Python machine learning
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...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Indianapolis, Indiana :
Wiley
[2019]
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630508706719 |
Tabla de Contenidos:
- 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
- Adding and Removing Cells
- Running 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
- Accessing Elements in a Series
- Specifying 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
- Sorting Data in a DataFrame
- Sorting 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
- Displaying a Legend
- Saving 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
- Getting Started with Scikit-learn
- Using the LinearRegression Class for Fitting the Model
- Making Predictions
- Plotting the Linear Regression Line
- Getting the Gradient and Intercept of the Linear Regression Line
- Examining the Performance of the Model by Calculating the Residual Sum of Squares
- Evaluating the Model Using a Test Dataset
- Persisting the Model
- Data Cleansing
- Cleaning Rows with NaNs
- Replacing NaN with the Mean of the Column
- Removing Rows
- Removing Duplicate Rows
- Normalizing Columns
- Removing Outliers
- Tukey Fences.
- Z-Score
- Summary
- Chapter 6 Supervised Learning-Linear Regression
- Types of Linear Regression
- Linear Regression
- Using the Boston Dataset
- Data Cleansing
- Feature Selection
- Multiple Regression
- Training the Model
- Getting the Intercept and Coefficients
- Plotting the 3D Hyperplane
- Polynomial Regression
- Formula for Polynomial Regression
- Polynomial Regression in Scikit-learn
- Understanding Bias and Variance
- Using Polynomial Multiple Regression on the Boston Dataset
- Plotting the 3D Hyperplane
- Summary
- Chapter 7 Supervised Learning-Classification Using Logistic Regression
- What Is Logistic Regression?
- Understanding Odds
- Logit Function
- Sigmoid Curve
- Using the Breast Cancer Wisconsin (Diagnostic) Data Set
- Examining the Relationship Between Features
- Plotting the Features in 2D
- Plotting in 3D
- Training Using One Feature
- Finding the Intercept and Coefficient
- Plotting the Sigmoid Curve
- Making Predictions
- Training the Model Using All Features
- Testing the Model
- Getting the Confusion Matrix
- Computing Accuracy, Recall, Precision, and Other Metrics
- Receiver Operating Characteristic (ROC) Curve
- Plotting the ROC and Finding the Area Under the Curve (AUC)
- Summary
- Chapter 8 Supervised Learning-Classification Using Support Vector Machines
- What Is a Support Vector Machine?
- Maximum Separability
- Support Vectors
- Formula for the Hyperplane
- Using Scikit-learn for SVM
- Plotting the Hyperplane and the Margins
- Making Predictions
- Kernel Trick
- Adding a Third Dimension
- Plotting the 3D Hyperplane
- Types of Kernels
- C
- Radial Basis Function (RBF) Kernel
- Gamma
- Polynomial Kernel
- Using SVM for Real-Life Problems
- Summary
- Chapter 9 Supervised Learning-Classification Using K-Nearest Neighbors (KNN)
- What Is K-Nearest Neighbors?.
- Implementing KNN in Python
- Plotting the Points
- Calculating the Distance Between the Points
- Implementing KNN
- Making Predictions
- Visualizing Different Values of K
- Using Scikit-Learn's KNeighborsClassifier Class for KNN
- Exploring Different Values of K
- Cross-Validation
- Parameter-Tuning K
- Finding the Optimal K
- Summary
- Chapter 10 Unsupervised Learning-Clustering Using K-Means
- What Is Unsupervised Learning?
- Unsupervised Learning Using K-Means
- How Clustering in K-Means Works
- Implementing K-Means in Python
- Using K-Means in Scikit-learn
- Evaluating Cluster Size Using the Silhouette Coefficient
- Calculating the Silhouette Coefficient
- Finding the Optimal K
- Using K-Means to Solve Real-Life Problems
- Importing the Data
- Cleaning the Data
- Plotting the Scatter Plot
- Clustering Using K-Means
- Finding the Optimal Size Classes
- Summary
- Chapter 11 Using Azure Machine Learning Studio
- What Is Microsoft Azure Machine Learning Studio?
- An Example Using the Titanic Experiment
- Using Microsoft Azure Machine Learning Studio
- Uploading Your Dataset
- Creating an Experiment
- Filtering the Data and Making Fields Categorical
- Removing the Missing Data
- Splitting the Data for Training and Testing
- Training a Model
- Comparing Against Other Algorithms
- Evaluating Machine Learning Algorithms
- Publishing the Learning Model as a Web Service
- Publishing the Experiment
- Testing the Web Service
- Programmatically Accessing the Web Service
- Summary
- Chapter 12 Deploying Machine Learning Models
- Deploying ML
- Case Study
- Loading the Data
- Cleaning the Data
- Examining the Correlation Between the Features
- Plotting the Correlation Between Features
- Evaluating the Algorithms
- Logistic Regression
- K-Nearest Neighbors
- Support Vector Machines.
- Selecting the Best Performing Algorithm
- Training and Saving the Model
- Deploying the Model
- Testing the Model
- Creating the Client Application to Use the Model
- Summary
- Index
- EULA.
