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

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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

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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 and Data Analytics
Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation,feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications,  data science glossary
data wrangling. decision trees. deep learning. dependent variable. dimension reduction. discrete variable. econometrics. feature. feature engineering. GATE .. “Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of  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   Machine Learning: An In-Depth Guide — Data Selection - Medium
The quality, amount, preparation, and selection of data is critical to the success of a machine learning solution. Feature Selection and Feature Engineering Some advanced techniques used for feature selection are principle component analysis (PCA), singular value decomposition (SVD), and Linear  Staff Engineer - Machine Learning – Intuit Careers
Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark). Basic knowledge ofmachine learning techniques (i.e. classification, regression, and clustering). Understand machine learning principles (training, validation, etc.) Knowledge of data query and 

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