Luca Massaron

Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments.

Machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. It explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible, specifies how to do practical things and more!

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it.

Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design… Visualizza altro

Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Meno dettagli

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries.

The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed… Visualizza altro

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries.

The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. Meno dettagli

This technical report details the author’s approach in the NIPS4B competition which led to a
final result of an area under the ROC curve of 0.89575 in the public leaderboard and 0.89041
in the private one. The described approach involved building an ensemble of generalized
linear models, such as a logistic/hinge regression as provided by the Vowpal Wabbit, an open source learning system library and program based on stochastic gradient descent optimization, blended with boosted trees… Visualizza altro

This technical report details the author’s approach in the NIPS4B competition which led to a
final result of an area under the ROC curve of 0.89575 in the public leaderboard and 0.89041
in the private one. The described approach involved building an ensemble of generalized
linear models, such as a logistic/hinge regression as provided by the Vowpal Wabbit, an open source learning system library and program based on stochastic gradient descent optimization, blended with boosted trees ensembles provided by Scikit-learn library in Python. Meno dettagli

A poster presenting a data mining churn modelling analysis in the natural gas and power distribution system, built using Clementine software.

An article presenting the usage and application of importance-performance analysis in customer satisfaction surveys.

A path analysis of clusters of young people in Internet, based on behavioural Internet audience panel data.

An analysis of Windows OS software usage based on behavioural panel data.