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_a019364424 _2Uk |
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_a9781119545675 _q(electronic bk.) |
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| 020 |
_a9781119557500 _q(electronic bk. ; _qoBook) |
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_a111955750X _q(electronic bk. ; _qoBook) |
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| 020 | _z9781119545637 | ||
| 020 | _z1119545633 | ||
| 037 |
_a9781119545675 _bWiley |
||
| 040 | _cCUS | ||
| 072 | 7 |
_aCOM _x051360 _2bisacsh |
|
| 100 | 1 |
_aLee, Wei-Meng, _eauthor. |
|
| 245 | 1 | 0 |
_aPython machine learning / _cWei-Meng Lee. |
| 260 | 1 |
_aIndianapolis, IN : _bWiley, _c2019. |
|
| 300 | _a1 online resource | ||
| 505 | 0 | _aCover; 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 | _aAdding 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 | _aAccessing 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 | _aSorting 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 | _aDisplaying 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 | _aPython 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 | 0 | _aMachine learning. | |
| 650 | 0 | _aPython (Computer program language) | |
| 650 | 7 |
_aCOMPUTERS _xProgramming Languages _xPython. _2bisacsh |
|
| 650 | 7 |
_aMachine learning. _2fast _0(OCoLC)fst01004795 |
|
| 650 | 7 |
_aPython (Computer program language) _2fast _0(OCoLC)fst01084736 |
|
| 856 | 4 | 0 |
_uhttps://doi.org/10.1002/9781119557500 _zWiley Online Library |
| 942 | _cEBK | ||
| 999 |
_c208849 _d208849 |
||