and exercises for every chapter.\u003c\/p\u003e\n\u003cp\u003eWritten for practitioners who are comfortable with Python and have a passing familiarity with the mathematics. You do not need a PhD, and you do not need to have taken a formal machine learning course.\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eCode targets Python 3.12 with scikit-learn 1.7, NumPy 2.2, Pandas 2.3, PyTorch 2.7, TensorFlow 2.19, XGBoost 3.0, LightGBM 4.6 and Gymnasium 1.1. The book notes where later releases have since shipped.\u003c\/em\u003e\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e","brand":"Professional Developer Store","offers":[{"title":"Default Title","offer_id":58295553130828,"sku":null,"price":4.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0849\/7728\/7500\/files\/MachineLearningA-Z-Cover_front_6.93x9.84.png?v=1785849337","url":"https:\/\/professionaldeveloper.store\/products\/new-product-11","provider":"Professional Developer Store","version":"1.0","type":"link"}