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

Go from entering "nmap 192.168.1.0/24" to UNDERSTANDING the command
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[100% OFF] Ionic and React: Building a Mobile Application UI Free

The Ionic Framework is supported on a variety of m... Moreobile (and desktop) platforms. In this course, you will learn how to go from no code to your very own application ready for sale on the Apple App Store and Google Play Store. Unlike many courses that spend a lot of time on background, this course will be fast-paced, with a minimum of fuss and fluff. It's all hands-on. I expect you to have some basic understanding of web development. You should know what a div is, for example; and know how to create a button. You should have a decent grasp of JavaScript, but you don't need to be a master. I don't expect you to be an expert in React, but you should grasp the basics of JSX, component architecture, state, and hooks. Other concepts will be explained as needed, as close to their use as I can get. By the time you complete the course, you should have the confidence you need to create and deploy your own mobile app UI for iOS or Android. It'll be a fast ride, so enroll now and hang on. Who this course is for: Any developer wanting to publish mobile applications with technology they already know and love. Less
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[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
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[10$] Cloud Computing for Machine Learning

Build and Deploy Machine Learning programs on the cloud
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[100% OFF] Python for beginners - Learn all the basics of python Free

Have you always wanted to learn programming but di... Moredn't know where to start ? Well now you are at the right place ! I created this python course to help everyone learn all the basics of this programming language. This course is really straight to the point and will give you all the notion about python. Also, the course is not that long so and the way the material is presented is very easy to assimilate. So if python is something that you are interested about, then you will definitely like this course. Enjoy your learning :) Who this course is for: People interested to learn how to program in python people curious about programming Less
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[100% OFF] Learn Creational Design Patterns in Java Free

Design patterns provide a template for writing qua... Morelity code. Knowing which design pattern to use in which scenario can be challenging but will make you a master Java programmer. In this course you will take a deep dive into creational patterns, which can help you create more flexible, reusable objects. I will be covering the six most popular creational patterns— Builder, Telescoping Constructor, Singleton, Prototype, Factory and Abstract Factory as well as concepts such as multithreading, mutability, inheritance and Java Heap and Stack workings. I will provide example use cases, complete with implementation instructions and tips for avoiding the unique challenges posed by each pattern also explain how do design patterns work inside Java Heap and Stack Memory. By the end of this course, you'll be equipped with the knowledge and skills necessary to implement each design patterns in your Java projects. Learning Objectives: What are creational design patterns? How to avoid complex constructors? Implementing the Builder pattern Implementing the Telescoping pattern Understanding similarity between Builder and Telescoping pattern Best interview answers for Singleton pattern Questions. Also get a practical idea about the advance concepts such as serialization, cloning, multi-threading and reflection Problem and Solution of Multi-threading with the Singleton pattern Implementing the Prototype pattern Best interview answers for Prototype pattern Questions. Implementing simple Factory pattern Implementing Abstract Factory pattern Who this course is for: Java Developer Java Developer curious about design patterns Java Architect Nail your Java Interviews Web Application Developers Industrial experts API Developers Tech Architects Less
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[100% OFF] Machine Learning with Jupyter Notebooks in Amazon AWS Free

