Machine learning fundamentals : Use Python and scikit-learn to get up and running with the hottest developments in machine learning /: Use Python and scikit-learn to get up and running with the hottest developments in machine learning. ([2018])

• Record Type: Book
• Further Information: Note: Hyatt Saleh.
• Authors: Saleh, Hyatt
• Contents: 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 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters 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 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
• Publisher Details: Birmingham : Packt Publishing
• Publication Date: 2018
• Copyright Date: 2018
• Extent: 1 online resource
• Subjects: 005.133; Python (Computer program language); Machine learning; Artificial intelligence; COMPUTERS / Programming Languages / Python; Electronic books
• Languages: English
• ISBNs: 1789801761; 9781789801767
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.