Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. Alice Zheng, Amanda Casari

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists


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ISBN: 9781491953242 | 214 pages | 6 Mb

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  • Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
  • Alice Zheng, Amanda Casari
  • Page: 214
  • Format: pdf, ePub, fb2, mobi
  • ISBN: 9781491953242
  • Publisher: O'Reilly Media, Incorporated
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Download ebook from google Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists English version by Alice Zheng, Amanda Casari 9781491953242

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists by Alice Zheng, Amanda Casari Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic. Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. If you understand basic machine learning concepts like supervised and unsupervised learning, you’re ready to get started. Not only will you learn how to implement feature engineering in a systematic and principled way, you’ll also learn how to practice better data science. Learn exactly what feature engineering is, why it’s important, and how to do it well Use common methods for different data types, including images, text, and logs Understand how different techniques such as feature scaling and principal component analysis work Understand how unsupervised feature learning works in the case of deep learning for images

Feature Engineering for Machine Learning: Principles and - アマゾン
Amazon配送商品ならFeature Engineering for Machine Learning: Principles andTechniques for Data Scientistsが通常配送無料。更にAmazonならポイント還元本が 多数。Alice Zheng, Amanda Casari作品ほか、お急ぎ便対象商品は当日お届けも 可能。 Feature Engineering for Machine Learning Models (豆瓣) - 豆瓣读书
Feature Engineering for Machine Learning Models. Feature Engineering forMachine Learning Models. 作者: Alice Zheng 出版社: O′Reilly 原作名: MasteringFeature Engineering Principles and Techniques for Data Scientists 出版年: 2017- 12-31 页数: 200 定价: GBP 34.50 装帧: Paperback ISBN: 9781491953242. 豆瓣 评分. Feature Engineering Made Easy: Identify unique features from your - Google Books Result
Sinan Ozdemir, Divya Susarla - ‎2018 - Computers Introduction to Data Science | Metis
Intro to data science using Python focused on data acquisition, cleaning, aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation. Videos 1-6 of Linear Algebra review from Andrew Ng's Machine Learning course (labeled as: III. Linear Algebra Review ( Week 1,  Principal Machine Learning Engineer Job at Intuit in Austin, Texas
Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering). Understand machine learning principles (training, validation, etc. ) Knowledge of data query and data processing tools (i.e. SQL); Computerscience fundamentals: data structures, algorithms, performance 



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