Are you a company or a IT administrator, data cent... Moreer architect, consultant, enterprise architect, data protection officer, programmer, data security specialist, or big data analyst and want to gain fundamental and intermediate level skills and enjoy a fascinating high paying career? Or maybe you just want to learn additional tips and techniques taking to a whole new level? Welcome to Machine Learning, Reinforcement Learning and AWS course For Beginners - A one of its kind course! The flipped classroom model with hand-on learning will help you experience direct into the course as your begin your learning journey. Be sure to watch the preview lectures that set course expectations! In this course, you'll learn and practice: Machine Learning topics Jupyter Notebooks Reinforcement Learning Machine Learning Services in AWS AWS Sagemaker Dynamic Programming Q-Learning Understand best practices, and much more.... You will also get complete resources, toolkit, and code where applicable with this course! We've built this course with our Team ClayDesk of industry recognized developers and consultants to bring you the best of everything! So, if you would like to: - start your freelancing career and consult companies, this course is for you - gain marketable skills as an IT expert and professional, this course is for you - This course is not designed for advanced level students ...this Machine Learning, Reinforcement Learning and AWS course is exactly what you need, and more. (You’ll even get a certification of completion) See what our students say “It is such a solid course that covers all important areas of machine learning, and I now know hoe to predict future products based on their features. Simply awesome!.” - Alex Neuman “This is such an awesome course. I loved every bit of it – Wonderful learning experience!” Ankit Goring. Join thousands of other students and share valuable experience Why take this course? As an enterprise architect consulting with global companies, technology evangelist, and brand innovator, I have designed, created, and implemented enterprise level projects, I am excited to share my knowledge and transfer skills to my students. Enroll now in Machine Learning, Reinforcement Learning and AWS today and revolutionize your learning. Stay at the cutting edge of Machine Learning and Data Science —and enjoy bigger, brighter opportunities with AWS. Qasim Shah Who this course is for: Beginner IT professionals who want to get in the forefront of the Artificial Intelligence and Machine Learning game Anyone who is curios about machine learning Less
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[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
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[100% OFF] Learn Figma for Web Design, User Interface, UI UX in an hour Free

Learn the most important Skill of 2020, Graphic de... Moresign in Figma in just an Hour! Taught by a Professional Web Designer who has his own multiple startups. Figma is the best tool to start your design career and fill your Passion for design. With it you can do UI UX Design, Graphical User Interface, Web Design, Wireframe, User Interface, Icon, Logo design, Graphic Design all in Figma! It is simple to use, is portable, ultra-light, and has amazing features to give your creativity wings! You will know why Figma is much better than other Softwares and why you should be using it to help you become a better designer, SO you keep designing and leave the worries to Figma! Less
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[100% OFF] Python for Beginners - Learn Python Programming in Hindi Free

Head into the world of Python Programming, easy an... Mored detailed. Build your own applications, right from the basics! Less
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111 used

[100% OFF] Home Business Basic Sales & Marketing Tools

Learn About Some Of The Basic Sales & Marketin... Moreg Tools For A Home Business Less
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63 used

[100% OFF] Python for beginners - Learn all the basics of python Free

Have you always wanted to learn programming but di... Moredn't know where to start ? Well now you are at the right place ! I created this python course to help everyone learn all the basics of this programming language. This course is really straight to the point and will give you all the notion about python. Also, the course is not that long so and the way the material is presented is very easy to assimilate. So if python is something that you are interested about, then you will definitely like this course. Enjoy your learning :) Less
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[100% OFF] Partnership Accounting Free

