توضیحات
This book serves as a complete resource for mastering machine learning and deep learning with Python. It introduces readers to supervised and unsupervised learning methods, covering algorithms like regression, decision trees, clustering, and ensemble methods using Scikit-learn. The book then moves into neural networks and deep learning with TensorFlow and Keras, explaining concepts such as CNNs, RNNs, and optimization techniques. Readers also learn data preprocessing, model evaluation, hyperparameter tuning, and deployment strategies. Through real-world examples and coding exercises, the authors bridge theory with practice, enabling readers to build scalable AI models and intelligent data-driven applications. It’s an essential guide for data scientists, AI engineers, and advanced Python programmers.










نقد و بررسیها
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