The machine learning workshop.:
With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problem...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2020.
|
| Ausgabe: | Second edition. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781838985462 1838985468 |
Internformat
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| 245 | 1 | 4 | |a The machine learning workshop. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2020. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
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| 505 | 0 | |a Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Scikit-Learn -- Introduction -- Introduction to Machine Learning -- Applications of ML -- Choosing the Right ML Algorithm -- Scikit-Learn -- Advantages of Scikit-Learn -- Disadvantages of Scikit-Learn -- Other Frameworks -- Data Representation -- Tables of Data -- Features and Target Matrices -- Exercise 1.01: Loading a Sample Dataset and Creating the Features and Target Matrices -- Activity 1.01: Selecting a Target Feature and Creating a Target Matrix -- Data Preprocessing -- Messy Data -- Missing Values | |
| 505 | 8 | |a Outliers -- Exercise 1.02: Dealing with Messy Data -- Dealing with Categorical Features -- Feature Engineering -- Exercise 1.03: Applying Feature Engineering to Text Data -- Rescaling Data -- Exercise 1.04: Normalizing and Standardizing Data -- Activity 1.02: Pre-processing an Entire Dataset -- Scikit-Learn API -- How Does It Work? -- Estimator -- Predictor -- Transformer -- Supervised and Unsupervised Learning -- Supervised Learning -- Unsupervised Learning -- Summary -- Chapter 2: Unsupervised Learning -- Real-Life Applications -- Introduction -- Clustering -- Clustering Types | |
| 505 | 8 | |a Applications of Clustering -- Exploring a Dataset -- Wholesale Customers Dataset -- Understanding the Dataset -- Data Visualization -- Loading the Dataset Using pandas -- Visualization Tools -- Exercise 2.01: Plotting a Histogram of One Feature from the Circles Dataset -- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process -- k-means Algorithm -- Understanding the Algorithm -- Initialization Methods -- Choosing the Number of Clusters -- Exercise 2.02: Importing and Training the k-means Algorithm over a Dataset -- Activity 2.02: Applying the k-means Algorithm to a Dataset | |
| 505 | 8 | |a Mean-Shift Algorithm -- Understanding the Algorithm -- Exercise 2.03: Importing and Training the Mean-Shift Algorithm over a Dataset -- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset -- DBSCAN Algorithm -- Understanding the Algorithm -- Exercise 2.04: Importing and Training the DBSCAN Algorithm over a Dataset -- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset -- Evaluating the Performance of Clusters -- Available Metrics in Scikit-Learn -- Exercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index | |
| 505 | 8 | |a Activity 2.05: Measuring and Comparing the Performance of the Algorithms -- Summary -- Chapter 3: Supervised Learning -- Key Steps -- Introduction -- Supervised Learning Tasks -- Model Validation and Testing -- Data Partitioning -- Split Ratio -- Exercise 3.01: Performing a Data Partition on a Sample Dataset -- Cross-Validation -- Exercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set -- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset -- Evaluation Metrics -- Evaluation Metrics for Classification Tasks -- Confusion Matrix -- Accuracy | |
| 520 | |a With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 2 | |a Neural Networks, Computer |0 https://id.nlm.nih.gov/mesh/D016571 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Saleh, Hyatt. |t Machine Learning Workshop : Get Ready to Develop Your Own High-Performance Machine Learning Algorithms with Scikit-learn, 2nd Edition. |d Birmingham : Packt Publishing, Limited, ©2020 |
