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[100% OFF] Agile Project Management 200+ Tools with Kanban Scrum Devops

Learn 200+ Tools of Agile + Scrum + Kanban + Lean & more. Only Agile Course that includes DevOps & iCAN Certification
100% success rate

[100% OFF] Complete MySQL Course: Beginner to Advanced

Complete MySQL course. Learn MySQL from scratch an... Mored go from beginner to advanced in MySQL. Less
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[100% OFF] Learn C# and make a videogame with Unity 2020 Free

Welcome to Learn C# and make a videogame with Unit... Morey 2020 : beginner to pro. If you are here, it is probably because you like video games and want to learn how to make your own video games. In addition, it is likely that you know how to draw pictures, design your own characters, your enemies, animations, scenarios, have thought out game mechanics, ... but you lack one thing: KNOW HOW TO PROGRAM! You don't have to be ashamed. I myself did not create my first video game until I was 23 years old and since then, I had been studying mathematics. No one is born taught and that is why we are here: so that you can learn to make your own video game in Unity with the C# (c sharp) programming language, a simple language that is easy to learn, and that will give you many possibilities to expand your knowledge. In this course, we will start with the basics and fundamentals: programming. Programming is like the language of computers. Right now you and I communicate in a common language: English, which we both understand perfectly and that is why we can be exchanging information: I write and you read. Probably if I were writing this to you in French or German, then you wouldn't even understand what I am telling you, not because it is easier or more difficult, but because I would be using a different language. Computers and mobile devices themselves have their own language, their jargon, their slang. That is where we will begin our learning path together, young Padawan: by learning what a program is, how it manages to communicate with the graphic part, and how we, by pressing an arrow or a button, can make the computer understand what we want to say . We are going to learn together basic programming aspects such as: • Variables and data types • Methods or code snippets • Object and object-oriented programming • Decision operators • Collections, arrays, and object lists • The iteration operators With all this, it will be like the cook who leaves the academy and has been taught to peel, cut, cook, boil, season, season ... food. But even if we know how to fry an egg, we all know that fried eggs made by us, by our mothers, or by our grandmothers do not taste the same. Therefore, once we know the tools and basic principles of programming, we will put them into practice with a real project, our first 2D video game: a 2D platform where the objective will be to dodge enemies while collecting the maximum number of coins. It will be a simple video game but we will see its true potential in everything that can really be done from what we know: • We will learn how to plan the Game Design and to split up the development stages of our video game • We will learn what a prefab is and how they are used to create scenes for a video game • We will create our own menus and the game information HUD • We will learn typical mechanics and gameplay such as infinite scrolling or the physics of a video game And really with all this, believe it or not, you will have made your first video game !!! I would also like you to introduce yourself when you start, say where you come from and what knowledge of video game development you have, and share in the course's forum what type of video game you would like to make. Please refrain from impossibilities like "I want to make a Dark Souls, a Witcher, a Metal Gear" and so on. These games have studies of more than 400 expert developers and we are looking to get started. The goal is for you to make your first video game, and as such, it should be an achievable and easy goal to carry out so you don't get discouraged. Well, I've already warmed up and I'm looking forward to learning C # together and starting to create our videogame in Unity. So don't think twice and sign up now to start your career as a video game developer with Unity 2020 and C #. Less
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[100% OFF] JavaScript + ES6 + ES7 + ES8 + ES9 -> The Complete Guide Free

