Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. Alice Zheng, Amanda Casari
Feature-Engineering-for.pdf
ISBN: 9781491953242 | 214 pages | 6 Mb
- 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
Free ebook download for ipad Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (English literature)
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
bol.com | Feature Engineering for Machine Learning Models, Alice
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
The Mathematics of Machine Learning – Towards Data Science
Research in mathematical formulations and theoretical advancement of MachineLearning is ongoing and some researchers are working on more advancetechniques. I will state what I believe to be the minimum level of mathematics needed to be a Machine Learning Scientist/Engineer and the importance of each
Feature Engineering for Machine Learning: Principles - Amazon.it
Scopri Feature Engineering for Machine Learning: Principles and Techniques forData Scientists di Alice Zheng, Amanda Casari: spedizione gratuita per i clienti Prime e per ordini a partire da 29€ spediti da Amazon.
Feature Engineering Tips for Data Scientists and Business Analysts
Using methods like these is important because additional relevant variables increase model accuracy, which makes feature engineering an essential part of the modeling process. The full white of your model. This is true whether you are building logistic, generalized linear, or machine learning models.
Feature Engineering Made Easy: Identify unique features from your - Google Books Result
Sinan Ozdemir, Divya Susarla - 2018 - Computers
Essential Algorithms Every ML Engineer Needs to Know
Originally a technique from statistics they have become an important tool in everyMachine learning engineer's tool kit. Common Principle component analysis; Low Variance Filter; High Correlation Filter; Random Forests; Backward Feature Elimination / Forward Feature construction. This is not a
Book: Mastering Feature Engineering - Data Science Central
Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. T … Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks.
A manifesto for Agile data science - O'Reilly Media
Applying methods from Agile software development to data science projects. Building accurate predictive models can take many iterations of featureengineering and hyperparameter tuning. In data science, iteration is . These seven principles work together to drive the Agile data science methodology.
Deep learning - Wikipedia
Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Deep learning models are loosely related to information processing and communication patterns in a
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