Python: Real World Machine Learning
Take your Python Machine learning skills to the next level
1st Edition
Published on 15. April 2025
956 pages
978-1-78712-067-9 (ISBN)
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Persons
Joshi Prateek :
Prateek Joshi is the founder of Plutoshift and a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. Apart from Artificial Intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make Artificial Intelligence accessible to everyone so that it can impact billions of people around the world.Sjardin Bastiaan :
Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale.Massaron Luca Massaron :
Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.Boschetti Alberto :
Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.Hearty John :
John Hearty is a Manager of Data Science team with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually, John struck out on his own as a consultant offering a comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favorite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfill a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation
Prateek Joshi is the founder of Plutoshift and a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. Apart from Artificial Intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make Artificial Intelligence accessible to everyone so that it can impact billions of people around the world.Sjardin Bastiaan :
Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale.Massaron Luca Massaron :
Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.Boschetti Alberto :
Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.Hearty John :
John Hearty is a Manager of Data Science team with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually, John struck out on his own as a consultant offering a comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favorite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfill a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation
Content
- Cover
- Copyright
- Credits
- Preface
- Table of Contents
- Module 1: Python Machine Learning Cookbook
- Chapter 1: The Realm of Supervised Learning
- Introduction
- Preprocessing data using different techniques
- Label encoding
- Building a linear regressor
- Computing regression accuracy
- Achieving model persistence
- Building a ridge regressor
- Building a polynomial regressor
- Estimating housing prices
- Computing the relative importance of features
- Estimating bicycle demand distribution
- Chapter 2: Constructing a Classifier
- Introduction
- Building a simple classifier
- Building a logistic regression classifier
- Building a Naive Bayes classifier
- Splitting the dataset for training and testing
- Evaluating the accuracy using cross-validation
- Visualizing the confusion matrix
- Extracting the performance report
- Evaluating cars based on their characteristics
- Extracting validation curves
- Extracting learning curves
- Estimating the income bracket
- Chapter 3: Predictive Modeling
- Introduction
- Building a linear classifier using Support Vector Machine (SVMs)
- Building a nonlinear classifier using SVMs
- Tackling class imbalance
- Extracting confidence measurements
- Finding optimal hyperparameters
- Building an event predictor
- Estimating traffic
- Chapter 4: Clustering with Unsupervised Learning
- Introduction
- Clustering data using the k-means algorithm
- Compressing an image using vector quantization
- Building a Mean Shift clustering model
- Grouping data using agglomerative clustering
- Evaluating the performance of clustering algorithms
- Automatically estimating the number of clusters using DBSCAN algorithm
- Finding patterns in stock market data
- Building a customer segmentation model
- Chapter 5: Building Recommendation Engines
- Introduction
- Building function compositions for data processing
- Building machine learning pipelines
- Finding the nearest neighbors
- Constructing a k-nearest neighbors classifier
- Constructing a k-nearest neighbors regressor
- Computing the Euclidean distance score
- Computing the Pearson correlation score
- Finding similar users in the dataset
- Generating movie recommendations
- Chapter 6: Analyzing Text Data
- Introduction
- Preprocessing data using tokenization
- Stemming text data
- Converting text to its base form using lemmatization
- Dividing text using chunking
- Building a bag-of-words model
- Building a text classifier
- Identifying the gender
- Analyzing the sentiment of a sentence
- Identifying patterns in text using topic modeling
- Chapter 7: Speech Recognition
- Introduction
- Reading and plotting audio data
- Transforming audio signals into the frequency domain
- Generating audio signals with custom parameters
- Synthesizing music
- Extracting frequency domain features
- Building Hidden Markov Models
- Building a speech recognizer
- Chapter 8: Dissecting Time Series and Sequential Data
- Introduction
- Transforming data into the time series format
- Slicing time series data