Welcome to my new course: 'JavaScript + ES6 + ES7 ... More+ ES8 + ES9 -> The Complete Guide'. This course starts from scratch, you neither need to know any pre-requisite programming knowledge nor any language expertise. From the environment Setup to Development, this course covers almost each and every concept required to become an expert JavaScript developer. Having a deep-dive of the concepts in this course, you'll learn all about: JavaScript: Development Environment Setup Fundamentals Operators Control Flow Arrays Functions Objects Prototypes Patterns To Create Objects Error Handling Miscellaneous Objects Debugging in Chrome Shopping Cart Project: Putting All Together ES6: Features with their Syntax Modules Class Symbols Iterators & Generators Promises Maps & Sets Extensions of Built-in Objects Reflect API Proxy API ES7: New Features ES8: New Features ES9: New Features World Weather Project: Putting All Together Course Roundup and many more... This course will assist: In becoming an proficient 'JavaScript Developer' and Inclining towards the learning/ understanding of any client/ server side programming language. Why JavaScript? JavaScript (JS) is a lightweight, interpreted or JIT compiled programming language with first-class functions. Most well-known as the scripting language for Web pages, many non-browser environments also use it, such as Node.js and Apache CouchDB. JS is a prototype-based, multi-paradigm, dynamic scripting language, supporting object-oriented, imperative, and declarative (e.g. functional programming) styles. The standard for JavaScript is ECMAScript. As of 2012, all modern browsers fully support ECMAScript 5.1. Older browsers support at least ECMAScript 3. On June 17, 2015, ECMA International published the 6th major version of ECMAScript, ECMAScript 2015. Since then, ECMAScript standards are on yearly release cycles. This course covers up to the latest version of JavaScript, which is currently ECMAScript 2019. Why we should learn JavaScript & the reasons are: Open Source Language: Freely Available. Much more than Scripting programming language. Scalability and Performance Features. Evolving steadily & ES6 represents the BEST !!! Much JavaScript-related innovation in the Market. Base of most of the frameworks like Angular. Supported by a broad coalition of companies. Who this course is for: Be a JS Ninja by understanding the most popular programming language in the world viz. JavaScript. Newcomer as well as experienced having the experience with JavaScript (ES5/ ES6) and know the basics of the language. Understanding the new Features and Additions, brought by ES6 (including ES5) to JavaScript. Understand the basics to move head with the popular libraries/frameworks like jQuery, React, Angular or NodeJS. Understand how JavaScript actually works internally. Less
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[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
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[100% OFF] How To Make Games with Gamemaker Studio 2 using GML Free

What Is Included In This Course? · Learn how t... Moreo make a PC game with GMS2 · Learn how to program games using GML (Gamemaker Language) · Be trained by a serial entrepreneur who does this for a living! This course does not cover animation or sound, we are focussing primarily on game maker language programming and teaching you pieces of code that you can then take and convert for use in your own games. Here’s what some of my students have to say about my other courses: "Lee ably shares a step-by-step means of becoming a profitable book publisher using Amazon KDP" "Very helpful course, thanks Lee! Will be looking out for any follow-up courses Lee puts out to help us publishers become more profitable." "Found it very useful as someone with almost no idea about how Kindle publishing works. The instructor is quite straightforward with not just information but also his own experiences and expertise" "Straightforward, action-based and no BS (something hard to find in "make money online" courses these days lol)" · 2020 course!. no outdated content! · Get a demonstration of how to get started with game design Less
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[100% OFF] Logo Design - Design a Logo in Photoshop for beginners Free

Do you want to learn how to create your very first... More logo? In this course you'll find the key elements that will make you a designer ready for the market. With step-by-step lessons guiding you through this wonderful journey, you'll learn the fundamentals of logo design, why it's so important to write down your ideas and needs and the most important part: You'll have fun doing what is one of the most important parts of a brand image: A logo! We'll sketch in paper our ideas, then we'll take them into the digital canvas and create our very first logo! Who this course is for: Anybody who wants to design a logo for its own or a client Less
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[100% OFF] Data Science: Supervised Machine Learning Bootcamp in Python Free

This course focuses on one of the main branches of... More Machine Learning that is Supervised Learning in Python. If you are not familiar with Python, there is nothing to worry about because the Lectures comprising the Python Libraries will train you enough and will make you comfortable with the programming language. The course is divided into two sections, in the first section, you will be having lectures about Python and the fundamental libraries like Numpy, Pandas, Seaborn, Scikit-Learn and Tensorflow that are necessary for one to be familiar with before putting his hands-on Supervised Machine Learning. Then is the Supervised Learning part, which basically comprises three main chapters Regression, Classification, and Deep Learning, each chapter is thoroughly explained, both theoretically and experimentally. During all of these lectures, we’ll be learning how to use the different machine learning algorithms to create some mind-blowing modules of Machine Learning, and at the end of the course, you’ll be trained enough that you would be able to develop you own Recognitions Systems and Prediction Models and many more. Let's get started! Who this course is for: Those who are interested in AI and Machine Learning Those who have basic knowledge of any programming language Those who want to be create awesome Machine Learning and AI modules And those who want to earn some handsome amount of money from Machine Learning Field in Future Less
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[100% OFF] Python Complete Course For Python Beginners Free

