“…This course covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-Series. On completion of the course, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. …”

Tabla de Contenidos: “…10.11.7 Advanced Concepts 518 -- Further Readings 519 -- Glossary 521 -- 11 Remote Sensing Satellites 524 -- 11.1 Remote Sensing -- An Overview 524 -- 11.1.1 Aerial Remote Sensing 525 -- 11.1.2 Satellite Remote Sensing 525 -- 11.2 Classification of Satellite Remote Sensing Systems 526 -- 11.2.1 Optical Remote Sensing Systems 526 -- 11.2.2 Thermal Infrared Remote Sensing Systems 528 -- 11.2.3 Microwave Remote Sensing Systems 529 -- 11.3 Remote Sensing Satellite Orbits 531 -- 11.4 Remote Sensing Satellite Payloads 531 -- 11.4.1 Classification of Sensors 531 -- 11.4.2 Sensor Parameters 534 -- 11.5 Passive Sensors 535 -- 11.5.1 Passive Scanning Sensors 536 -- 11.5.2 Passive Non-scanning Sensors 539 -- 11.6 Active Sensors 540 -- 11.6.1 Active Non-scanning Sensors 540 -- 11.6.2 Active Scanning Sensors 540 -- 11.7 Types of Images 542 -- 11.7.1 Primary Images 542 -- 11.7.2 Secondary Images 542 -- 11.8 Image Classification 545 -- 11.9 Image Interpretation 546 -- 11.9.1 Interpreting Optical and Thermal Remote Sensing Images 546 -- 11.9.2 Interpreting Microwave Remote Sensing Images 547 -- 11.9.3 GIS in Remote Sensing 547 -- 11.10 Applications of Remote Sensing Satellites 548 -- 11.10.1 Land Cover Classification 548 -- 11.10.2 Land Cover Change Detection 549 -- 11.10.3 Water Quality Monitoring and Management 550 -- 11.10.4 Flood Monitoring 551 -- 11.10.5 Urban Monitoring and Development 552 -- 11.10.6 Measurement of Sea Surface Temperature 552 -- 11.10.7 Deforestation 553 -- 11.10.8 Global Monitoring 553 -- 11.10.9 Predicting Disasters 555 -- 11.10.10 Other Applications 558 -- 11.11 Major Remote Sensing Missions 558 -- 11.11.1 Landsat Satellite System 558 -- 11.11.2 SPOT Satellite System 561 -- 11.11.3 Radarsat Satellite System 564 -- 11.11.4 Indian Remote Sensing Satellite System 565 -- 11.12 Future Trends 573 -- Further Readings 574 -- Glossary 575 -- 12 Weather Satellites 577 -- 12.1 Weather Forecasting -- An Overview 577 -- 12.2 Weather Forecasting Satellite Fundamentals 580.…”

“…El volum pretén, a més recuperar les formes de treball i les pràctiques d'antany, acostar el món laboral d'aquella època i les diferents formes de treball, dedicant una part als sistemes d'explotació agrària i dels recursos forestals. Finalment, el llibre també serveix per a recopilar els oficis tradicionals desenvolupats en la població…”
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“…—Bjarne Stroustrup, Designer of C++ “I am happy to see the content of Alex’s course, the development and teaching of which I strongly supported as the CTO of Silicon Graphics, now available to all programmers in this elegant little book.” —Forest Baskett, General Partner, New Enterprise Associates “Paul’s patience and architectural experience helped to organize Alex’s mathematical approach into a tightly-structured edifice—an impressive feat!” …”

“…Coverage includes Assessing the strengths and limitations of mainstream monitoring tools and IDS technologies Using Attack Graphs to map paths of network vulnerability and becoming more proactive about preventing intrusions Analyzing network behavior to immediately detect polymorphic worms, zero-day exploits, and botnet DoS attacks Understanding the theory, advantages, and disadvantages of the latest Web Application Firewalls Implementing IDS/IPS systems that protect wireless data traffic Enhancing your intrusion detection efforts by converging with physical security defenses Identifying attackers’ “geographical fingerprints” and using that information to respond more effectively Visualizing data traffic to identify suspicious patterns more quickly Revisiting intrusion detection ROI in light of new threats, compliance risks, and technical alternatives Includes contributions from these leading network security experts: Jeff Forristal, a.k.a. Rain Forest Puppy, senior security professional and creator of libwhisker Seth Fogie, CEO, Airscanner USA; leading-edge mobile security researcher; coauthor of Security Warrior Dr. …”

“…We will quickly go through the architecture and fundamentals of Active Directory and then dive deep into the core components, such as forests, domains, sites, trust relationships, OU, objects, attributes, DNS, and replication. …”

“…You also explore the different post-installation tasks, including configuration of domains and forests; management of users, computers, groups, and organizational units; and configuration of group policies. …”

“…What you will learn Prepare data for machine learning methods with ease Understand how to write production-ready code and package it for use Produce simple and effective data visualizations for improved insights Master advanced methods, such as Boosted Trees and deep neural networks Use natural language processing to extract insights in relation to text Implement tree-based classifiers, including Random Forest and Boosted Tree Who this book is for This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement..…”

“…You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. …”

“…Korstian Professor of Forest Economics & Management Nicholas School of the Environment, Duke University, USA…”

“…What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. …”

“…You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. …”

“…What you will learn Develop a joke recommendation engine to show jokes that match users’ tastes Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machines using reinforcement learning Implement natural language processing (NLP) techniques for sentiment analysis and customer segmentation Produce simple and effective data visualizations for improved insights Use NLP to extract insights for text Implement tree-based classifiers including random forest and boosted tree Who this book is for If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning techniques using R, this is an ideal Learning Path for you. …”

“…Use a Telco data set, to predict customer churn using Random Forests. In Detail Spark juggernaut keeps on rolling and getting more and more momentum each day. …”

“…What You Will Learn Master basic statistics, descriptive statistics, and probability theory Explore ML methods, including decision trees and decision forests Learn probability distributions normal and Poisson distributions Explore hypothesis testing, p-values, types I and II error handling Master logistic regression, linear regression, and regression trees Learn correlation, R-Square, RMSE, MAE, and coefficient of determination Audience This beginner-level course has been niched to cater to an individual looking to master statistics and probability for data science and analysis, an individual looking to pursue a career in data science, or professionals and students wanting to understand statistics for data analysis. …”

“…You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.By the end of this book, you'll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.What you will learnEmploy Bioconductor to determine differential expressions in RNAseq dataRun SAMtools and develop pipelines to find single nucleotide polymorphisms (SNPs) and IndelsUse ggplot to create and annotate a range of visualizationsQuery external databases with Ensembl to find functional genomics informationExecute large-scale multiple sequence alignment with DECIPHER to perform comparative genomicsUse d3.js and Plotly to create dynamic and interactive web graphicsUse k-nearest neighbors, support vector machines and random forests to find groups and classify dataWho this book is forThis book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning through a recipe-based approach. …”

“…What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. …”

“…What you will learn Become familiar with the basic features of the TensorFlow library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks to improve predictive modeling Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Implement the gradient boosted random forest to predict housing prices Take TensorFlow into production Who this book is for If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. …”