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Dive deep into the intricacies of data preprocessing, exploring techniques to prepare your data for effective analysis. Master supervised learning algorithms like linear and logistic regression, support vector machines, and decision trees. Uncover the secrets of unsupervised learning, including clustering and dimensionality reduction, and gain insights into model evaluation and selection to fine-tune your results.
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FAQ Section
What programming languages are used in the book?
The book primarily focuses on Python, leveraging its extensive libraries for machine learning tasks.
What is the target audience for this book?
This book is designed for beginners and intermediate learners looking to gain a comprehensive understanding of machine learning.
Does the book include practical exercises and case studies?
Yes, the book features hands-on exercises and real-world case studies to reinforce learning and provide practical application examples.
What specific deep learning libraries are covered?
The book touches upon several deep learning libraries and frameworks, including TensorFlow and PyTorch, although the focus is broader than deep learning specifics.