Partnership accounting will cover accounting topic... Mores related to a partnership form of business entity. This partnership course will focus on the accounting issues that are different in a partnership as compared to other types of entities like a sole proprietorship or corporations. As we focus on the differences, we always want to keep in mind the similarities in accounting for different business entities. Most of what we have learned about the double entry accounting system and day to day accounting transactions in prior courses will remain the same. We are concentrating on the areas that will different because the differences are where the new information lies. We will start by defining what a partnership is and comparing the characteristics of a partnership with other business entities. Understanding one business entity and its components are often best learned by contrasting them with the attributes of others, always considering the relative pros and cons, thinking of situations and circumstances that would benefit one form of business entity over another. The course will discuss the process for setting up a new partnership, typically starting with the partners contributing capital to the partnership, requiring us to record the contribution and the capital accounts. We will discuss the allocation of net income to the partners, one of the primary differences between a partnership type of entity and other types of entities. There is a lot of flexibility for net income allocation in a partnership, and this is one of its primary benefits. The course will cover the recording of partnership draws, how to record them, as well as the closing process for a partnership. The steps of the closing process will be much the same as those for any business entity except for the allocation of net income to the partners. We will consider the situation where a partner leaves a partnership or where a new partner is added. The transactions related to a partner leaving or being added is unique to a partnership entity. The course will cover a partnership liquidation process or closing process. The liquidation process is a very useful exercise for any entity because it allows us to do take apart a business. We have discussed the process of putting new busses together in prior courses, but to fully understand how something works we must also take it apart. The liquidation process is particularly important for a partnership entity because it emphasizes the capital accounts and the process necessary to reduce the likelihood of problems during the process. We also include a comprehensive problem designed to take a step back and consider all the similarities between business entities so that we can put the differences we have studied in this course into perspective. The comprehensive problem will cover the entire accounting cycle from entering journal entries and posting to the general ledger, to recording adjusting entries and creating the financial statements, to the closing process. The course also includes definition and key accounting terms related to partnerships. In addition to the instructional videos, this course will include downloadable • Downloadable PDF Files • Excel Practice Files • Multiple Choice Practice Questions • Short Calculation Practice Questions • Discussion Questions The PDF files allow us to download reference information we can use offline and as a guide to help us work through the material. Excel practice files will be preformatted so that we can focus on the adjusting process and learning some of the basics of Excel, like addition, subtraction, and cell relationships. Multiple choice example question helps us improve our test-taking skills by reducing the information into the size and format of multiple choice questions and discussing how to approach these questions. Short calculation questions help us reduce problems that have some calculation down to a short format that could be used in multiple choice questions. Discussion Question will provide an opportunity to discuss these topics with the instructor and other students, a process many students find very helpful because it allows us to see the topic from different viewpoints. Who will we be learning from? You will be learning from somebody who has technical experience in accounting concepts and in accounting software like QuickBooks, as well as experience teaching and putting together curriculum. You will be learning from somebody who is a: • CPA – Certified Public Accountant • CGMA – Chartered Global Management Accountant • Master of Science in Taxation • CPS – Certifies Post-Secondary Instructor • Curriculum Development Export As a practicing CPA the instructor has worked with many technical accounting issues and helped work through them and discuss them with clients of all levels. As a CPS and professor, the instructor has taught many accounting classes and worked with many students in the fields of accounting, business, and business applications. The instructor also has a lot of experience designing courses and learning how students learn best and how to help students achieve their objectives. Experience designing technical courses has also benefit in being able to design a course in a logical fashion and deal with problems related to technical topics and the use of software like QuickBooks Pro. Content Includes: • Process for setting up a partnership • The allocation of net income to partners • The recording of partner withdrawals • Recording the partnership closing process • Journal entries related to a partner leaving the partnership • Journal entries related to adding a new partner to the partnership • The liquidation process for a partnership • A comprehensive problem on the accounting cycle • Key terms definitions and explanations related to partnerships Who this course is for: Accounting students Business owner Anyone who whats to learn accounting Less
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[100% OFF] Apache kafka™ - A Complete Hands-on Kafka Developer's Guide Free

Apache Kafka has become the leading distributed da... Moreta streaming enterprise big data technology. Kafka is used in production by over 33% of the Fortune 500 companies such as Netflix, Airbnb, Uber, Walmart, and LinkedIn. To learn Kafka easily, step-by-step, you have come to the right place! No prior Kafka knowledge is required. If you look at the documentation, you can see that Apache Kafka is not easy to learn... Thanks to my several years of experience in Kafka and Big Data, I wanted to make learning Kafka accessible to everyone. We'll take a step-by-step approach to learn all the fundamentals of Apache Kafka. At the end of this course, you'll be productive and you'll know the following: The Apache Kafka Ecosystem Architecture The Kafka Core Concepts: Topics, Partitions, Brokers, Replicas, Producers, Consumers, and more! Launch your own Kafka cluster in no time using native Kafka binaries – Windows / MacOS X / Linux Learn and Practice using the Kafka Command Line Interface (CLI) Code Producer and Consumers using the Java API Real-world project using Twitter as a source of data for a producer and ElasticSearch as a sink for our consumer Note: The hands-on section is based on Java, which is the native Kafka programming language. But, good news! Your learning in Java will be completely applicable to other programming languages, such as Python, C#, Node.js or Scala, and Big Data frameworks such as Spark, NiFi or Akka. Who this course is for: Developers who want to learn the Apache Kafka Fundamentals, start a cluster and write their first application Architects who want to understand how Apache Kafka fits into their solution architecture Anyone looking to learn the full theory of how Apache Kafka works as a distributed system Less
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[100% OFF] The Complete Android App Development Masterclass: Build Apps Free

