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Data Science: Questions and Answers


Author: George A Duckett

Publisher: CreateSpace Independent Publishing Platform


Publish Date: March 21, 2016

ISBN-10: 1530655277

Pages: 262

File Type: PDF

Language: English

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Book Preface

Data Science: Questions and Answers

Machine Learning

Skip to questions,

Wiki by user dawny33


From The Discipline of Machine Learning by Tom Mitchell:

The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” This question covers a broad range of learning tasks, such as how to design autonomous mobile robots that learn to navigate from their own experience, how to data mine historical medical records to learn which future patients will respond best to which treatments, and how to build search engines that automatically customize to their user’s interests. To be more precise, we say that a machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E. Depending on how we specify T, P, and E, the learning task might also be called by names such as data mining, autonomous discovery, database updating, programming by example, etc.

Data Science: Questions and Answers

Table of Contents

About this book
Machine Learning (95 questions)
Bigdata (30 questions)
Data Mining (28 questions)
Classification (28 questions)
Neuralnetwork (23 questions)
Statistics (19 questions)
Python (19 questions)
Clustering (15 questions)
R (14 questions)
Text Mining (14 questions)
NLP (13 questions)
Dataset (12 questions)
Efficiency (11 questions)
Algorithms (11 questions)
Hadoop (11 questions)
SVM (11 questions)
Tools (9 questions)
Recommendation (9 questions)
Visualization (9 questions)
Databases (8 questions)
Feature Selection (8 questions)
NoSQL (7 questions)
Predictive Modeling (7 questions)
Definitions (6 questions)
Education (6 questions)
Search (5 questions)
Similarity (5 questions)
Social Network Analysis (5 questions)
Time Series (5 questions)
Scalability (4 questions)
Beginner (4 questions)
Data Cleaning (3 questions)
Aws (3 questions)
Graphs (3 questions)
Cross Validation (3 questions)
Apache Spark (3 questions)
Categorical Data (2 questions)
Hierarchical Data Format (2 questions)
Xgboost (2 questions)
Sequence (1 question)

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