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Publisher ‏ : ‎ Packt Publishing; 3rd ed. edition (December 9, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 772 pages
ISBN-10 ‏ : ‎ 1789955750
ISBN-13 ‏ : ‎ 978-1789955750
Item Weight ‏ : ‎ 2.9 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 1.59 inches

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

Are you interested in diving into the world of machine learning and deep learning using Python? Look no further! In this post, we will explore how you can leverage popular libraries such as scikit-learn and TensorFlow 2 to build powerful machine learning models.

Scikit-learn is a versatile machine learning library that allows you to easily build and train machine learning models. With a wide range of algorithms and tools, scikit-learn is perfect for both beginners and experienced data scientists. From classification to regression, clustering to dimensionality reduction, scikit-learn has you covered.

On the other hand, TensorFlow 2 is a powerful deep learning framework that is widely used in the industry. With TensorFlow 2, you can easily build and train deep neural networks for a variety of tasks, including image classification, natural language processing, and more. TensorFlow 2 also provides high-level APIs such as Keras, making it easy to quickly prototype and experiment with different neural network architectures.

In this post, we will walk you through the basics of machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. We will cover topics such as data preprocessing, model training, evaluation, and deployment. By the end of this post, you will have a solid understanding of how to use these powerful libraries to build your own machine learning models.

So, whether you are a beginner looking to get started with machine learning or an experienced data scientist looking to expand your skills, Python, scikit-learn, and TensorFlow 2 have everything you need to succeed. Stay tuned for more updates and tutorials on machine learning and deep learning with Python!
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