SKU/Artículo: AMZ-1446154777
DATA SCIENCE TECHNIQUES: CLASSIFICATION, NAIVE BAYES, kNN AND PATTERN RECOGNITION Examples with MATLAB
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0.35 kg
0.35 kg
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Amazon
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USA
USA
Sobre este producto
- Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning. The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. Organizations are increasingly the data science to interpret information and provide actionable recommendations to improve business outcomes. The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability. This book develops data science techniques for data analysis, specialy Support Vector Machine, Discriminant Analysis, Decision Trees, Logistic Regression, Nearest Neighbor Classifiers (kNN), Ensemble Classifiers, Naive Bayes, Pattern Recognition, and Neural Networks.
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AR$102.518
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AR$70.697
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