As part of the best-selling *Pocket Primer* series, this book prepares programmers for machine learning and deep learning with Tensor Flow. It begins with a quick introduction to Python, followed by chapters on Num Py, Pandas, Matplotlib, and scikit-learn. The final chapters provide Tensor Flow 1.x code samples, including detailed examples for Tensor Flow Dataset, crucial for Tensor Flow 2.
The journey starts with Python basics and progresses through essential data manipulation and visualization libraries. You’ll explore machine learning fundamentals with scikit-learn before diving into Tensor Flow, learning to construct data pipelines with Tensor Flow Dataset APIs like map(), filter(), and batch().
Understanding these concepts is vital for modern AI applications. This book transitions readers from basic programming to advanced machine learning and deep learning techniques, blending theory with practical skills. Companion files with source code enhance learning, making this an essential resource for mastering Python, machine learning, and Tensor Flow.
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