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| contents | Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Scikit-Learn -- Introduction -- Introduction to Machine Learning -- Applications of ML -- Choosing the Right ML Algorithm -- Scikit-Learn -- Advantages of Scikit-Learn -- Disadvantages of Scikit-Learn -- Other Frameworks -- Data Representation -- Tables of Data -- Features and Target Matrices -- Exercise 1.01: Loading a Sample Dataset and Creating the Features and Target Matrices -- Activity 1.01: Selecting a Target Feature and Creating a Target Matrix -- Data Preprocessing -- Messy Data -- Missing Values Outliers -- Exercise 1.02: Dealing with Messy Data -- Dealing with Categorical Features -- Feature Engineering -- Exercise 1.03: Applying Feature Engineering to Text Data -- Rescaling Data -- Exercise 1.04: Normalizing and Standardizing Data -- Activity 1.02: Pre-processing an Entire Dataset -- Scikit-Learn API -- How Does It Work? -- Estimator -- Predictor -- Transformer -- Supervised and Unsupervised Learning -- Supervised Learning -- Unsupervised Learning -- Summary -- Chapter 2: 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 2.01: Plotting a Histogram of One Feature from the Circles Dataset -- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process -- k-means Algorithm -- Understanding the Algorithm -- Initialization Methods -- Choosing the Number of Clusters -- Exercise 2.02: Importing and Training the k-means Algorithm over a Dataset -- Activity 2.02: Applying the k-means Algorithm to a Dataset Mean-Shift Algorithm -- Understanding the Algorithm -- Exercise 2.03: Importing and Training the Mean-Shift Algorithm over a Dataset -- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset -- DBSCAN Algorithm -- Understanding the Algorithm -- Exercise 2.04: Importing and Training the DBSCAN Algorithm over a Dataset -- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset -- Evaluating the Performance of Clusters -- Available Metrics in Scikit-Learn -- Exercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index Activity 2.05: Measuring and Comparing the Performance of the Algorithms -- Summary -- Chapter 3: Supervised Learning -- Key Steps -- Introduction -- Supervised Learning Tasks -- Model Validation and Testing -- Data Partitioning -- Split Ratio -- Exercise 3.01: Performing a Data Partition on a Sample Dataset -- Cross-Validation -- Exercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set -- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset -- Evaluation Metrics -- Evaluation Metrics for Classification Tasks -- Confusion Matrix -- Accuracy |
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| spelling | Saleh, Hyatt, author. The machine learning workshop. Second edition. Birmingham, UK : Packt Publishing, 2020. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (viewed October 22, 2020). Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Scikit-Learn -- Introduction -- Introduction to Machine Learning -- Applications of ML -- Choosing the Right ML Algorithm -- Scikit-Learn -- Advantages of Scikit-Learn -- Disadvantages of Scikit-Learn -- Other Frameworks -- Data Representation -- Tables of Data -- Features and Target Matrices -- Exercise 1.01: Loading a Sample Dataset and Creating the Features and Target Matrices -- Activity 1.01: Selecting a Target Feature and Creating a Target Matrix -- Data Preprocessing -- Messy Data -- Missing Values Outliers -- Exercise 1.02: Dealing with Messy Data -- Dealing with Categorical Features -- Feature Engineering -- Exercise 1.03: Applying Feature Engineering to Text Data -- Rescaling Data -- Exercise 1.04: Normalizing and Standardizing Data -- Activity 1.02: Pre-processing an Entire Dataset -- Scikit-Learn API -- How Does It Work? -- Estimator -- Predictor -- Transformer -- Supervised and Unsupervised Learning -- Supervised Learning -- Unsupervised Learning -- Summary -- Chapter 2: 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 2.01: Plotting a Histogram of One Feature from the Circles Dataset -- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process -- k-means Algorithm -- Understanding the Algorithm -- Initialization Methods -- Choosing the Number of Clusters -- Exercise 2.02: Importing and Training the k-means Algorithm over a Dataset -- Activity 2.02: Applying the k-means Algorithm to a Dataset Mean-Shift Algorithm -- Understanding the Algorithm -- Exercise 2.03: Importing and Training the Mean-Shift Algorithm over a Dataset -- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset -- DBSCAN Algorithm -- Understanding the Algorithm -- Exercise 2.04: Importing and Training the DBSCAN Algorithm over a Dataset -- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset -- Evaluating the Performance of Clusters -- Available Metrics in Scikit-Learn -- Exercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index Activity 2.05: Measuring and Comparing the Performance of the Algorithms -- Summary -- Chapter 3: Supervised Learning -- Key Steps -- Introduction -- Supervised Learning Tasks -- Model Validation and Testing -- Data Partitioning -- Split Ratio -- Exercise 3.01: