Machine Learning With Python pdf book was written by Sebastian Raschka and Vahid Mirjalili, two experienced Machine Learning practitioners who have authored several Machine Learning books before. Published by Packt Publishing, the book was released on December 11th, 2017, and is available in both printed and digital formats.
The themes covered in this Machine Learning With Python pdf include supervised learning techniques such as SVM, Decision Trees, and logistic regression; unsupervised learning methods such as PCA and k-means clustering; practical Machine Learning algorithms such as Neural Networks, Support Vector Machines (SVMs), Random Forests, Boosting Techniques, and Bayesian Network Models; Machine Learning related topics like feature engineering and Hyperparam.
This book is available for download on our website in a pdf file. Also, it is attainable in printed format. This pdf has sold thousands of copies worldwide and has garnered numerous positive reviews from Machine Learning professionals and students alike.
Summary of Machine Learning With Python
This novel is about Machine Learning With Python pdf, a comprehensive guide to Machine Learning using the most popular programming language of all: Python. The book covers Machine Learning concepts in detail and how they are applied in real-world scenarios.
It also provides an overview of Machine Learning algorithms and libraries such as sci-kit-learn, pandas, and NumPy along with examples of how to use them. Additionally, Machine Learning With Python pdf provides an overview of Machine Learning tools and techniques like clustering, decision tree learning, artificial neural networks, and support vector machines.
Lastly, the book covers Machine Learning best practices with a focus on model evaluation and deployment. Machine Learning With Python pdf is suitable for both beginners in Machine Learning as well as experienced Machine Learning practitioners.
Machine Learning With Python Book Details
|Book||Machine Learning With Python|
|Author||Sebastian Raschka and Vahid Mirjalili|
|Number of Pages||476|
About the Author
Sebastian Raschka and Vahid Mirjalili are Machine Learning experts and authors of the Machine Learning With Python pdf. Both Sebastian and Vahid have years of experience in Machine Learning and have written multiple Machine Learning projects and books.
Sebastian Raschka was born and raised in Germany. He obtained his Doctoral degree from the University of Wisconsin-Madison where he specialized in Machine Learning and Data Science. Sebastian is an active Machine Learning researcher, blogger, and open-source contributor.
Vahid Mirjalili is an Iranian Machine Learning expert, author, and entrepreneur. He obtained his Doctoral degree in Machine Learning from the University of Toronto where he specialized in Machine Learning and Deep Learning. Vahid has co-authored multiple Machine Learning books and projects with Sebastian Raschka.
Sebastian and Vahid have worked together for years in Machine Learning, having written multiple Machine Learning projects and books. They are both Machine Learning experts and active Machine Learning researchers. Sebastian is an open-source contributor and blogger who has developed many Machine Learning applications.
Machine Learning With Python Book Multiple Languages Editions
This interesting novel is available in multiple languages. You can study this book in any language of your interest.
|Book Editions||Check Now|
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FAQs (Frequently Asked Questions)
Who is the author of the Machine Learning With Python pdf?
The pdf was authored by Machine Learning experts Sebastian Raschka and Vahid Mirjalili.
What is the genre of Machine Learning With Python?
This classic book is related to Domestic fiction and Computer.
How many pages are in Machine Learning With Python?
Machine Learning With Python has 476 pages in total.
What rating is Machine Learning With Python?
The novel earned a 4.5 out of 5-star rating from its readers.
What topics does Machine Learning With Python cover?
This novel covers Machine Learning concepts in detail and how they are applied in real-world scenarios.