Learn Python From Beginner To Advanced Level I... Moreve created thorough, extensive, but easy to follow content which you’ll easily understand and absorb. The course starts with the basics, including Python fundamentals, programming, and user interaction. The curriculum is going to be very hands-on as we walk you from start to finish becoming a professional Python developer. We will start from the very beginning by teaching you Python basics and programming fundamentals, and then going into advanced topics and different career fields in Python so you can get real-life practice and be ready for the real world. The topics covered in this course are: * Beginner to Expert Python contents: Array implementation File methods Keywords and Identifiers Python Tuples Python Basics Python Fundamentals Data Structures Object-Oriented Programming with Python Functional Programming with Python Lambdas Decorators Generators Testing in Python Debugging Error Handling Regular Expressions Comprehensions Modules See you inside the course! Less
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[100% OFF] Web Development Masterclass - Complete Certificate Course Free

This course offers a comprehensive look into the e... Morentire web development process - from local server configuration using WAMP and MAMP (Apache, MySQL, PHP) to production deployment using the latest web technologies including: LAMP Stack (Linux, Apache, PHP and MySQL) for Ubuntu, HTML5, CSS, Bootstrap, JavaScript, jQuery, XML, and AJAX. The content is ideal for those interested in working as a web developer, launching a web application, or devoted enthusiasts. The concepts explored in this course are suitable for individuals of all skill levels. Each module starts with the fundamental concepts and gradually transitions into more advanced material. What makes this course unique? This course is specifically intended to teach students to develop web applications using the most efficient methods and the latest web technologies. Through live demonstrations we teach the importance of using scalable cloud hosting infrastructures during the development process. This includes small informational websites of only a few pages to advanced 'big data' style, dynamic web applications. We go through several live examples of web development and Linux based server configurations on popular Cloud hosting providers such as Linode. Section 1 & 2 (Introduction, How the Internet Works) The course begins with with an overview of learning objectives. We then explore how the internet works, including the composition and transmission of data packets over both local and wide-area Networks. Next we take a look at the HTTP and HTTPS protocols as applicable to client and server side communications - including DNS Lookups. This section will also teach students how email exchange works over SMTP and IMAP. The role of Network Ports and Firewalls are also introduced. Section 3, 4: (The Web Development Process, Planning a Website) Students will gain an understanding of all the steps involved in the web development process. Section 5: Web Hosting and System Requirements We conduct an in-depth case analysis of web hosting solutions including: Shared, Virtual Private, Dedicated and Cloud Hosting. We provide an overview of various packages offered by different web hosting companies and weigh the pros and cons of each one. Students are also introduced to the importance of web server scalability and the advantages of cloud hosting over traditional hosting services. We also examine the infrastructure requirements of popular, resource intensive applications such as Netflix and Facebook to emphasize the importance of avoiding costly mistakes in the initial stages of development. Section 6: Domain Names We provide an overview of the domain name registration process. This includes the role of registrars and TLD (Top Level Domain) administration by ICANN. ccTLD's (Country Code Top Level Domains) are also explored. Students gain hands-on experience with the registration process using popular domain registrars. We provide a comparison of different registrars and register a sample domain name using GoDaddy. Other topics of discussion include: domain name privacy and administration, auto-renewal, domain forwarding, and name server assignment. Section 7: Testing Environment Students are introduced to configuring a local testing server on a PC or MAC system using WAMP (Windows) or MAMP (MAC). The lessons provide a detailed guide on installation of the packages and an overview of the interface. Section 8: Production Environment Students acquire the skills needed to optimally configure a live production environment for securely hosting web applications on a cloud server (Linode). The section starts with an overview of different cloud hosting providers such as Amazon Web Services, Microsoft Azure, Linode and Digital Ocean. We then provide a live demonstration of server setup using Linode. This includes deployment of Ubuntu and installation of LAMP Stack (Linux) on the Linode Server using remote access consoles such as Terminal (MAC) and PuTTY for Windows. Students gain an in-depth knowledge of server maintenance, file and directory commands. We also explore SSH Authentication for multiple users and file permissions to reduce security vulnerabilities. Remote Desktop connections for server administration are taught, using Tight VNC (Windows) and RealVNC (MAC). A thorough overview of the Linode server management console is conducted, to show students how to scale servers, change