Machine learning fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning/ Hyatt Saleh.

Saleh  Hyatt

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dc.contributor.author	Saleh Hyatt
dc.date.accessioned	2024-01-26T21:37:56Z
dc.date.available	2024-01-26T21:37:56Z
dc.date.issued	2018
dc.identifier.citation	Saleh. Machine learning fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning - 1 online resource (240 p.) - URL: https://libweb.kpfu.ru/ebsco/pdf/1948716.pdf
dc.identifier.isbn	1789801761
dc.identifier.isbn	9781789801767
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/178508
dc.description	Description based upon print version of record.
dc.description	Supervised Learning Algorithms: Predict Annual Income
dc.description.abstract	As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the differences between supervised and unsupervised models and by ...
dc.description.tableofcontents	Intro; Preface; Introduction to Scikit-Learn; Introduction; Scikit-Learn; Advantages of Scikit-Learn; Disadvantages of Scikit-Learn; Data Representation; Tables of Data; Features and Target Matrices; Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices; Activity 1: Selecting a Target Feature and Creating a Target Matrix; Data Preprocessing; Messy Data; Exercise 2: Dealing with Messy Data; Dealing with Categorical Features; Exercise 3: Applying Feature Engineering over Text Data; Rescaling Data; Exercise 4: Normalizing and Standardizing Data
dc.description.tableofcontents	Activity 2: Preprocessing an Entire DatasetScikit-Learn API; How Does It Work?; Supervised and Unsupervised Learning; Supervised Learning; Unsupervised Learning; Summary; Unsupervised Learning: Real-Life Applications; Introduction; Clustering; Clustering Types; Applications of Clustering; Exploring a Dataset: Wholesale Customers Dataset; Understanding the Dataset; Data Visualization; Loading the Dataset Using Pandas; Visualization Tools; Exercise 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset; Activity 3: Using Data Visualization to Aid the Preprocessing Process
dc.description.tableofcontents	K-means AlgorithmUnderstanding the Algorithm; Exercise 6: Importing and Training the k-means Algorithm over a Dataset; Activity 4: Applying the k-means Algorithm to a Dataset; Mean-Shift Algorithm; Understanding the Algorithm; Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset; Activity 5: Applying the Mean-Shift Algorithm to a Dataset; DBSCAN Algorithm; Understanding the Algorithm; Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters
dc.description.tableofcontents	Available Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index; Activity 7: Measuring and Comparing the Performance of the Algorithms; Summary; Supervised Learning: Key Steps; Introduction; Model Validation and Testing; Data Partition; Split Ratio; Exercise 10: Performing Data Partition over a Sample Dataset; Cross Validation; Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set; Activity 8: Data Partition over a Handwritten Digit Dataset; Evaluation Metrics
dc.description.tableofcontents	Evaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification Task; Choosing an Evaluation Metric; Evaluation Metrics for Regression Tasks; Exercise 13: Calculating Evaluation Metrics over a Regression Task; Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset; Error Analysis; Bias, Variance, and Data Mismatch; Exercise 14: Calculating the Error Rate over Different Sets of Data; Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits; Summary
dc.language	English
dc.language.iso	en
dc.subject.other	Python (Computer program language)
dc.subject.other	Machine learning.
dc.subject.other	Artificial intelligence.
dc.subject.other	COMPUTERS / Programming Languages / Python.
dc.subject.other	Electronic books.
dc.title	Machine learning fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning/ Hyatt Saleh.
dc.type	Book
dc.description.pages	1 online resource (240 p.)
dc.collection	Электронно-библиотечные системы
dc.source.id	EN05CEBSCO05C131030