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
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.