• New
- zdjęcie 1
- zdjęcie 2
- zdjęcie 3

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning, 2nd Edition - K. Gallatin

Index: KSZ-25310 EAN: 9788328908116
Ask a question

The second edition of the book Machine Learning in Python , contains over 200 proven recipes , which are based on the latest editions of Python libraries. They contain ready-made codes , which can be adapted to your needs. The book presents ready-made examples, concerning working with data in many formats, databases and data stores, as well as many other tips, which can be useful when solving a spectrum of problems, from preparing and loading data, to training models and using neural networks. The publication is suitable for people who want to implement machine learning algorithms in practice.

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning,...
€13.23
€12.60 tax excl.
Check quantity
Available
Shipping in 24 hours
Free shipping
30 days to return
PayPal payment
Check quantity
Manufacturer: Helion

‌

Product description: Machine learning in Python. Recipes. From data preparation to deep learning. 2nd edition - Kyle Gallatin, Chris Albon

The second edition of the book Machine Learning in Python , contains over 200 proven recipes , which are based on the latest editions of Python libraries. They contain ready-made codes , which can be adapted to your needs. The book presents ready-made examples on working with data in many formats, databases and data stores, as well as many other tips that can be useful when solving a spectrum of problems, from preparing and loading data, to training models and using neural networks . The publication is suitable for people who want to implement machine learning algorithms in practice.

We also offer other books for programmers - check them out!

Machine Learning in Python. Recipes. From Data Preparation to Deep Learning. 2nd Edition - K. Gallatin

Machine Learning in Python. Recipes. From Data Preparation to Deep Learning. 2nd Edition - K. Gallatin.

Machine Learning in Python. Recipes. From Data Preparation to Deep Learning. 2nd Edition - K. Gallatin

Machine Learning in Python. Recipes. From Data Preparation to Deep Learning.

A few words about the authors

Kyle Gallatin is a software engineer who works on Etsy’s machine learning platform and has also worked as a data scientist, data analyst, and machine learning engineer.

Dr. Chris Albon has been a data scientist and political scientist for many years. He currently works for Devoted Health and was also the lead data scientist at the Kenyan startup BRCK.

Machine Learning - Recipes

Machine learning is one of the most interesting and dynamically developing areas of information technology. The book contains information on working with data in many formats, databases and data stores. It also discusses dimensionality reduction techniques and methods for evaluating and selecting models. The publication contains recipes for such topics as linear and logistic regression, decision trees and random forests, as well as k-nearest neighbor algorithms, support vector machines (SVM), naive Bayes classification and clustering.

Information about the book

  • Original title: Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning, 2nd Edition
  • Authors: Kyle Gallatin, Dr. Chris Albon
  • Translation: Robert Górczyński
  • ISBN: 978-83-289-0811-6
  • Release year: 2024
  • Size: 165 x 235 mm
  • Binding: softcover
  • Number of pages: 398
  • Publisher: Helion S.A.
Machine Learning in Python. Recipes. From Data Preparation to Deep Learning. 2nd Edition - K. Gallatin

The book is intended for programmers.

Contents

Introduction

1. Vector, matrix and array

  1. 1.0 Introduction
  2. 1.1. Creating a Vector
  3. 1.2. Creating a matrix
  4. 1.3. Creating a Sparse Matrix
  5. 1.4. NumPy Array Preallocation
  6. 1.5. Downloading items
  7. 1.6. Describing Matrices
  8. 1.7. Performing Operations on All Elements
  9. 1.8. Finding the Maximum and Minimum Values
  10. 1.9. Calculating Mean, Variance, and Standard Deviation
  11. 1.10. Changing the shape of the board
  12. 1.11. Transposing a Vector or Matrix
  13. 1.12. Matrix Flattening
  14. 1.13. Finding the Rank of a Matrix
  15. 1.14. Getting the diagonal of a matrix
  16. 1.15. Calculating the trace of a matrix
  17. 1.16. Calculating the dot product
  18. 1.17. Adding and Subtracting Matrices
  19. 1.18. Matrix Multiplication
  20. 1.19. Inverting a Matrix
  21. 1.20. Random Number Generation

2. Loading data

  1. 2.0 Introduction
  2. 2.1. Loading a Sample Dataset
  3. 2.2. Creating a Simulated Data Set
  4. 2.3. Loading a CSV file
  5. 2.4. Loading an Excel file
  6. 2.5. Loading a JSON file
  7. 2.6. Loading a Parquet file
  8. 2.7. Loading an Avro file
  9. 2.8. Querying an SQLite Database
  10. 2.9. Querying a Remote SQL Database
  11. 2.10. Loading data from Google Sheets
  12. 2.11. Loading data from S3 bucket
  13. 2.12. Loading unstructured data

3. Data preparation

  1. 3.0 Introduction
  2. 3.1. Creating a Data Frame
  3. 3.2. Describing Data
  4. 3.3. Moving around the data frame
  5. 3.4. Fetching rows based on certain conditions
  6. 3.5. Sorting values
  7. 3.6. Replacing Values
  8. 3.7. Changing the column name
  9. 3.8. Finding the Min, Max, Sum, Average, and Count of Items in a Column
  10. 3.9. Finding Unique Values
  11. 3.10. Handling Missing Values
  12. 3.11. Deleting Columns
  13. 3.12. Deleting a row
  14. 3.13. Removing duplicate rows
  15. 3.14. Grouping Rows by Value
  16. 3.15. Grouping Rows by Time
  17. 3.16. Aggregating operations and statistics
  18. 3.17. Iterating Through a Column
  19. 3.18. Calling a function for all elements of a column
  20. 3.19. Calling a function for a group
  21. 3.20. Concatenation of DataFrame objects
  22. 3.21. Combining DataFrame objects

4. Handling numeric data

  1. 4.0 Introduction
  2. 4.1. Rescaling a feature
  3. 4.2. Standardizing a feature
  4. 4.3. Normalizing observations
  5. 4.4. Generating Polynomial Features and Interactions
  6. 4.5. Feature Transformation
  7. 4.6. Outlier Detection
  8. 4.7. Handling Outliers
  9. 4.8. Feature discretization
  10. 4.9. Grouping observations using a cluster
  11. 4.10. Removing observations with missing values
  12. 4.11. Filling in missing values

5. Handling categorization data

  1. 5.0 Introduction
  2. 5.1. Coding of Nominal Categorizing Features
  3. 5.2. Coding of ordinal categorizing features
  4. 5.3. Coding of feature dictionaries
  5. 5.4. Inserting Missing Class Values
  6. 5.5. Handling Unbalanced Classes

6. Text support

  1. 6.0. Introduction
  2. 6.1. Text Cleanup
  3. 6.2. Processing and cleaning HTML data
  4. 6.3. Removing a punctuation mark
  5. 6.4. Text Tokenization
  6. 6.5. Removing words of little importance
  7. 6.6. Word stemming
  8. 6.7. Parts of Speech Labeling
  9. 6.8. Named Entity Recognition
  10. 6.9. Text Encoding Using Bag of Words Model
  11. 6.10. Determining the weight of words
  12. 6.11. Using Text Vectors to Calculate Text Similarity in a Search Query
  13. 6.12. Using the Sentiment Analysis Classifier

7. Date and time support

  1. 7.0. Introduction
  2. 7.1. Converting a Text String to a Date
  3. 7.2. Time Zone Support
  4. 7.3. Getting the date and time
  5. 7.4. Splitting Date Data into Multiple Features
  6. 7.5. Calculating the difference between dates
  7. 7.6. Day of the week coding
  8. 7.7. Creating a Time-Delayed Feature
  9. 7.8. Using Elapsed Time Windows
  10. 7.9. Handling Missing Data in Data Series Containing Date and Time Values

