Download or read book CUSTOMER PERSONALITY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-01 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we embark on an exciting journey through the world of machine learning, where we explore the intricacies of working with datasets, visualizing their distributions, performing regression analysis, and predicting clusters. This book serves as a comprehensive guide for both beginners and experienced practitioners who are eager to delve into the realm of machine learning and discover the power of predictive analytics. Chapter 1 and Chapter 2 sets the stage by introducing the importance of data exploration. We learn how to understand the structure of a dataset, identify its features, and gain insights into the underlying patterns. Through various visualization techniques, we uncover the distribution of variables, detect outliers, and discover the relationships between different attributes. These exploratory analyses lay the foundation for the subsequent chapters, where we dive deeper into the realms of regression and cluster prediction. Chapter 3 focuses on regression analysis on number of total purchases, where we aim to predict continuous numerical values. By applying popular regression algorithms such as linear regression, random forest, naïve bayes, KNN, decision trees, support vector, Ada boost, gradient boosting, extreme gradient boosting, and light gradient boosting, we unlock the potential to forecast future trends and make data-driven decisions. Through real-world examples and practical exercises, we demonstrate the step-by-step process of model training, evaluation, and interpretation. We also discuss techniques to handle missing data, feature selection, and model optimization to ensure robust and accurate predictions. Chapter 4 sets our exploration of clustering customers, we embarked on a captivating journey that allowed us to uncover hidden patterns and gain valuable insights from our datasets. We began by understanding the importance of data exploration and visualization, which provided us with a comprehensive understanding of the distribution and relationships within the data. Moving forward, we delved into the realm of clustering, where our objective was to group similar data points together and identify underlying structures. By applying K-means clustering algorithm, we were able to unveil intricate patterns and extract meaningful insights. These techniques enabled us to tackle various real-world challenges, including customer personality analysis. Building upon the foundation of regression and cluster prediction, Chapter 5 delves into advanced topics, using machine learning models to predict cluster. We explore the power of logistic regression, random forest, naïve bayes, KNN, decision trees, support vector, Ada boost, gradient boosting, extreme gradient boosting, and light gradient boosting models to predict the clusters. Throughout the book, we emphasize a hands-on approach, providing practical code examples and interactive exercises to reinforce the concepts covered. By utilizing popular programming languages and libraries such as Python and scikit-learn, we ensure that readers gain valuable coding skills while exploring the diverse landscape of machine learning. Whether you are a data enthusiast, a business professional seeking insights from your data, or a student eager to enter the world of machine learning, this book equips you with the necessary tools and knowledge to embark on your own data-driven adventures. By the end of this journey, you will possess the skills and confidence to tackle real-world challenges, make informed decisions, and unlock the hidden potential of your data. So, let us embark on this exhilarating voyage through the intricacies of machine learning. Together, we will unravel the mysteries of datasets, harness the power of predictive analytics, and unlock a world of endless possibilities. Get ready to dive deep into the realm of machine learning and unleash the potential of your data. Welcome to the exciting world of predictive analytics!