Welcome To The Complete Android App Development Ma... Moresterclass: Build 4 Apps ⇉ Join 500,000+ Students Who Have Enrolled in Our Udemy Courses! ⇉ 10,000+ Five Star Reviews Show Students Who Enrolling Are Getting Real Results! ⇉ You Get Over 20+ hours and 100+ Lectures of FULL HD content! ⇉ Watch the Promo Video to see how you can Get Started Today! _________________________________________________________________________ Here are all of the benefits to enrolling in our complete Android App course today -We've designed this course to include everything you need to know about Android App Development in 2020 and beyond.. -We've also designed this course so that you can learn everything you need to start building your own Android Apps in less than 8 weeks.. -Our previous android app course had more than 100,000 students -- so we've made this course bigger, better, with more apps and even more affordable -Don't have any previous experience with Android App Development? No problem. We take you from beginner to advanced and show you how to get build real world android apps from scratch - you don't need any prior experience to enroll in our course. But thats not all... We believe the best way to learn in by DOING. That is why we have you build 4 real world applications right along side of us in this course! These hands on projects will allow you to not only learn by watching, but by DOING as well. Here are some of the projects you will create in our complete after effects cc master class First you will build a sample application (add in more about these and the other projects) Here are all of the Useful Skills you will learn in our complete course: Java Language Designing reach User Interfaces Debugging android applications YouTube and Google Maps APIs Databases Content Providers Networking in Android Handling Background Tasks Notifications Version control with Git and GitHub Tips for Publishing So much more _________________________________________________________________________ With the right mindset, understanding, and application, you will instantly begin learning how to become a professional android app developer from scratch. When we learn something new - we add it to the course - at no additional cost to you! This is a course that will continue to add more and more to every aspect of your life. _________________________________________________________________________ What I can't do in this Course.. I can't guarantee your success – this course does take work on your part. But it can be done! I am also not responsible for your actions. You are responsible for 100% of the decisions and actions you make while using this course. _________________________________________________________________________ It's time to take action! This course will not remain this price forever! Enroll Today! Every hour you delay is costing you money... See you in the course! Sincerely, Meisam & Joe Less
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[100% OFF] The Complete Typescript for Beginners From Zero To Hero 2020 Free

Every other course online teaches you the basic sy... Morentax and features of Typescript, but only this course will show you how to apply Typescript on real projects, instructing you how to build large, successful projects through example. Typescript is a 'super-set' of Javascript. That means that if you already know Javascript, you are ready to take this course. Typescript adds in several important features to Javascript, including a type system. This type system is designed to help you catch errors during development, rather than when you are running your code. That means you'll be twice as productive by catching bugs earlier in development. But besides the type system, Typescript also provides several tools for structuring large codebases and writing truly reusable code. ES6 is the 6th edition, officially known as ECMAScript 2015, and was finalised in June 2015. ES6 adds significant new syntax for writing complex applications, including classes and modules, but defines them semantically in the same terms as ECMAScript 5 strict mode. Browser support for ES6 is still incomplete. However, ES6 code can be transpiled into ES5 code, which has more consistent support across browsers. Typescript is a superset of Javascript that compiles to plain Javascript. It is also the main language used for Angular 2. ES6 | ES2015 | Typescript | ES6 Tutorial | ES2015 Tutorial | Typescript Tutorial | ES6 Tutorial for Beginners | ES2015 Tutorial for Beginners | Typescript tutorial for Beginners Less
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[100% OFF] Learn Next.js Free

