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Mastering machine learning with Python in six steps : a practical implementation guide to predictive data analytics using Python /
Your practical guide to moving from novice to master in machine learning with Python 3 in six steps, this book covers fundamental to advanced topics gradually helping beginners become worthy practitioners. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[California] :
Apress,
[2019]
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- Basic OperatorsArithmetic 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
- Chapter 2: Step 2: Introduction to Machine LearningHistory 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
- 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
- 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