Performing a Data Partition on a Sample Dataset -- Cross-Validation -- Exercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set -- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset -- Evaluation Metrics -- Evaluation Metrics for Classification Tasks -- Confusion Matrix -- Accuracy With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Neural networks (Computer science) fast Artificial intelligence fast Machine learning fast Python (Computer program language) fast Print version: Saleh, Hyatt. Machine Learning Workshop : Get Ready to Develop Your Own High-Performance Machine Learning Algorithms with Scikit-learn, 2nd Edition. Birmingham : Packt Publishing, Limited, ©2020 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2532421 Volltext |
| spellingShingle | Saleh, Hyatt The machine learning workshop. Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Scikit-Learn -- Introduction -- Introduction to Machine Learning -- Applications of ML -- Choosing the Right ML Algorithm -- Scikit-Learn -- Advantages of Scikit-Learn -- Disadvantages of Scikit-Learn -- Other Frameworks -- Data Representation -- Tables of Data -- Features and Target Matrices -- Exercise 1.01: Loading a Sample Dataset and Creating the Features and Target Matrices -- Activity 1.01: Selecting a Target Feature and Creating a Target Matrix -- Data Preprocessing -- Messy Data -- Missing Values Outliers -- Exercise 1.02: Dealing with Messy Data -- Dealing with Categorical Features -- Feature Engineering -- Exercise 1.03: Applying Feature Engineering to Text Data -- Rescaling Data -- Exercise 1.04: Normalizing and Standardizing Data -- Activity 1.02: Pre-processing an Entire Dataset -- Scikit-Learn API -- How Does It Work? -- Estimator -- Predictor -- Transformer -- Supervised and Unsupervised Learning -- Supervised Learning -- Unsupervised Learning -- Summary -- Chapter 2: 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 2.01: Plotting a Histogram of One Feature from the Circles Dataset -- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process -- k-means Algorithm -- Understanding the Algorithm -- Initialization Methods -- Choosing the Number of Clusters -- Exercise 2.02: Importing and Training the k-means Algorithm over a Dataset -- Activity 2.02: Applying the k-means Algorithm to a Dataset Mean-Shift Algorithm -- Understanding the Algorithm -- Exercise 2.03: Importing and Training the Mean-Shift Algorithm over a Dataset -- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset -- DBSCAN Algorithm -- Understanding the Algorithm -- Exercise 2.04: Importing and Training the DBSCAN Algorithm over a Dataset -- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset -- Evaluating the Performance of Clusters -- Available Metrics in Scikit-Learn -- Exercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index Activity 2.05: Measuring and Comparing the Performance of the Algorithms -- Summary -- Chapter 3: Supervised Learning -- Key Steps -- Introduction -- Supervised Learning Tasks -- Model Validation and Testing -- Data Partitioning -- Split Ratio -- Exercise 3.01: Performing a Data Partition on a Sample Dataset -- Cross-Validation -- Exercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set -- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset -- Evaluation Metrics -- Evaluation Metrics for Classification Tasks -- Confusion Matrix -- Accuracy Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Neural networks (Computer science) fast Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | The machine learning workshop. |
| title_auth | The machine learning workshop. |
| title_exact_search | The machine learning workshop. |
| title_full | The machine learning workshop. |
| title_fullStr | The machine learning workshop. |
| title_full_unstemmed | The machine learning workshop. |
| title_short | The machine learning workshop. |
| title_sort | machine learning workshop |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Neural networks (Computer science) fast Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Machine learning. Neural networks (Computer science) Artificial intelligence. Neural Networks, Computer Artificial Intelligence Machine Learning Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. Artificial intelligence Machine learning Python (Computer program language) |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2532421 |
| work_keys_str_mv | AT salehhyatt themachinelearningworkshop AT salehhyatt machinelearningworkshop |