Data Science grāmatas
Business Skills for Data Scientists: Practical Guidance in Six Key Topics
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Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics
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Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics
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Textbook of Machine Learning and Data Mining: with Bioinformatics Applications
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Raising Heretics: Teaching Kids to Change the World
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Data Science Interview: Prep for SQL, Panda, Python, R Language, Machine Learning, DBMS and RDBMS - And More - The Full Data Scientist Interview Handbook
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Deep Learning for Beginners A Comprehensive Introduction of Deep Learning Fundamentals for Beginners to Understanding Frameworks, Neural Networks, Large Datasets, and Creative Applications with Ease
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Data Science From Scratch The #1 Data Science Guide For Everything A Data Scientist Needs To Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, And Decision Trees
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Machine Learning For Beginners An Introduction for Beginners, Why Machine Learning Matters Today and How Machine Learning Networks, Algorithms, Concepts and Neural Networks Really Work
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Python Machine Learning A Practical Beginner's Guide for Understanding Machine Learning, Deep Learning and Neural Networks with Python, Scikit-Learn, Tensorflow and Keras
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Python Machine Learning For Beginners Handbook For Machine Learning, Deep Learning And Neural Networks Using Python, Scikit-Learn And TensorFlow
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Neural Networks A Practical Guide For Understanding And Programming Neural Networks And Useful Insights For Inspiring Reinvention
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Raspberry Pi 3 A Practical Beginner's Guide To Understanding The Full Potential Of Raspberry Pi 3 By Starting Your Own Projects Using Python Programming
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Deep Learning For Beginners 2 Manuscripts: Deep Learning For Beginners And Data Science From Scratch
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The Missing B in AI: Why Behavioral AI is the Missing Piece in How We Design Products, Services, and Systems
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The Complete Software Engineering Lifecycle: Methods, Patterns, and Implementation
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