root passwords, manage the DNS Zone file and create back-ups. Section 9: FTP Setup Students learn the role of an FTP (File Transfer Protocol) client to connect to a remote server, through a local machine. We provide a live demonstration on file upload using FileZilla and explore the FileZilla Interface for connection management. Section 10: HTML Development This section explores HTML. HTML is a key fundamental building block when learning to develop websites. Students initially learn the basics of HTML page structure and gradually transition into working with spacing, text formatting, lists, images, videos, links, anchors, tables, forms and much more. We include several projects, where students are shown first-hand, how to develop and code html web pages from scratch. Section 11: CSS Development Students learn to use CSS to create stylish, responsive web page layouts. We start by discussing the parts and types of CSS rules, followed by CSS classes, DIVS, and ID's. Students gain significant exposure to various formatting options, including margins, padding, font properties, backgrounds, transparency, positioning, link styling and CSS tables. This section includes a hands-on web development project where students utilize the concepts explored in the lesson. Section 12: Document Object Model (DOM) As a precursor to JavaScript, we explore the DOM (Document Object Model). We explain its usage and application in manipulating HTML and CSS elements. Section 13: JavaScript Development We introduce students to JavaScript coding to add interactivity to static HTML and CSS pages. The section starts with discussing JavaScript placement, using both internal and external scripts. We then take a look at JavaScript output, variable declarations, arithmetic operators, objects, strings, math functions, arrays, conditional statements, loops, functions and events. Students also go through two hands-on projects that will further solidify their knowledge of the concepts explored in the lessons. Section 14: JQuery Library We explore the jQuery library and many of its unique features, used to create stunning, animated web pages. We start with the basics, from embedding jQuery into web pages to working with Selectors. Students then explore working with events, toggling, fading, sliding, and other forms of animation. The lessons also teach students how to work with callbacks, chaining, dragables, accordion menus and many other functions. Section 15: Bootstrap Framework Students are taught to use the Bootstrap framework for responsive, mobile-first - front-end development. As one of the leading open-source development libraries, Bootstrap is an essential part of the developers coding arsenal. The section begins with teaching students how to include essential Bootstrap files into web pages. It then explores the Bootstrap Grid system and popular layout strategies for optimal cross-device and cross-browser compatibility. Students gain exposure to just about every Bootstrap component, from typography, tables and images, to jumbotrons, wells, alerts, buttons, glyphicons and progress bars. The section includes several hands-on exercises that will walk students through the process of creating stunning layouts, complete with modals, carousels, popovers, drop-down menus, forms and much more. Section 16: PHP Development Students gain exposure to creating dynamic web applications and functionality using PHP. We start with the basics, including variable declaration and data output. The lessons then transition into working with objects, conditional statements, loops, functions, arrays and form validation. Section 17: MySQL Database Integration Students learn to unleash the true power of web application development with MySQL database integration. We start with the basics from database and table creation, to user management. Next we explore the execution of commonly used SQL statements for database modification and administration. Students are also taught how to create database connections and execute SQL statements from PHP scripts. Section 18, 19 (XML, AJAX, & Development) We explore XML and AJAX integration to create dynamic content, without page refresh. The lessons cover several hands-on training exercises that will encompass many of the exciting functions AJAX offers. Students will build a website complete with database integration, registration forms with server-side validation, user authentication, and a SMTP-based contact submit form. Section 20: Google Apps for Work Students learn to create a customized business email address using Google Apps for Work. Email administration is also explored, along with alias creation. 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] 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] GraphQL from Scratch with React Node Free

Looking to Build Lightning Fast Realtime Web Apps ... Moreusing GraphQL with MERN Stack? This course is your starting point to be able to build Modern Web Apps for present and future. Get started with very easy to follow lectures to make yourself comfortable with GraphQL and learn how you can combine it with Node and MongoDB to build GraphQL Server and also learn to combine with React to build GraphQL Client. 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. 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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. 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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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