Data Science and Machine Learning: From Data to Knowledge

Michele di Nuzzo

dec. 2021 · Michele di Nuzzo

Despre această carte electronică

Extracting knowledge from information through data analysis: the data scientist has been called the most attractive profession of the 21st century. Analyze the relationships between data, discover new information and, thanks to machine learning, exploit the immense potential hidden in it by building predictive models. In this book, we illustrate methods to analyze and manipulate data, and Machine Learning and Deep Learning algorithms to predict information, moving from theoretical knowledge to practical applications with statistical software R, through extensive practical examples

What you will learn

Mathematics and algebra for machine learning

Statistics and probability for data science

Use of the statistical software R and R-Studio

Data preparation and feature engineering

Design and validate machine learning algorithms

Regression, classification and clustering algorithms

Making predictions based on time series

The models of neural networks and deep learning

Data visualization & data storytelling

Who this book is for

This book is for anyone who wants to learn how to manipulate and analyze data by drawing new knowledge from it. If you are an IT manager or an analyst who wants to enter the world of Data Science and Big Data, if you are a developer who wants to know the new trends in the field of Artificial Intelligence or you are simply curious about this world, then this book is for you.

Contents

Data science and analysis models

Big data management

Univariate and multivariate analysis, probability and hypothesis testing

Exploring and visualizing data

Data preparation and data cleaning

Supervised learning: classification and regression

Unsupervised learning: clustering and dimensionality reduction

Semi-Supervised Learning

Association algorithms and time series analysis

Validation measures and algorithms optimization

Neural networks and Deep Learning

Convolutional networks for image recognition

Recurrent Networks and LSMT for sequences

Encoders for feature selection

Generative algorithms

Despre autor

Michele di Nuzzo is a computer engineer who has been working with data analysis for more than fifteen years. Expert in multidimensional database, he has participated on several projects on data warehousing, business intelligence, analytical tools, ad-hoc analysis, predictive models, data science and strategic consulting. In his career he has followed the entire data life cycle, from the ETL activity to the projects in large-scale distribution, retail, e-commerce, etc. He also dealt with Project Management, in which he earned several masters.