Ciaburro Giuseppe Ciaburro – författare

Extract patterns and knowledge from your data in easy way using MATLAB

About This Book

Get your first steps into machine learning with the help of this easy-to-follow guideLearn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLABUnderstand how your data works and identify hidden layers in the data with the power of machine learning.

Who This Book Is For

This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.

What You Will Learn

Learn the introductory concepts of machine learning.Discover different ways to transform data using SAS XPORT, import and export tools,Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.Learn feature selection and extraction for dimensionality reduction leading to improved performance.

In Detail

MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.

You''ll start by getting your system ready with t he MATLAB environment for machine learning and you''ll see how to easily interact with the Matlab workspace. We''ll then move on to data cleansing, mining and analyzing various data types in machine learning and you''ll see how to display data values on a plot. Next, you''ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.

You''ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you''ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.

At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.

Style and approach

The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Uncover the power of artificial neural networks by implementing them through R code.

About This Book

Develop a strong background in neural networks with R, to implement them in your applicationsBuild smart systems using the power of deep learningReal-world case studies to illustrate the power of neural network models

Who This Book Is For

This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!

What You Will Learn

Set up R packages for neural networks and deep learningUnderstand the core concepts of artificial neural networksUnderstand neurons, perceptrons, bias, weights, and activation functionsImplement supervised and unsupervised machine learning in R for neural networksPredict and classify data automatically using neural networksEvaluate and fine-tune the models you build.

In Detail

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.

This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you''ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.

By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.

Style and approach

A step-by-step guide filled with real-world practical examples.

Build effective regression models in R to extract valuable insights from real data

About This Book

Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing valuesFrom Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in RA complete guide to building effective regression models in R and interpreting results from them to make valuable predictions

Who This Book Is For

This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful

What You Will Learn

Get started with the journey of data science using Simple linear regressionDeal with interaction, collinearity and other problems using multiple linear regressionUnderstand diagnostics and what to do if the assumptions fail with proper analysisLoad your dataset, treat missing values, and plot relationships with exploratory data analysisDevelop a perfect model keeping overfitting, under-fitting, and cross-validation into considerationDeal with classification problems by applying Logistic regressionExplore other regression techniques – Decision trees, Bagging, and Boosting techniquesLearn by getting it all in action with the help of a real world case study.

In Detail

Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables.

This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples.

By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.

Style and approach

An easy-to-follow step by step guide which will help you get to grips with real world application of Regression Analysis with R

Leverage the power of Microsoft Azure Data Factory v2 to build hybrid data solutions

About This Book

Combine the power of Azure Data Factory v2 and SQL Server Integration ServicesDesign and enhance performance and scalability of a modern ETL hybrid solutionInteract with the loaded data in data warehouse and data lake using Power BI

Who This Book Is For

This book is for you if you are a software professional who develops and implements ETL solutions using Microsoft SQL Server or Azure cloud. It will be an added advantage if you are a software engineer, DW/ETL architect, or ETL developer, and know how to create a new ETL implementation or enhance an existing one with ADF or SSIS.

What You Will Learn

Understand the key components of an ETL solution using Azure Data Factory and Integration ServicesDesign the architecture of a modern ETL hybrid solutionImplement ETL solutions for both on-premises and Azure dataImprove the performance and scalability of your ETL solutionGain thorough knowledge of new capabilities and features added to Azure Data Factory and Integration Services

In Detail

ETL is one of the essential techniques in data processing. Given data is everywhere, ETL will always be the vital process to handle data from different sources.

Hands-On Data Warehousing with Azure Data Factory starts with the basic concepts of data warehousing and ETL process. You will learn how Azure Data Factory and SSIS can be used to understand the key components of an ETL solution. You will go through different services offered by Azure that can be used by ADF and SSIS, such as Azure Data Lake Analytics, Machine Learning and Databrick''s Spark with the help of practical examples. You will explore how to design and implement ETL hybrid solutions using different integration services with a step-by-step approach. Once you get to grips with all this, you will use Power BI to interact with data coming from different sources in order to reveal valuable insights.

By the end of this book, you will not only learn how to build your own ETL solutions but also address the key challenges that are faced while building them.

Style and approach

A step-by-step guide to develop data movement code using SSIS, Azure Data Factory, and database stored procedures for implementing intelligent BI solutions.

A practical guide to mastering reinforcement learning algorithms using Keras

Key Features

Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into actionGet to grips with Keras and practice on real-world unstructured datasetsUncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning

Book Description

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library.

The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes.

Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms.

By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.

What you will learn

Practice the Markov decision process in prediction and betting evaluationsImplement Monte Carlo methods to forecast environment behaviorsExplore TD learning algorithms to manage warehouse operationsConstruct a Deep Q-Network using Python and Keras to control robot movementsApply reinforcement concepts to build a handwritten digit recognition model using an image datasetAddress a game theory problem using Q-Learning and OpenAI Gym

Who this book is for

Keras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x

Key Features

Experimental projects showcasing the implementation of high-performance deep learning models with Keras.Use-cases across reinforcement learning, natural language processing, GANs and computer vision.Build strong fundamentals of Keras in the area of deep learning and artificial intelligence.

Book Description

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas.

To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more.

By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.

What you will learn

Apply regression methods to your data and understand how the regression algorithm worksUnderstand the basic concepts of classification methods and how to implement them in the Keras environmentImport and organize data for neural network classification analysisLearn about the role of rectified linear units in the Keras network architectureImplement a recurrent neural network to classify the sentiment of sentences from movie reviewsSet the embedding layer and the tensor sizes of a network

Who this book is for

If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch

Key Features

Learn and implement machine learning algorithms in a variety of real-life scenariosCover a range of tasks catering to supervised, unsupervised and reinforcement learning techniquesFind easy-to-follow code solutions for tackling common and not-so-common challenges

Book Description

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.

With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.

By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

What you will learn

Use predictive modeling and apply it to real-world problemsExplore data visualization techniques to interact with your dataLearn how to build a recommendation engineUnderstand how to interact with text data and build models to analyze itWork with speech data and recognize spoken words using Hidden Markov ModelsGet well versed with reinforcement learning, automated ML, and transfer learningWork with image data and build systems for image recognition and biometric face recognitionUse deep neural networks to build an optical character recognition system

Who this book is for

This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.

Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease

Key Features

Understand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real world

Book Description

Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You''ll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.

By the end of this book, you''ll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.

What you will learn

Get to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chains

Who this book is for

This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.