Download or read book REGRESSION SEGMENTATION CLUSTERING AND PREDICTION PROJECTS WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-02-25 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROJECT 1: TIME-SERIES WEATHER: FORECASTING AND PREDICTION WITH PYTHON Weather data are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, humidity, and the variations and interactions of these variables, and how they change over time. Different spatial scales are used to describe and predict weather on local, regional, and global levels. The dataset used in this project contains weather data for New Delhi, India. This data was taken out from wunderground. It contains various features such as temperature, pressure, humidity, rain, precipitation, etc. The main target is to develop a prediction model accurate enough for forecasting temperature and predicting target variable (condition). Time-series weather forecasting will be done using ARIMA models. The machine learning models used in this project to predict target variable (condition) are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: HOUSE PRICE: ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON The dataset used in this project is taken from the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome. The data contains information from the 1990 California census. Although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning. The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows: longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value, and ocean_proximity. The machine learning models used in this project used to perform regression on median_house_value and to predict it as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: CUSTOMER PERSONALITY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers. Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; and Complain = 1 if customer complained in the last 2 years, 0 otherwise. The target in this project is to perform clustering and predicting to summarize customer segments. In this project, you will perform clustering using KMeans to get 4 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: CUSTOMER SEGMENTATION, CLUSTERING, AND PREDICTION WITH PYTHON In this project, you will develop a customer segmentation, clustering, and prediction to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Following is the Data Dictionary for Credit Card dataset: CUSTID: Identification of Credit Card holder (Categorical); BALANCE: Balance amount left in their account to make purchases; BALANCEFREQUENCY: How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated); PURCHASES: Amount of purchases made from account; ONEOFFPURCHASES: Maximum purchase amount done in one-go; INSTALLMENTSPURCHASES: Amount of purchase done in installment; CASHADVANCE: Cash in advance given by the user; PURCHASESFREQUENCY: How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased); ONEOFFPURCHASESFREQUENCY: How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased); PURCHASESINSTALLMENTSFREQUENCY: How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done); CASHADVANCEFREQUENCY: How frequently the cash in advance being paid; CASHADVANCETRX: Number of Transactions made with "Cash in Advanced"; PURCHASESTRX: Number of purchase transactions made; CREDITLIMIT: Limit of Credit Card for user; PAYMENTS: Amount of Payment done by user; MINIMUM_PAYMENTS: Minimum amount of payments made by user; PRCFULLPAYMENT: Percent of full payment paid by user; and TENURE: Tenure of credit card service for user. In this project, you will perform clustering using KMeans to get 5 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.

Download or read book CUSTOMER SEGMENTATION CLUSTERING AND PREDICTION WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-04 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we conducted a customer segmentation, clustering, and prediction analysis using Python. We began by exploring the customer dataset, examining its structure and contents. The dataset contained various features such as demographic, behavioral, and transactional attributes. To ensure accurate analysis and modeling, we performed data preprocessing steps. This involved handling missing values, removing duplicates, and addressing any data quality issues that could impact the results. We also split the dataset into features (X) and the target variable (y) for prediction tasks. Since the dataset had features with different scales and units, we applied feature scaling techniques. This process standardized or normalized the data, ensuring that all features contributed equally to the analysis. We then performed regression analysis on the "PURCHASESTRX" feature, which represents the number of purchase transactions made by customers. To begin the regression analysis, we first prepared the dataset by handling missing values, removing duplicates, and addressing any data quality issues. We then split the dataset into features (X) and the target variable (y), with "PURCHASESTRX" being the target variable for regression. We selected appropriate regression algorithms for modeling, such as Linear Regression, Random Forest, Naïve Bayes, KNN, Decision Trees, Support Vector, Ada Boost, Catboost, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron regressors. After training and evaluation, we analyzed the performance of the regression models. We examined the metrics to determine how accurately the models predicted the number of purchase transactions made by customers. A lower MAE and RMSE indicated better predictive performance, while a higher R2 score indicated a higher proportion of variance explained by the model. Based on the analysis, we provided insights and recommendations. These could include identifying factors that significantly influence the number of purchase transactions, understanding customer behavior patterns, or suggesting strategies to increase customer engagement and transaction frequency. Next, we focused on customer segmentation using unsupervised machine learning techniques. K-means clustering algorithm was employed to group customers into distinct segments. The optimal number of clusters was determined using KElbowVisualizer. To gain insights into the clusters, we visualized them 3D space. Dimensionality PCA reduction technique wasused to plot the clusters on scatter plots or 3D plots, enabling us to understand their separations and distributions. We then interpreted the segments by analyzing their characteristics. This involved identifying the unique features that differentiated one segment from another. We also pinpointed the key attributes or behaviors that contributed most to the formation of each segment. In addition to segmentation, we performed clusters prediction tasks using supervised machine learning techniques. Algorithms such as Logistic Regression, Random Forest, Naïve Bayes, KNN, Decision Trees, Support Vector, Ada Boost, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron Classifiers were chosen based on the specific problem. The models were trained on the training dataset and evaluated using the test dataset. To evaluate the performance of the prediction models, various metrics such as accuracy, precision, recall, F1-score, and ROC-AUC were utilized for classification tasks. Summarizing the findings and insights obtained from the analysis, we provided recommendations and actionable insights. These insights could be used for marketing strategies, product improvement, or customer retention initiatives.

