Data Science and Machine Learning

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