【独家发布】Statistics for Machine Learning

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1. Statistics for Machine Learning
   
2. Authors: Pratap Dangeti
   
3. 
   
4. ISBN-10 书号: 1788295757
   
5. 
   
6. ISBN-13 书号: 9781788295758
   
7. 
   
8. Release 出版日期: 2017-09-06
   
9. 
   
10. pages 页数: (311)
    
11. 
    
12. 
    
13. 49.99
    
14. 
    
15. Book Description
    
16. Key Features
    
17. Learn about the statistics behind powerful predictive models with p-value, ANOVA, F-statistics.
    
18. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
    
19. Master the statistical aspect of machine learning with the help of this example-rich guide in R & Python.
    
20. Book Description
    
21. Complex statistics in machine learning worries a lot of developers. Knowing statistics helps in building strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for machine learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and make you comfortable with it. You will come across programs for performing tasks such as model, parameters fitting, regression, classification, density collection, working with vectors, matrices, and more.By the end of the book, you will understand concepts of required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problems.
    
22. 
    
23. What you will learn
    
24. Understanding Statistical & Machine learning fundamentals necessary to build models
    
25. Understanding major differences & parallels between statistics way of solving problem & machine learning way of solving problem
    
26. Know how to prepare data and “feed” the models by using the appropriate machine learning algorithms from the adequate R & Python packages
    
27. Analyze the results and tune the model appropriately to his or her own predictive goals
    
28. Understand concepts of required statistics for Machine Learning
    
29. Draw parallels between statistics and machine learning
    
30. Understand each component of machine learning models and see impact of changing them
    
31. Contents
    
32. Chapter 1. Questions
    
33. Chapter 2. Journey From Statistics To Machine Learning
    
34. Chapter 3. Parallelism Of Statistics And Machine Learning
    
35. Chapter 4. Logistic Regression Versus Random Forest
    
36. Chapter 5. Tree-Based Machine Learning Models
    
37. Chapter 6. K-Nearest Neighbors And Naive Bayes
    
38. Chapter 7. Support Vector Machines And Neural Networks
    
39. Chapter 8. Recommendation Engines
    
40. Chapter 9. Unsupervised Learning
    
41. Chapter 10. Reinforcement Learning

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