Download or read book Data Driven Dealings Development written by Jesko Rehberg and published by . This book was released on 2020-12-11 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: When I was starting my Python data analysis journey I was missing books or tutorials which really cover all the topics involved when trying to conduct a sales analysis successfully. Especially when you are a complete newbie conducting an analysis from A-Z without any (or not sufficient) pre-knowledge is difficult, because most books only cover specific parts of the whole project, and it is challenging to put all the pieces of the puzzle together. That is my main motivation for writing this book: you to have one guideline in hand which leads all the way through your whole sales analysis project, from installing all the necessary Python libraries, cleaning the data, effectively training the Machine Learning (ML) models and deploying the results to your colleagues in an intelligible way. The topics covered in this book are: Explorative Data Analysis (EDA)Feature Engineering and Clustering (Unsupervised Machine Learning) Predicting of future sales using statistical modelling, Prophet, and Long-Short-Term Memory (LSTM) using deep learning techniques (Tensorflow/ Keras) Market Basket Analysis using the Apriori Algorithm and Spark Recommend products to our customers using Scikit-Learn, Pandas, Tensorflow, and Turicreate Stac

Download or read book Customer Personality Exploratory Data Analysis written by Snehi Nainesh Pachchigar and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Businesses these days rely largely on targeted marketing to have a better possibility of expanding as well as retaining their customer base. Tech giants like Google, Facebook have grown their business model surrounding targeted ads which help businesses to grow. Customer Personality Analysis is a detailed analysis of a company's ideal set of customers. Here, a dataset of customer characteristics is used to understand and analyze the customer habits and preferences. The dataset includes customer attributes such as birth year, education, marital status, having children or not, income, the amount spent on various products. The thesis represents how the data is cleaned and preprocessed along with reducing dimensionality. The meat of the thesis lies in clustering of data and discovering and predicting their choices. To enable the same the data went through iteration of data preprocessing to enable getting the best results. For clustering, the agglomerative algorithm and k-means algorithm are being used which will help bucket customers into segments per their choices. The analysis aided with visualization will help to target customers based on their interests. The model and techniques used here can further be replicated to adjust to the different datasets and hence gain valuable insights.

Download or read book Machine Learning for Emotion Analysis in Python written by Allan Ramsay and published by Packt Publishing Ltd. This book was released on 2023-09-28 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kickstart your emotion analysis journey with this step-by-step guide to data science success Key Features Discover the inner workings of the end-to-end emotional analysis workflow Explore the use of various ML models to derive meaningful insights from data Hone your craft by building and tweaking complex emotion analysis models with practical projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionArtificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially. With this book, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you’ll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions. The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, you’ll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you’ll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion. By the end of this book, you’ll be well-equipped to use emotion mining and analysis to drive business decisions.What you will learn Distinguish between sentiment analysis and emotion analysis Master data preprocessing and ensure high-quality input Expand the use of data sources through data transformation Design models that employ cutting-edge deep learning techniques Discover how to tune your models’ hyperparameters Explore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use cases Practice your newly acquired skills by working on real-world scenarios Who this book is forThis book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.

Download or read book Proceedings of Second Doctoral Symposium on Computational Intelligence written by Deepak Gupta and published by Springer Nature. This book was released on 2021-09-19 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features high-quality research papers presented at Second Doctoral Symposium on Computational Intelligence (DoSCI-2021), organized by Institute of Engineering and Technology (IET), AKTU, Lucknow, India, on 6 March 2021. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum-inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high-performance computing, biomedical computing, decision support and decision making.