8. Image handling

  1. 8.0. Introduction
  2. 8.1. Loading an image
  3. 8.2. Saving an image
  4. 8.3. Resize the image
  5. 8.4. Cropping an image
  6. 8.5. Blurring the image
  7. 8.6. Sharpening an image
  8. 8.7. Increasing Contrast
  9. 8.8. Isolating Colors
  10. 8.9. Image Thresholding
  11. 8.10. Removing the background of an image
  12. 8.11. Edge Detection
  13. 8.12. Detecting corners in an image
  14. 8.13. Creating Features in Machine Learning
  15. 8.14. Using Color Histogram as Feature
  16. 8.15. Using Trained Embeddings as Features
  17. 8.16. Object Detection with OpenCV
  18. 8.17. Classifying Images with PyTorch

9. Dimensionality Reduction Using Feature Extraction

  1. 9.0. Introduction
  2. 9.1. Reducing Features Using Principal Components
  3. 9.2. Reducing Features When Data Are Linearly Inseparable
  4. 9.3. Reducing Features by Maximizing Class Disjointness
  5. 9.4. Reducing Features Using Matrix Decomposition
  6. 9.5. Reducing Features in Sparse Data

10. Dimensionality Reduction Using Feature Selection

  1. 10.0. Introduction
  2. 10.1. Thresholding the variance of a numerical feature
  3. 10.2. Binary Feature Variance Thresholding
  4. 10.3. Handling Highly Correlated Features
  5. 10.4. Removing features that are not relevant for classification
  6. 10.5. Recursive feature elimination

11. Model evaluation

  1. 11.0. Introduction
  2. 11.1. Cross-validation models
  3. 11.2. Creating a Base Regression Model
  4. 11.3. Creating a Base Classification Model
  5. 11.4. Evaluating Binary Classifier Predictions
  6. 11.5. Evaluation of binary classifier thresholding
  7. 11.6. Evaluating the predictions of a multi-class classifier
  8. 11.7. Classifier Performance Visualization
  9. 11.8. Evaluation of the regression model
  10. 11.9. Clustering model evaluation
  11. 11.10. Defining Custom Model Evaluation Factors
  12. 11.11. Visualization of the effect of training set size
  13. 11.12. Creating a text report on the evaluation factor
  14. 11.13. Visualization of the effect of changing the value of hyperparameters

12. Model selection

  1. 12.0. Introduction
  2. 12.1. Selecting the best models using an exhaustive search
  3. 12.2. Selecting the best models using random search
  4. 12.3. Selecting the best models from multiple machine learning algorithms
  5. 12.4. Selecting the best models at the data preparation stage
  6. 12.5. Accelerating Model Selection with Parallelism
  7. 12.6. Accelerating Model Selection Using Algorithm-Specific Methods
  8. 12.7. Performance Evaluation After Model Selection

13. Linear regression

  1. 13.0. Introduction
  2. 13.1. Drawing lines
  3. 13.2. Handling Interaction Influence
  4. 13.3. Determining the nonlinear relationship
  5. 13.4. Reducing Variance with Regularization
  6. 13.5. Reducing Features Using LASSO Regression

14. Trees and forests

  1. 14.0. Introduction
  2. 14.1. Training a Decision Tree Classifier
  3. 14.2. Training a Decision Tree Regressor
  4. 14.3. Decision Tree Model Visualization
  5. 14.4. Training a Random Forest Classifier
  6. 14.5. Training a Random Forest Regressor
  7. 14.6. Evaluating Random Forest with Out-of-Bag Error Estimator
  8. 14.7. Identifying Important Features in Random Forests
  9. 14.8. Selecting Important Features in Random Forest
  10. 14.9. Handling Unbalanced Classes
  11. 14.10. Controlling the size of the tree
  12. 14.11. Improving performance with boost
  13. 14.12. Training the XGBoost model
  14. 14.13 Improving Real-Time Performance with LightGBM

15. k nearest neighbors algorithm

  1. 15.0. Introduction
  2. 15.1. Finding the nearest neighbors of an observation
  3. 15.2. Creating a k-nearest neighbor classifier
  4. 15.3. Determining the best neighborhood size
  5. 15.4. Creating a Radius-Based Nearest Neighbor Classifier
  6. 15.5. Finding Approximate Nearest Neighbors
  7. 15.6. Estimation of approximate nearest neighbors

