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[100% OFF] Python and Flask Full-Stack Web Development for beginners Free

A full stack developer is a web developer or engineer who can develop both the front end and back end of a website or application. They can tackle projects that involve databases, building user-facing websites, or even work with clients during the planning phase of projects. Full stack web developers usually have some of the following front end and back end skill sets. Front End Skills ( This is used to build the client software or application) HTML CSS Bootstrap JavaScript JSON XML JQuery Angular React Back End Skills ( This is used to build the server software or application) Nodejs Expressjs REST SQL MongoDB Python Java C# ASP PHP RUBY C++ Python is a programming language. Python can be used on a server to create web applications. Flask is a Python framework for building lightweight and dynamic web applications. It helps speed up tedious behind-the-scenes development work, such as URL mapping, and offers more control to the developer to build applications Being a full-stack web developer equips you with the skill set to develop web based projects. You can prototype quickly and switch between front and back end development based on requirements. Projects we will create: Calculator Countdown timer Interactive Quiz Todo List Application URL Shortner Application Basic API Who this course is for: Beginners to Web Development Beginners to Python Beginners to Flask
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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] 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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[100% OFF] Front End Web Development For Beginners (A Practical Guide) Free

Learn Front End Web Development Student reviews... More: "Great Course" -Rich Helton "This is great training for beginners and aspiring front end web developers this is simple yet very very informative" -Leynard Caballero Villagracia "I think it is a great course for the beginning level." -Hoang Cong "Great course and perfect explanation specially for beginners." -Kenan Dedoviq "Easy to understand for beginners..." -Hiren Bechra "Yeah, it's been a good match for me definitely. The content and the way of teaching by the mentors is very effective and efficient. Any one can begin with building web pages, taking up this certain course" -Abhinav Anand ======================================================== Not sure yet? Just scroll down and watch the free preview lectures! Enroll with confidence! Your enrollment is backed by Udemy's 30-day, no-questions-asked, money-back guarantee! What are the requirements? · All information and resources are included in this course. What am I going to get from this course? · Learn how to hand code HTML and CSS · Work with Bootstrap 4 · Build 2 great projects to get you started on your Front End Web Development education What is the target audience? · Anyone who wants to dramatically increase their Front End Web Development Skills! Less
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[100% OFF] Adobe Illustrator CC 2020 MasterClass Free

This course will help you learn the basics as well... More as advanced levels of concepts and applications in Adobe Illustrator CC 2020. It covers real world applications of: 1. Logo Design 2. Packaging Design & 3D Mock-Up 3. Typography and Color Theory 4. Social Media Posting Also, the tools of Adobe Illustrator have been thoroughly explained that will help you in creating the designs with much ease. Salient Features of this Course: A comprehensive course consisting of 179 video lectures that are organized in a pedagogical sequence. Detailed explanation of all tools and commands used in the course. Step-by-step instructions to guide the users through the learning process. Practice Test You will get a Certificate of Completion after completing the course. 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 Angular 7 Boilerplate In Hindi (Get A CRUD Project) Free

Description This short course is designed to begi... Morenners having little idea of web applications and is based on a boilerplate project created by Angular 7 CLI. It introduces the versions of AngularJS to Angular2+. It also demonstrates the installation process to the first run of the app. Next, it tries to explain the folder structure and code from file to file. Who this course is for: Beginner in Angular and Node js from Hindi/Urdu speaking background Less
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[100% OFF] JavaScript, Bootstrap, & PHP - Certification for Beginners

A Comprehensive Guide for Beginners interested in ... Morelearning JavaScript, Bootstrap, & PHP Less
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[100% OFF] Digital Marketing (SEO, Google Ads, Google Analytics etc) Free

Although this course is for beginners, the knowled... Morege it provides is really advanced. It is full of tips and tricks as well as tested methods that will make you an expert in digital marketing and online advertising. Its curiculim includes: Blogging Social Media Google Ads SEO Google Analytics E-commerce Optimization Conversion Optimization Each section of the course is a step by step guide that not only gives you usefull instructions about the topic it addresses but also tried methods that you can easily use to bring profits to your business and to be a successful digital marketing professional. Less
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[100% OFF] Java Parallel Computation on Hadoop Free

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

Do you need to master the art of front-end develop... Morement? Look no further. This course is your complete beginners guide to developing cutting-edge web pages that are fully mobile responsive. The course branches into three sections. 1. Explore HTML5 Learn the composition of a web page and how a web browser interprets html code to display the visual elements of a page. Learn the core fundamental aspects of HTML syntax, to ensure you are well prepared for the remaining sections ahead. 2. Explore CSS3 Learn to add stunning design elements to really make web pages visually aesthetic. Learn a broad range of CSS attributes to make web pages completely mobile responsive, even on the trickiest of devices such as phones and tablets. 3. Explore JavaScript Dive into adding interactive client-side functionality using JavaScript. JavaScript is an extremely powerful front-end programming language that can really help take web pages to another level. Learn how to add client-side validation to forms, animate images and objects, and manipulate both HTML and CSS. Who this course is for: Anyone who needs to learn to code Anyone who needs to build a website Less
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[100% OFF] The Complete PHP 7 Guide for Web Developers Free

PHP levels up! The world’s favorite back-end pro... Moregramming language now gets a new version packed with new features and significant upgrades. PHP is touted as the de facto language for server-side scripting in app development and even websites. The popularity of the language has also made it extremely useful as a general-purpose programming language. The latest version gets a huge boost in terms of performance, speed, new scalar type declarations and even a new operator. The performance and speed boost comes from the new turbocharged Zend Engine 3, which also allows PHP7 to consume half as much as memory as PHP5 and support more component users at the same time. In addition the new engine, other changes to PHP7 include replacing fatal- or recoverable-level legacy PHP error mechanisms with object-oriented exceptions, inclusion of left-hand-side expressions, unmaintained or deprecated server application programming interfaces (SAPIs) were removed, list() operator now includes support for strings, new language features such as return type declarations for functions, and support for the scalar types in return type and parameter declarations. With so much power under the hood, it is no wonder a lot of developers are shifting to PHP7. With this course, you too can learn how to supercharge your apps and websites. Our complete PHP 7 guide is the perfect course to get you started with the amazing features included into the already powerful programming language. It comes loaded with everything you need to know to upgrade to the fancy new iteration. The course will cover not only the fundamentals of PHP 5, but will also familiarize you with the new features and changes in PHP 7. These new additions may not seem like much, but they will improve your coding by four fold, especially ones such as unserialise function (which will accept another optional parameter) and password hash function (that now automatically generates a secure salt, rather than accept a given salt). The PHP 7 tutorial also includes breaking down features such as the new spaceship operator, Throwable Interface, Handling Fatal Errors, Generator Delegation, Anonymous Classes, Fetching Data & Error Handling and so much more. That’s not all. Some sections even includes quizzes to help you test your understanding and a project that will allow you to become comfortable coding in PHP 7. In this course, you will learn: Introduction to PHP, its fundamentals and its environment What is new in PHP 7 and how it differs from PHP 5 New features such as spaceship operators, error handling, new declarations, new classes, new functions, etc. Fully design a GoodQuotes app project that will allow you to add, edit and remove data Less
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[100% OFF] Complete Vue.js 3 (Inc. Composition API, Vue Router, Vuex) Free

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