Download or read book Microsoft Azure Essentials Azure Machine Learning written by Jeff Barnes and published by Microsoft Press. This book was released on 2015-04-25 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Research Anthology on Artificial Intelligence Applications in Security written by Management Association, Information Resources and published by IGI Global. This book was released on 2020-11-27 with total page 2253 pages. Available in PDF, EPUB and Kindle. Book excerpt: As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.

Download or read book Explainable and Interpretable Models in Computer Vision and Machine Learning written by Hugo Jair Escalante and published by Springer. This book was released on 2018-11-29 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

Download or read book Machine Learning for Cybersecurity Cookbook written by Emmanuel Tsukerman and published by Packt Publishing Ltd. This book was released on 2019-11-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.

Download or read book 2019 Global Conference for Advancement in Technology GCAT written by IEEE Staff and published by . This book was released on 2019-10-18 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Global conference targets different scientific fields and invites academics, researchers and educators to share innovative ideas and expose their works in the presence of experts from all over the world GCAT 2019 focuses on original research and practice driven applications It provides a common linkage between a vibrant scientific and research community and industry professionals by offering a clear view on modern problems and challenges in information technology GCAT 2019 offers a balance between innovative industrial approaches and original research work while keeping the readers informed of the security techniques, approaches, applications and new technologies The conference is an opportunity for students, doctors, academics and researchers to open up to the outside world, make connections and collaborate with various domain experts GCAT 2019 particularly welcomes papers on the following topics

Download or read book Progress in Computing Analytics and Networking written by Himansu Das and published by Springer Nature. This book was released on 2020-03-26 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on new and original research ideas and findings in three broad areas: computing, analytics, and networking and their potential applications in the various domains of engineering – an emerging, interdisciplinary area in which a wide range of theories and methodologies are being investigated and developed to tackle complex and challenging real-world problems. The book also features keynote presentations and papers from the International Conference on Computing Analytics and Networking (ICCAN 2019), which offers an open forum for scientists, researchers and technocrats in academia and industry from around the globe to present and share state-of-the-art concepts, prototypes, and innovative research ideas in diverse fields. Providing inspiration for postgraduate students and young researchers working in the field of computer science & engineering, the book also discusses hardware technologies and future communication technologies, making it useful for those in the field of electronics.

Download or read book International Conference on Intelligent Data Communication Technologies and Internet of Things ICICI 2018 written by Jude Hemanth and published by Springer. This book was released on 2018-12-20 with total page 1590 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses data communication and computer networking, communication technologies and the applications of IoT (Internet of Things), big data, cloud computing and healthcare informatics. It explores, examines and critiques intelligent data communications and presents inventive methodologies in communication technologies and IoT. Aimed at researchers and academicians who need to understand the importance of data communication and advanced technologies in IoT, it offers different perspectives to help readers increase their knowledge and motivates them to conduct research in the area, highlighting various innovative ideas for future research.

Download or read book Hands On Python Deep Learning for the Web written by Anubhav Singh and published by Packt Publishing Ltd. This book was released on 2020-05-15 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use the power of deep learning with Python to build and deploy intelligent web applications Key FeaturesCreate next-generation intelligent web applications using Python libraries such as Flask and DjangoImplement deep learning algorithms and techniques for performing smart web automationIntegrate neural network architectures to create powerful full-stack web applicationsBook Description When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices. What you will learnExplore deep learning models and implement them in your browserDesign a smart web-based client using Django and FlaskWork with different Python-based APIs for performing deep learning tasksImplement popular neural network models with TensorFlow.jsDesign and build deep web services on the cloud using deep learningGet familiar with the standard workflow of taking deep learning models into productionWho this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.

Download or read book Data Management Analytics and Innovation written by Valentina Emilia Balas and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry.