Title
Handson Machine Learning With Scikitlearn, Keras, And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems
Delivery time: 8-12 business days (International)
Through A Recent Series Of Breakthroughs, Deep Learning Has Boosted The Entire Field Of Machine Learning. Now, Even Programmers Who Know Close To Nothing About This Technology Can Use Simple, Efficient Tools To Implement Programs Capable Of Learning From Data. This Bestselling Book Uses Concrete Examples, Minimal Theory, And Productionready Python Frameworks (Scikitlearn, Keras, And Tensorflow) To Help You Gain An Intuitive Understanding Of The Concepts And Tools For Building Intelligent Systems.With This Updated Third Edition, Author Aurlien Gron Explores A Range Of Techniques, Starting With Simple Linear Regression And Progressing To Deep Neural Networks. Numerous Code Examples And Exercises Throughout The Book Help You Apply What You'Ve Learned. Programming Experience Is All You Need To Get Started.Use Scikitlearn To Track An Example Ml Project End To End Explore Several Models, Including Support Vector Machines, Decision Trees, Random Forests, And Ensemble Methods Exploit Unsupervised Learning Techniques Such As Dimensionality Reduction, Clustering, And Anomaly Detection Dive Into Neural Net Architectures, Including Convolutional Nets, Recurrent Nets, Generative Adversarial Networks, Autoencoders, Diffusion Models, And Transformers Use Tensorflow And Keras To Build And Train Neural Nets For Computer Vision, Natural Language Processing, Generative Models, And Deep Reinforcement Learning
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What topics are covered in 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'? A: The book covers a wide range of topics, including linear regression, deep neural networks, support vector machines, decision trees, random forests, ensemble methods, unsupervised learning techniques, and various neural net architectures such as convolutional nets, recurrent nets, and transformers.
- Q: Is prior programming knowledge required to understand this book? A: Yes, basic programming experience is recommended to effectively follow along with the examples and exercises, as the book is designed for readers with some familiarity with Python.
- Q: How does this book approach teaching machine learning concepts? A: The book uses concrete examples and minimal theory to provide an intuitive understanding of machine learning concepts, making it accessible even for those new to the field.
- Q: What is the format of the exercises in the book? A: Throughout the book, there are numerous code examples and exercises designed to help readers apply what they've learned and reinforce their understanding of the material.
- Q: Can this book help with practical machine learning projects? A: Yes, the book includes practical guidance on tracking an example machine learning project from start to finish using Scikit-learn and other Python frameworks.
- Q: What is the publication date of the latest edition? A: The latest edition of the book was published on November 8, 2022.
- Q: Does this book cover deep learning frameworks? A: Yes, it covers popular deep learning frameworks such as TensorFlow and Keras, including how to build and train neural networks for various applications like computer vision and natural language processing.
- Q: What is the total number of pages in the book? A: The book contains a total of 861 pages.
- Q: Is the book suitable for beginners in machine learning? A: Yes, the book is suitable for beginners as it starts with fundamental concepts and gradually progresses to more complex topics, making it accessible to those new to machine learning.
- Q: What edition is this book, and how does it compare to previous editions? A: This is the third edition of the book, which includes updated content and techniques reflecting the latest advancements in machine learning and deep learning.