Python: Advanced Predictive Analytics: 2017-12 python重量级高级大数据分析...
Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications By 作者: Ashish Kumar – Joseph Babcock ISBN-10 书号: 1788992369 ISBN-13 书号: 9781788992367 Release 出版日期: 2017-12-27 pages 页数: (660 ) $99.99 Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You’ll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: Learning Predictive Analytics with Python Mastering Predictive Analytics with Python What You Will Learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis
7-5 LL(1)分析-最左推导 本题目为LL(1)分析程序最后一步，输出最左推导序列。 输入格式: 输入一个上下文无关文法，先输入文法产生式条数，再输入各个产生式，字符“ε”用“@”表示。 输入一个待分析的字符串，以"#"结束。 输出格式: 输出待分析字符串的最左推导。 输入样例: 在这里给出一组输入。例如： 9 S->MH S->a H->LSo H->@ K->dML K->@ L->eHf M->K M->bLM bef# 输出样例: 在这里给出相应的输出。例如： S=>MH=>bLMH=>beHfMH=>befMH=>befKH=>befH=>bef 代码长度限制 100 KB 时间限制 400 ms 内存限制 128 MB
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