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