16. Logistic regression

  1. 16.0. Introduction
  2. 16.1. Training a Binary Classifier
  3. 16.2. Training a Multi-Class Classifier
  4. 16.3. Variance Reduction Through Regularization
  5. 16.4. Training a Classifier on Very Large Data
  6. 16.5. Handling Unbalanced Classes

17. Support Vector Machine

  1. 17.0. Introduction
  2. 17.1. Training a Linear Classifier
  3. 17.2. Handling Linearly Inseparable Classes Using Kernel Functions
  4. 17.3. Determining the Projected Probability
  5. 17.4. Identification of support vectors
  6. 17.5. Handling Unbalanced Classes

18. Naive Bayes Classifier

  1. 18.0. Introduction
  2. 18.1. Training a Classifier for Continuous Features
  3. 18.2. Training a Classifier for Discrete or Numerical Features
  4. 18.3. Training a Naive Bayes Classifier for Binary Features
  5. 18.4. Calibrating the Forecast Probability

19. Clustering

  1. 19.0. Introduction
  2. 19.1. Clustering with k means
  3. 19.2. Accelerating clustering with k-means
  4. 19.3. Clustering using the meanshift algorithm
  5. 19.4. Clustering using the DBSCAN algorithm
  6. 19.5. Clustering by Hierarchical Linking

20. Tensors in PyTorch

  1. 20.0. Introduction
  2. 20.1. Creating a Tensor
  3. 20.2. Creating a Tensor from NumPy
  4. 20.3. Creation of a sparse tensor
  5. 20.4. Selecting Tensor Elements
  6. 20.5. Describing a Tensor
  7. 20.6. Performing operations on tensor elements
  8. 20.7. Finding the minimum and maximum values
  9. 20.8. Changing the shape of a tensor
  10. 20.9. Tensor Transpose
  11. 20.10. Tensor Flattening
  12. 20.11. Calculating the dot product
  13. 20.12. Tensor Multiplication

21. Neural networks

  1. 21.0. Introduction
  2. 21.1. Using the PyTorch Framework's Autograd Engine
  3. 21.2. Preparing data for a neural network
  4. 21.3. Designing a Neural Network
  5. 21.4. Training a Binary Classifier
  6. 21.5. Training a Multi-Class Classifier
  7. 21.6. Training the Regressor
  8. 21.7. Generating Forecasts
  9. 21.8. Training History Visualization
  10. 21.9. Reducing Overfitting with Weight Regularization
  11. 21.10. Overfitting Reduction Using Early Termination Technique
  12. 21.11. Reducing Overfitting Using the Dropout Technique
  13. 21.12. Saving the progress of the learning model
  14. 21.13. Fine-tuning a neural network
  15. 21.14. Neural network visualization

22. Neural Networks for Unstructured Data

  1. 22.0 Introduction
  2. 22.1. Training a Neural Network for Image Classification
  3. 22.2. Training a Neural Network for Text Classification
  4. 22.3. Fine-tuning the Trained Model for Image Classification
  5. 22.4. Fine-tuning the Trained Model for Text Classification

23. Saving, Loading, and Sharing Trained Models

  2. 23.0. Introduction
  3. 23.1. Saving and Loading a Scikit-Learn Model
  4. 23.2. Saving and Loading a TensorFlow Model
  5. 23.3. Saving and Loading a PyTorch Model
  6. 23.4. Sharing scikit-learn models
  7. 23.5. Sharing TensorFlow Models
  8. 23.6. Sharing PyTorch models with Seldon

Book - Author Kyle Gallatin, Chris Albon
Book - ISBN 978-83-289-0811-6
Book - Binding miękka
Book - Publisher Helion SA
Język programowania Programowanie
Język programowania Python
Package width 16.5 cm
Package height 23.5 cm
Package depth 2 cm
Package weight 0.623 kg
Send us your question

Be the first to ask a question about this product!

Customers who bought this product also bought:

Products from the same category: