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[100% OFF] The Art of Doing: Master Networks and Network Scanning

Go from entering "nmap 192.168.1.0/24" t... Moreo UNDERSTANDING the command Less
Expires 06.03.2021
131 used, 66% success rate
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[100% OFF] The Art of Doing: Master Networks and Network Scanning

Go from entering "nmap 192.168.1.0/24" t... Moreo UNDERSTANDING the command Less
Expires 06.03.2021
131 used, 66% success rate
Rate:

[100% OFF ] HANDS ON DOCKER for JAVA Developers Free

This course is a 100% HANDS ON course for Java Ent... Morehusiasts who want to use DOCKER To Build->Ship->Run Java Apps using Docker and want to learn thru 10+ real world hands on use cases. This course is optimized for the busy professional with real world use cases examples and problem solving. The student registering for the course should be able to dedicate time towards Hands on labs to get a clearer understanding on how to use docker. Docker Version: 18.03.1-ce, JDK 8 Learn to build real world apps using Java and Docker with Microservices using the Spring framework, JQuery, Bootstrap and much more.... T Apart from the theoretical aspect here are the HANDS ON LAB Exercises which will be covered MICROSERVICES using Docker . Build a Spring MVC and MYSQL RESTFUL MICROSERVICE, Scale a micro service with multiple containers Build a Proxy Servlet, a filter with a Spring MVC app backed by MYSQL to load balance the requests between containers. Learn what the real world problems are and how Docker attempts to solve real world use cases. Learn to Run WEB Apps on Apache HTTP and NGINX Web servers in Docker as containers. Learn to run Simple Java Programs developed using JDK8 using Docker Create a sample Spring MVC Web App running with a bootstrap and JQUERY UI and run it using Docker Learn about Docker machines and Docker compose Upload your code to DOCKER HUB and share your Docker images for deployments with peers Less
Expired
58 used

[100% OFF] Java Parallel Computation on Hadoop Free

Build your essential knowledge with this hands-on,... More introductory course on the Java parallel computation using the popular Hadoop framework: - Getting Started with Hadoop - HDFS working mechanism - MapReduce working mecahnism - An anatomy of the Hadoop cluster - Hadoop VM in pseudo-distributed mode - Hadoop VM in distributed mode - Elaborated examples in using MapReduce Learn the Widely-Used Hadoop Framework Apache Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. Hadoop is an Apache top-level project being built and used by a global community of contributors and users. It is licensed under the Apache License 2.0. All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are common and thus should be automatically handled in software by the framework. Apache Hadoop's MapReduce and HDFS components originally derived respectively from Google's MapReduce and Google File System (GFS) papers. Who are using Hadoop for data-driven applications? You will be surprised to know that many companies have adopted to use Hadoop already. Companies like Alibaba, Ebay, Facebook, LinkedIn, Yahoo! is using this proven technology to harvest its data, discover insights and empower their different applications! Contents and Overview As a software developer, you might have encountered the situation that your program takes too much time to run against large amount of data. If you are looking for a way to scale out your data processing, this is the course designed for you. This course is designed to build your knowledge and use of Hadoop framework through modules covering the following: - Background about parallel computation - Limitations of parallel computation before Hadoop - Problems solved by Hadoop - Core projects under Hadoop - HDFS and MapReduce - How HDFS works - How MapReduce works - How a cluster works - How to leverage the VM for Hadoop learning and testing - How the starter program works - How the data sorting works - How the pattern searching - How the word co-occurrence - How the inverted index works - How the data aggregation works - All the examples are blended with full source code and elaborations Come and join us! With this structured course, you can learn this prevalent technology in handling Big Data. Who this course is for: IT Practitioners Software Developers Software Architects Programmers Data Analysts Data Scientists Less
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[100% OFF] Learn to Code with Python 3! Free

If you would like to learn Python 3 programming in... More 2020, you are going to LOVE this course! Get started with the most beginner friendly programming language and start writing your very own programs today! We will cover the following topics in this course: Introduction to Python and setup Python programming basics Functions - coding exercises Lists, tuples and dictionaries Files in Python 3 Error handling Object oriented programming Date & time Regular expressions Interacting with HTTP Networking in Python 3 Threading E-mails, PDFs, images This course was designed for absolute beginners who wish to master the Python programming language. All lectures are downloadable for offline viewing. English subtitles and a certificate of completion are are available as well. Thank you for taking the time to read this and we hope to see you in the course! Who this course is for: Students interested in learning the Python programming language. Students who wish to pursue a career in software development. Less
Expired
89 used

[100% OFF] Complete Python Bootcamp for Data Science& Machine Learning Free

This comprehensive course will be your guide to le... Morearning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! Enroll in the course and become a data scientist today! Less
Expired
68 used

[100% OFF] Complete Machine Learning with R Studio - ML for 2020 Free

You're looking for a complete Machine Learning cou... Morerse that can help you launch a flourishing career in the field of Data Science & Machine Learning, right? You've found the right Machine Learning course! After completing this course you will be able to: · Confidently build predictive Machine Learning models to solve business problems and create business strategy · Answer Machine Learning related interview questions · Participate and perform in online Data Analytics competitions such as Kaggle competitions Check out the table of contents below to see what all Machine Learning models you are going to learn. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning. Why should you choose this course? This course covers all the steps that one should take while solving a business problem through linear regression. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 3 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use R for Machine Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Who this course is for: People pursuing a career in data science Working Professionals beginning their Data journey Statisticians needing more practical experience Less
Expired
78 used

[100% OFF] Legal Document Automation using Documate Free

In this course, you will learn how to automate you... Morer legal documents using Documate. The McKinsey Global Institute found that 23% of a lawyer's job can be automated. Legal documents can be tedious and repetitive. However, with the right approach, you can automate the creation of legal documents. Documate is one of the most powerful technologies to improve your legal practice. Attorneys that embrace document automation will enjoy tremendous benefits resulting from their new competitive advantage. Get started automating your documents! Less
Expired

[100% OFF] Deep Learning for Beginners: Neural Networks in R Studio Free

You're looking for a complete Artificial Neural Ne... Moretwork (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create a predictive model using Neural Networks. Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems. Below are the course contents of this course on ANN: Part 1 - Setting up R studio and R Crash course This part gets you started with R. This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Part 2 - Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 - Creating Regression and Classification ANN model in R In this part you will learn how to create ANN models in R Studio. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models. We also understand the importance of libraries such as Keras and TensorFlow in this part. Part 4 - Data Preprocessing In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful. In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. Part 5 - Classic ML technique - Linear Regression This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a Neural Network model in R will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below are some popular FAQs of students who want to start their Deep learning journey- Why use R for Deep Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Who this course is for: People pursuing a career in data science Working Professionals beginning their Neural Network journey Statisticians needing more practical experience Anyone curious to master ANN from Beginner level in short span of time Less
Expired
46 used

[100% OFF] Complete Vue.js 3 (Inc. Composition API, Vue Router, Vuex) Free

Another Vue.js 3 from zero to hero course - kind o... Moref. This course is for developers who want to move fast. We cover the traditional way of building Vue apps - the Options API - as well as the the new Composition API, and even see how you can mix and match them together. There are 8 modules; 4 introduce fundamental skills (Options API; Composition API; Vuex and Vue Router). Every other module is a project, so you can see how to apply the fundamental skills in real apps. I am a big believer in learning by doing. After covering Vue; we look at Vuex, Vue's state management solution, and Vue Router, for front-end routing. The course culminates with a capstone project, using the Vue trunity (Vue, Vuex, Vue Router) to build an application. Less
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80 used

[100% OFF] Mastering CSS Grid 2020 - With 3 cool projects Free

CSS Grid is a relatively new and unused concept to... More CSS. It's very important these days that our layouts are simple to maintain, and easy to adjust based on the dimensions of our device. CSS grid simplifies this process over other existing strategies. In this course we take you deep into how to build a variety of different layouts in CSS Grid. We cover the following:- All the properties in CSS Grid, how they work, and any gotchas that you may not be aware of when using them. Alignment and how it works in CSS Grid, including the alignment of tracks and grid items at the grid container level. We also show you how to override alignment at the grid item level. The basics of responsive web design, such as media queries and how they work with mobile / tablet etc. Grid areas and how it helps simplify responsive web design The concept of implicit and explicit grids and what the differences are A deep look into the Autoplacement algorithm Animation at a high level and what works currently with CSS Grid How to convert some layouts in Flexbox to use CSS Grid instead When to use Flexbox over CSS Grid Once we cover all of these concepts, we then go about showing you some basic layout problems and how they can be solved. This includes:- Column based layout Basic Sidebar layout Vertical Text alignment Modal alignment Stick footer layout Formatting form fields And hopefully in future much more. We then look at some advance layout topics such as:- The Holygrail layout The Media Objects layout A Viewport Friendly Image Gallery A Responsive Image Gallery An Animated Sidebar A Monthly Calendar layout A Newspaper Cover layout A Responsive Twitter Clone Layout A Responsive Movie Gallery Then if you haven't had enough, we have 3 real life application examples that will give you the experience you need. They are:- A Chat UI interface An Uber Eats Clone Responsive Application A YouTube Clone Responsive Application Who this course is for: Web Developers Web Designers User Experience Designers Front End Developers Full Stack Developers Less
Expired
130 used