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• Part-I - Introduction to Amazon Web Services (100 Pages)
• Chapter 1: AWS Concepts and TechnologiesIntroduction to services like S3, EC2, Identity Access Management, Roles, Load Balancer, Cloud Formation, etc.
• Chapter 2: AWS Billing and PricingUnderstanding AWS pricing, billing, group and tagging, etc.
• Chapter 3: AWS Cloud SecurityDescription about AWS compliance and artifacts, AWS Shield, Cloudwatch, Cloud Trail, etc. Part-II - Machine Learning in AWS (300 Pages)
• Chapter 4: Data Collection and Preparation Concepts include AWS data stores, migration and helper tools. It also includes pre-processing concepts like encoding, feature engineering, missing values removal, etc.
• Chapter 5: Data Modelling and AlgorithmsIn this section, we will talk about all the algorithms that AWS supports, including regression, clustering, classification, image, and text analytics, etc. We will then look at Sagemaker service and how to make models using it.
• Chapter 6: Data Analysis and VisualizationThis chapter talks about the relationship between variables, data distributions, the composition of data, etc.
• Chapter 7: Model Evaluation and OptimizationThis chapter talks about the monitoring of training jobs, evaluating the model accuracy, and fine-tuning models.
• Chapter 8: Implementation and OperationIn this chapter, we'll look at the deployment of models, security, and monitoring.
• Chapter 9: Building a Machine Learning WorkflowIn this chapter, we'll look at the machine learning workflow in AWS . Part-IV - Projects (100 Pages)
• Chapter 10: Project - Building skills with Alexa
• Chapter 11: Project - Time series forecasting using Amazon forecast
• Chapter 12: Project - Modelling and deployment using XGBoost in Sagemaker
• Chapter 13: Text classification using Amazon comprehend and textract
• Chapter 14: Building a complete project pipeline.
• (source: Nielsen Book Data)

Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract. By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning-Specialty certification exam. What You Will Learn Be familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud Formation Understand SageMaker, Amazon Comprehend, and Amazon Forecast Execute live projects: from the pre-processing phase to deployment on AWS Who This Book Is For Machine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification.
(source: Nielsen Book Data)

• Deep Neuro-Fuzzy Systems with Python Chapter 1: Introduction to Fuzzy Set Theory
• Chapter 2: Fuzzy Rules and Reasoning
• Chapter 3: Fuzzy Inference Systems
• Chapter 4: Introduction to Machine Learning
• Chapter 5: Artificial Neural Networks
• Chapter 6: Fuzzy Neural Networks
• Chapter 7: Advanced Fuzzy Networks.
• (source: Nielsen Book Data)

Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. You'll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. You'll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book you'll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. You'll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. What You'll Learn Understand fuzzy logic, membership functions, fuzzy relations, and fuzzy inference Review neural networks, back propagation, and optimization Work with different architectures such as Takagi-Sugeno model, Hybrid model, genetic algorithms, and approximations Apply Python implementations of deep neuro fuzzy system Who This book Is For Data scientists and software engineers with a basic understanding of Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic.
(source: Nielsen Book Data)

• Chapter 1: Installation and Environment Setup
• Chapter Goal: Making System Ready for Image Processing and Analysis
• No of pages 20
• Sub -Topics (Top 2)
• 1.
• Installing Jupyter Notebook
• 2.
• Installing OpenCV and other Image Analysis dependencies
• 3.
• Installing Neural Network Dependencies
• Chapter 2: Introduction to Python and Image Processing
• Chapter Goal: Introduction to different concepts of Python and Image processing Application on it.
• No of pages: 50
• Sub - Topics (Top 2)
• 1. Essentials of Python
• 2. Terminologies related to Image Analysis
• Chapter 3: Advanced Image Processing using OpenCV
• Chapter Goal: Understanding Algorithms and their applications using Python
• No of pages: 100
• Sub - Topics (Top 2):
• 1.
• Operations on Images
• 2.
• Image Transformations
• Chapter 4: Machine Learning Approaches in Image Processing
• Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario
• No of pages: 100
• Sub - Topics (Top 2):
• 1.
• Image Classification and Segmentation
• 2.
• Applying Supervised and Unsupervised Learning approaches on Images using Python
• Chapter 5: Real Time Use Cases
• Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book
• No of pages: 100
• Sub - Topics (Top 5):
• 1. Facial Detection
• 2. Facial Recognition
• 3. Hand Gesture Movement Recognition
• 4. Self-Driving Cars Conceptualization: Advanced Lane Finding
• 5. Self-Driving Cars Conceptualization: Traffic Signs Detection
• Chapter 6: Appendix A
• Chapter Goal: Advanced concepts Introduction
• No of pages: 50
• Sub - Topics (Top 2):
• 1. AdaBoost and XGBoost
• 2. Pulse Coupled Neural Networks.
• (source: Nielsen Book Data)

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
(source: Nielsen Book Data)

• Table of Contents Introduction to Web Application Penetration Testing Metasploit Essentials The Metasploit Web Interface Using Metasploit for Reconnaissance Web Application Enumeration using Metasploit Vulnerability scanning using WMAP Vulnerability Assessment using Metasploit (Nessus) Pentesting CMSes - WordPress Pentesting CMSes - Joomla Pentesting CMSes - Drupal Penetration Testing on Technological Platforms - JBoss Penetration Testing on Technological Platforms - Apache Tomcat Penetration Testing on Technological Platforms - Jenkins Web Application Fuzzing - Logical Bug Hunting Writing Penetration Testing Reports.
• (source: Nielsen Book Data)

Identify, exploit, and test web application security with ease Key Features Get up to speed with Metasploit and discover how to use it for pentesting Understand how to exploit and protect your web environment effectively Learn how an exploit works and what causes vulnerabilities Book DescriptionMetasploit has been a crucial security tool for many years. However, there are only a few modules that Metasploit has made available to the public for pentesting web applications. In this book, you'll explore another aspect of the framework - web applications - which is not commonly used. You'll also discover how Metasploit, when used with its inbuilt GUI, simplifies web application penetration testing. The book starts by focusing on the Metasploit setup, along with covering the life cycle of the penetration testing process. Then, you will explore Metasploit terminology and the web GUI, which is available in the Metasploit Community Edition. Next, the book will take you through pentesting popular content management systems such as Drupal, WordPress, and Joomla, which will also include studying the latest CVEs and understanding the root cause of vulnerability in detail. Later, you'll gain insights into the vulnerability assessment and exploitation of technological platforms such as JBoss, Jenkins, and Tomcat. Finally, you'll learn how to fuzz web applications to find logical security vulnerabilities using third-party tools. By the end of this book, you'll have a solid understanding of how to exploit and validate vulnerabilities by working with various tools and techniques. What you will learn Get up to speed with setting up and installing the Metasploit framework Gain first-hand experience of the Metasploit web interface Use Metasploit for web-application reconnaissance Understand how to pentest various content management systems Pentest platforms such as JBoss, Tomcat, and Jenkins Become well-versed with fuzzing web applications Write and automate penetration testing reports Who this book is forThis book is for web security analysts, bug bounty hunters, security professionals, or any stakeholder in the security sector who wants to delve into web application security testing. Professionals who are not experts with command line tools or Kali Linux and prefer Metasploit's graphical user interface (GUI) will also find this book useful. No experience with Metasploit is required, but basic knowledge of Linux and web application pentesting will be helpful.
(source: Nielsen Book Data)