| LDR | | 05435cmm u2200589Ii 4500 |
| 001 | | 000000317905 |
| 003 | | OCoLC |
| 005 | | 20230525183649 |
| 006 | | m d |
| 007 | | cr unu|||||||| |
| 008 | | 201027s2020 enka o 000 0 eng d |
| 019 | |
▼a 1178714596
▼a 1181842248
▼a 1191043302 |
| 020 | |
▼a 9781838985462 |
| 020 | |
▼a 1838985468 |
| 020 | |
▼z 9781839219061 |
| 035 | |
▼a 2532421
▼b (N$T) |
| 035 | |
▼a (OCoLC)1201697296
▼z (OCoLC)1178714596
▼z (OCoLC)1181842248
▼z (OCoLC)1191043302 |
| 037 | |
▼a CL0501000160
▼b Safari Books Online |
| 040 | |
▼a UMI
▼b eng
▼e rda
▼e pn
▼c UMI
▼d EBLCP
▼d UKAHL
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▼d OCLCF
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| 049 | |
▼a MAIN |
| 050 | 4 |
▼a QA76.87 |
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▼a 006.31
▼2 23 |
| 100 | 1 |
▼a Saleh, Hyatt,
▼e author. |
| 245 | 14 |
▼a The machine learning workshop. |
| 250 | |
▼a Second edition. |
| 260 | |
▼a Birmingham, UK :
▼b Packt Publishing,
▼c 2020. |
| 300 | |
▼a 1 online resource (1 volume) :
▼b illustrations |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
| 338 | |
▼a online resource
▼b cr
▼2 rdacarrier |
| 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. |
| 588 | |
▼a Description based on online resource; title from title page (viewed October 22, 2020). |
| 590 | |
▼a OCLC control number change |
| 650 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a Neural networks (Computer science) |
| 650 | 0 |
▼a Artificial intelligence. |
| 650 | 7 |
▼a Machine learning
▼2 fast
▼0 (OCoLC)fst01004795 |
| 650 | 7 |
▼a Python (Computer program language)
▼2 fast
▼0 (OCoLC)fst01084736 |
| 655 | 4 |
▼a Electronic books. |
| 655 | 0 |
▼a Electronic books. |
| 776 | 08 |
▼i Print version:
▼a Saleh, Hyatt
▼t The the Machine Learning Workshop : Get Ready to Develop Your Own High-Performance Machine Learning Algorithms with Scikit-learn, 2nd Edition
▼d Birmingham : Packt Publishing, Limited,c2020 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2532421 |
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▼a ProQuest Ebook Central
▼b EBLB
▼n EBL6269367 |
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▼a Askews and Holts Library Services
▼b ASKH
▼n AH37507361 |
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▼b YANK
▼n 301401445 |
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▼n 2532421 |
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▼a 관리자 |
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