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" Mastering machine learning with Python in six steps : "
Manohar Swamynathan.
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BL
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| Record Number
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851304
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| Main Entry
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Swamynathan, Manohar
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| Title & Author
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Mastering machine learning with Python in six steps : : a practical implementation guide to predictive data analytics Using Python /\ Manohar Swamynathan.
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| Edition Statement
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2nd editon
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| Publication Statement
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Berkeley, CA :: Apress L.P.,, 2019.
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| Page. NO
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1 online resource (469 pages)
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| ISBN
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148424947X
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: 9781484249475
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9781484249468
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| Contents
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Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Step 1: Getting Started in Python 3; The Best Things in Life Are Free; The Rising Star; Choosing Python 2.x or Python 3.x; Windows; OSX; Graphical Installer; Command Line Installer; Linux; From Official Website; Running Python; Key Concepts; Python Identifiers; Keywords; My First Python Program; Code Blocks; Indentations; Suites; Basic Object Types; When to Use List, Tuple, Set, or Dictionary; Comments in Python; Multiline Statements; Multiple Statements on a Single Line
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Basic Operators; Arithmetic Operators; Comparison or Relational Operators; Assignment Operators; Bitwise Operators; Logical Operators; Membership Operators; Identity Operators; Control Structures; Selections; Iterations; Lists; Tuples; Sets; Changing Sets in Python; Removing Items from Sets; Set Operations; Set Unions; Set Intersections; Set Difference; Set Symmetric Difference; Basic Operations; Dictionary; User-Defined Functions; Defining a Function; The Scope of Variables; Default Argument; Variable Length Arguments; Modules; File Input/Output; Opening a File; Exception Handling; Summary
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Basic Statistics SummaryViewing Data; Basic Operations; Merge/Join; Join; Grouping; Pivot Tables; Matplotlib; Using Global Functions; Customizing Labels; Object-Oriented; Line Plots Using ax.plot(); Multiple Lines on the Same Axis; Multiple Lines on Different Axis; Control the Line Style and Marker Style; Line Style Reference; Marker Reference; Colormaps Reference; Bar Plots Using ax.bar(); Horizontal Bar Charts Using ax.barh(); Side by Side Bar Chart; Stacked Bar Example Code; Pie Chart Using ax.pie(); Example Code for Grid Creation; Plotting Defaults; Machine Learning Core Libraries
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Chapter 2: Step 2: Introduction to Machine Learning; History and Evolution; Artificial Intelligence Evolution; Different Forms; Statistics; Frequentist; Bayesian; Regression; Data Mining; Data Analytics; Descriptive Analytics; Diagnostic Analytics; Predictive Analytics; Prescriptive Analytics; Data Science; Statistics vs. Data Mining vs. Data Analytics vs. Data Science; Machine Learning Categories; Supervised Learning; Unsupervised Learning; Reinforcement Learning; Frameworks for Building ML Systems; Knowledge Discovery in Databases; Selection; Preprocessing; Transformation; Data Mining
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Interpretation / EvaluationCross-Industry Standard Process for Data Mining; Phase 1: Business Understanding; Phase 2: Data Understanding; Phase 3: Data Preparation; Phase 4: Modeling; Phase 5: Evaluation; Phase 6: Deployment; SEMMA (Sample, Explore, Modify, Model, Assess); Sample; Explore; Modify; Model; Assess; Machine Learning Python Packages; Data Analysis Packages; NumPy; Array; Creating NumPy Array; Data Types; Array Indexing; Field Access; Basic Slicing; Advanced Indexing; Array Math; Broadcasting; Pandas; Data Structures; Series; DataFrame; Reading and Writing Data
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| Abstract
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Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version's approach is based on the "six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You'll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You'll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you'll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.
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| Subject
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Machine learning.
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| Subject
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Python (Computer program language)
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Machine learning.
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Python (Computer program language)
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| Dewey Classification
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005.13/3
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| LC Classification
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QA76.73.P98
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