Python machine learning / (Record no. 69057)
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| fixed length control field | 06406cam a2200661Ii 4500 |
| 001 - CONTROL NUMBER | |
| control field | on1091899483 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220711203512.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190408s2019 inu o 000 0 eng d |
| 019 ## - | |
| -- | 1096293762 |
| -- | 1096457961 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119545675 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1119545676 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119545699 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1119545692 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119557500 |
| -- | (electronic bk. ; |
| -- | oBook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 111955750X |
| -- | (electronic bk. ; |
| -- | oBook) |
| 029 1# - (OCLC) | |
| OCLC library identifier | CHNEW |
| System control number | 001050890 |
| 029 1# - (OCLC) | |
| OCLC library identifier | CHVBK |
| System control number | 567422437 |
| 029 1# - (OCLC) | |
| OCLC library identifier | UKMGB |
| System control number | 019364424 |
| 037 ## - | |
| -- | 9781119545675 |
| -- | Wiley |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 005.133 |
| 100 1# - AUTHOR NAME | |
| Author | Lee, Wei-Meng, |
| 245 10 - TITLE STATEMENT | |
| Title | Python machine learning / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Cover; Title Page; Copyright; About the Author; About the Technical Editor; Credits; Acknowledgments; Contents at a glance; Contents; Introduction; Chapter 1 Introduction to Machine Learning; What Is Machine Learning?; What Problems Will Machine Learning Be Solving in This Book?; Classification; Regression; Clustering; Types of Machine Learning Algorithms; Supervised Learning; Unsupervised Learning; Getting the Tools; Obtaining Anaconda; Installing Anaconda; Running Jupyter Notebook for Mac; Running Jupyter Notebook for Windows; Creating a New Notebook; Naming the Notebook |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Adding and Removing CellsRunning a Cell; Restarting the Kernel; Exporting Your Notebook; Getting Help; Summary; Chapter 2 Extending Python Using NumPy; What Is NumPy?; Creating NumPy Arrays; Array Indexing; Boolean Indexing; Slicing Arrays; NumPy Slice Is a Reference; Reshaping Arrays; Array Math; Dot Product; Matrix; Cumulative Sum; NumPy Sorting; Array Assignment; Copying by Reference; Copying by View (Shallow Copy); Copying by Value (Deep Copy); Summary; Chapter 3 Manipulating Tabular Data Using Pandas; What Is Pandas?; Pandas Series; Creating a Series Using a Specified Index |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Accessing Elements in a SeriesSpecifying a Datetime Range as the Index of a Series; Date Ranges; Pandas DataFrame; Creating a DataFrame; Specifying the Index in a DataFrame; Generating Descriptive Statistics on the DataFrame; Extracting from DataFrames; Selecting the First and Last Five Rows; Selecting a Specific Column in a DataFrame; Slicing Based on Row Number; Slicing Based on Row and Column Numbers; Slicing Based on Labels; Selecting a Single Cell in a DataFrame; Selecting Based on Cell Value; Transforming DataFrames; Checking to See If a Result Is a DataFrame or Series |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Sorting Data in a DataFrameSorting by Index; Sorting by Value; Applying Functions to a DataFrame; Adding and Removing Rows and Columns in a DataFrame; Adding a Column; Removing Rows; Removing Columns; Generating a Crosstab; Summary; Chapter 4 Data Visualization Using matplotlib; What Is matplotlib?; Plotting Line Charts; Adding Title and Labels; Styling; Plotting Multiple Lines in the Same Chart; Adding a Legend; Plotting Bar Charts; Adding Another Bar to the Chart; Changing the Tick Marks; Plotting Pie Charts; Exploding the Slices; Displaying Custom Colors; Rotating the Pie Chart |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Displaying a LegendSaving the Chart; Plotting Scatter Plots; Combining Plots; Subplots; Plotting Using Seaborn; Displaying Categorical Plots; Displaying Lmplots; Displaying Swarmplots; Summary; Chapter 5 Getting Started with Scikit-learn for Machine Learning; Introduction to Scikit-learn; Getting Datasets; Using the Scikit-learn Dataset; Using the Kaggle Dataset; Using the UCI (University of California, Irvine) Machine Learning Repository; Generating Your Own Dataset; Linearly Distributed Dataset; Clustered Dataset; Clustered Dataset Distributed in Circular Fashion |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart-it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. - Python data science-manipulating data and data visualization - Data cleansing - Understanding Machine learning algorithms - Supervised learning algorithms - Unsupervised learning algorithms - Deploying machine learning models Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level. |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Programming Languages |
| -- | Python. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1002/9781119557500 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Indianapolis, IN : |
| -- | Wiley, |
| -- | 2019. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 588 0# - | |
| -- | Online resource; title from PDF title page (EBSCO, viewed April 9, 2019) |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Python (Computer program language) |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | COMPUTERS |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| -- | (OCoLC)fst01004795 |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Python (Computer program language) |
| -- | (OCoLC)fst01084736 |
| 994 ## - | |
| -- | 92 |
| -- | DG1 |
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