SO, YOU’RE LOOKING FOR A COURSE THAT WILL TEACH ... MoreYOU NEXT.JS QUICKLY & IN A FUN MANNER? I’ve got just the course for you! Welcome, my name is Josh Werner with Learn Tech Plus and I’ve put this course together to help people just like you quickly master Next.js...Whether you’re a beginner or experienced with Next.js! If your goal is to become a master of Next.js, then this course is perfect for you. It will get you started on the right path and give you the knowledge and skills you need to master Next.js... Learning Next.js is Not only for Experienced users, but also everyone else. Because when you have mastered Next.js, you can get around Next.js quickly and increase your productivity! In short, a good understanding of Next.js is tremendously beneficial. Now, in this course, we'll go far beyond that. By the end of it, you'll have gained complete proficiency in Next.js even if you're currently a complete beginner! THAT’S RIGHT...NO PRIOR EXPERIENCE OR KNOWLEDGE IS REQUIRED! You don’t need any previous experience or knowledge to take this course. In fact, all you need is a desire to learn and master Next.js. This is not one of those courses that will throw too much at you at once and cause you to get overwhelmed. This is a course that you’ll not only learn so much from, but also enjoy the journey as you’re learning (which is a very important part of the learning process) The course will take you by the hand and teach you everything you need to know step by step and even put your knowledge to practice immediately by showing you how to setup an account with Next.js and How to use Next.js. On top of this, you will also get my continuous support as well to make sure you’re successful with my course. LEARN NEXT.JS BY DOING! (LEARN NEXT.JS FROM SCRATCH!) We will go step by step and cover Next.js. The goal here is to help you A) Setup a Free Account with Next.js B) Work with Next.js Here’s what we’ll cover in the course: 1. We’ll start from the very beginning and explain what Next.js is, why & how it’s used. 2. Introduce you to Next.js Less
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[100% OFF] CISSP Master Class: Become A CISSP Today Free

In this CISSP Master Class: Become a CISSP Today .... More I will help you with all the knowledge you need to pass the CISSP Certification. Hi, I am Sagar Bansal, . I have helped hundreds of students to pass CISSP Exam within their first attempt. For me, It is a simple exam in English, you need to answer the Questions from (ISC)²'s point of view, in their perfect world. Understand and answer every question from a Manager or a Risk Advisers point of view, NOT from an executive or as a techie. Most hands-on techies that fail the CISSP exams do so because they answer from a point of being reactive, not being proactive. The CISSP exam is using the Computerized Adaptive Testing (CAT) format, and is 3 hour long and will have 100-150 questions. Most people studying for the CISSP certification will use multiple books, video courses, and 3-5000 practice questions before taking the exam, this really is the path to success. Who is the target audience? Anyone wanting lean more about the CISSP certification! People wanting to grow their IT Security and Cyber Security knowledge New and experienced IT Security professionals Anyone wanting to break into IT Security Basic knowledge Wanting to pass the Information Systems Security Professional (CISSP) Certification Exam Wanting to learn about management level IT Security and Cyber Security What will you learn Prepare for the latest version of the Information Systems Security Professional (CISSP) Certification Exam Learn why you want to get your CISSP certification, what it can give you Where to start on your CISSP certification journey Learn why you want to get your CISSP certification, what it can give you Understand IT Security and Cyber Security from a management level perspective Less
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[100% OFF] ML for Business Managers: Build Regression model in R Studio Free

You're looking for a complete Linear Regression co... Moreurse that teaches you everything you need to create a Linear Regression model in R, right? You've found the right Linear Regression course! After completing this course you will be able to: · Identify the business problem which can be solved using linear regression technique of Machine Learning. · Create a linear regression model in R and analyze its result. · Confidently practice, discuss and understand Machine Learning concepts A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. How this course will help you? 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 technique of machine learning, which is Linear Regression 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. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: · Section 1 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation · Section 2 - R basic 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. · Section 3 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. · Section 4 - Data Preprocessing In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. · Section 5 - Regression Model 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, what are other variations to the ordinary least squared method 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 regression model in R will soar. You'll have a thorough understanding of how to use regression modelling 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 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 is the Linear regression technique of Machine learning? Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning? There are four reasons to learn Linear regression technique of Machine learning: 1. Linear Regression is the most popular machine learning technique 2. Linear Regression has fairly good prediction accuracy 3. Linear Regression is simple to implement and easy to interpret 4. It gives you a firm base to start learning other advanced techniques of Machine Learning How much time does it take to learn Linear regression technique of machine learning? Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 4 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 R 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 R Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture in R where we actually run each query with you. Why use R for data 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 Anyone curious to master Linear Regression from beginner to advanced in short span of time Less
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