- Operating on time series data
- Extracting statistics from time series data
- Building Hidden Markov Models for sequential data
- Building Conditional Random Fields for sequential text data
- Analyzing stock market data using Hidden Markov Models
- Chapter 9: Image Content Analysis
- Introduction
- Operating on images using OpenCV-Python
- Detecting edges
- Histogram equalization
- Detecting corners
- Detecting SIFT feature points
- Building a Star feature detector
- Creating features using visual codebook and vector quantization
- Training an image classifier using Extremely Random Forests
- Building an object recognizer
- Chapter 10: Biometric Face Recognition
- Introduction
- Capturing and processing video from a webcam
- Building a face detector using Haar cascades
- Building eye and nose detectors
- Performing Principal Components Analysis
- Performing Kernel Principal Components Analysis
- Performing blind source separation
- Building a face recognizer using Local Binary Patterns Histogram
- Chapter 11: Deep Neural Networks
- Introduction
- Building a perceptron
- Building a single layer neural network
- Building a deep neural network
- Creating a vector quantizer
- Building a recurrent neural network for sequential data analysis
- Visualizing the characters in an optical character recognition database
- Building an optical character recognizer using neural networks
- Chapter 12: Visualizing Data
- Introduction
- Plotting 3D scatter plots
- Plotting bubble plots
- Animating bubble plots
- Drawing pie charts
- Plotting date-formatted time series data
- Plotting histograms
- Visualizing heat maps
- Animating dynamic signals
- Module 2: Advanced Machine Learning with Python
- Chapter 1: Unsupervised Machine Learning
- Principal component analysis
- Introducing k-means clustering
- Self-organizing maps
- Further reading
- Summary
- Chapter 2: Deep Belief Networks
- Neural networks - a primer
- Restricted Boltzmann Machine
- Deep belief networks
- Further reading
- Summary
- Chapter 3: Stacked Denoising Autoencoders
- Autoencoders
- Stacked Denoising Autoencoders
- Further reading
- Summary
- Chapter 4: Convolutional Neural Networks
- Introducing the CNN
- Further Reading
- Summary
- Chapter 5: Semi-Supervised Learning
- Introduction
- Understanding semi-supervised learning
- Semi-supervised algorithms in action
- Further reading
- Summary
- Chapter 6: Text Feature Engineering
- Introduction
- Text feature engineering
- Further reading
- Summary
- Chapter 7: Feature Engineering Part II
- Introduction
- Creating a feature set
- Feature engineering in practice
- Further reading
- Summary
- Chapter 8: Ensemble Methods
- Introducing ensembles
- Using models in dynamic applications
- Further reading
- Summary
- Chapter 9: Additional Python Machine Learning Tools
- Alternative development tools
- Further reading
- Summary
- Appendix: Chapter Code Requirements
- Module 3: Large Scale Machine Learning with Python
- Chapter 1: First Steps to Scalability
- Explaining scalability in detail
- Python for large scale machine learning
- Python packages
- Summary
- Chapter 2: Scalable Learning in Scikit-learn
- Out-of-core learning
- Streaming data from sources
- Stochastic learning
- Feature management with data streams
- Summary
- Chapter 3: Fast SVM Implementations
- Datasets to experiment with on your own
- Support Vector Machines
- Feature selection by regularization
- Including non-linearity in SGD
- Hyperparameter tuning
- Summary
- Chapter 4: Neural Networks and Deep Learning
- The neural network architecture
- Neural networks and regularization
- Neural networks and hyperparameter optimization
- Neural networks and decision boundaries
- Deep learning at scale with H2O
- Deep learning and unsupervised pretraining
- Deep learning with theanets
- Autoencoders and unsupervised learning
- Summary
- Chapter 5: Deep Learning with TensorFlow
- TensorFlow installation
- Machine learning on TensorFlow with SkFlow
- Keras and TensorFlow installation
- Convolutional Neural Networks in TensorFlow through Keras
- CNN's with an incremental approach
- GPU Computing
- Summary
- Chapter 6: Classification and Regression Trees at Scale
- Bootstrap aggregation
- Random forest and extremely randomized forest
- Fast parameter optimization with randomized search
- CART and boosting
- XGBoost
- Out-of-core CART with H2O
- Summary
- Chapter 7: Unsupervised Learning at Scale
- Unsupervised methods
- Feature decomposition - PCA
- PCA with H2O
- Clustering - K-means
- K-means with H2O
- LDA
- Summary
- Chapter 8: Distributed Environments - Hadoop and Spark
- From a standalone machine to a bunch of nodes
- Setting up the VM
- The Hadoop ecosystem
- Spark
- Summary
- Chapter 9: Practical Machine Learning with Spark
- Setting up the VM for this chapter
- Sharing variables across cluster nodes
- Data preprocessing in Spark
- Machine learning with Spark
- Summary
- Appendix: Introduction to GPUs and Theano
- GPU computing
- Theano - parallel computing on the GPU
- Installing Theano
- Bibliography
