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

Title:
Machine learning fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning/ Hyatt Saleh.
Authors:
Source:
EN05CEBSCO05C131030
Publication Year:
2018
Collection:
Kazan Federal University Digital Repository
Document Type:
Buch book
Language:
English
ISBN:
978-1-78980-176-7
1-78980-176-1
Accession Number:
edsbas.4C25D48E
Database:
BASE

Weitere Informationen

Description based upon print version of record. ; Supervised Learning Algorithms: Predict Annual Income ; 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 . ; 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 ; 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 ; 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 ...