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[100% OFF] Complete Digital Marketing Course in Hindi with Google Free

Best Complete Digital Marketing Course in Hindi with Google (2020). If you want to learn from Basic to Advance Complete Digital Marketing Course in Hindi With Google & want to become an internet marketing expert and learn digital marketing strategy. SEO in Hindi, Social Media Marketing in Hindi, SEM, Google Ads in Hindi, YouTube marketing, Email, TikTok Ads India in Hindi & TikTok Marketing, Facebook Marketing in Hindi, Copywriting, content marketing, video marketing, quora marketing & Inbound marketing. This is one of the most comprehensive digital Marketing Courses in Hindi With Google available in India. We'll cover: Email Marketing. Build a mailing list of 1000 individuals in 30 times from scratch. Advertising is the action of sending a message, normally using email. In its broadest sense, each email delivered to a current or prospective customer might be considered advertising. It entails using email ask an organization to send advertising, or solicit donations or sales. 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Who this course is for: Entrepreneurs, business owners, bloggers, YouTubers, social media fans Website owners who want to increase web traffic, conversions, and sales Business owners who want to use digital marketing (social media, content marketing, YouTube, and many other strategies) to grow their business Anyone wanting to do your own marketing - and not waste money Who this course is for: This Digital Marketing Course in Hindi with Google is best for Students, Entrepreneurs, Business owners, Bloggers, YouTubers, Social media influencers or Digital Curious हिंदी में सीखने जिसे जायदा समझ आता हो Website owners who want to increase web traffic, conversions, and sales This Digital Marketing Course in Hindi is best for Business owners who want to use digital marketing (social media, content marketing, YouTube, and many other strategies) to grow their business Anyone wanting to do your own marketing - and not waste money This Digital Marketing Course in Hindi With Google is best for Youth who wants to start career in Digital Marketing Freelancer or Part Time job seeker

[100% OFF] Data Structures with Python Free

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[100% OFF] 50-point SEO Audit 2020 To Test Your Site Or As Freelancer Free

Learn to do an SEO audit with my comprehensive 50-... Morepoint checklist. You will be able to not only identify problems on your site, but I'll show you how to fix them on your own. My goal is for you to be able to find and fix SEO problems on your site. You will only need to hire freelancers to do the intermediate to advanced technical work that is beyond SEO. Sometimes you will encounter SEO issues that can be fixed by a software engineer or a network engineer. In those cases, I recommend that you hire a freelancer. But in most cases, I show you how to fix the SEO problems on your own. After The Audit, You Will Have: After performing the audit, you'll have a list of improvements and an ability to implement: Making your site mobile Improving site load speed Content quality Optimal content strategy with full site crawling, indexing, and ranking potential Sell SEO Audit Services As A Freelancer Or As An Agency Service SEO audits are a popular service to provide. After this course, you'll be able to impress potential clients that you will give them an impressive and comprehensive site audit with many actionable items they can implement. After that, you will be able to implement most of those action items and make more revenue from that. Who this course is for: Website owners, entrepreneurs, freelancers Less
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[100% OFF] Apache kafka™ - A Complete Hands-on Kafka Developer's Guide Free

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

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[100% OFF] Mastering Agile Scrum Workshop Free

UPDATE FOR 2020 with Tanmay's latest knowledge on ... MoreAgile Scrum Certification! This course is about learning the latest Agile Scrum methodology for the Software development field. In this course, you will learn all other Agile methodologies along with detailed information on Scrum. By this course, experienced project managers can grow in their careers and get the next level of opportunity as an Agile Scrum Master and the junior team members can learn the process of the Agile Scrum methodology. The biggest target audience is at any experience level who wants to learn Agile Scrum in detail and apply in the career!!! Who this course is for: Project Managers who would like to be an Agile Scrum Master Qualifying Agile Scrum Master Certification Exam People who want to learn about Agile Scrum Developers, BAs, Architects, Analysts, Designers, Managers, etc Less
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[100% OFF] Learn Ethical Hacking By Hacking Real Websites Legally Free

If you want to learn ethical hacking the fun and e... Moreasy way like playing a computer game, then this course is for you. There exists on the Internet several websites which allows people to hack and learn hacking. These websites typically have different levels from easy to advanced. By hacking each level and gradually levelling up, you will learn hacking. Traditionally, ethical hacking is taught by installing virtual machines on your PC and hacking the virtual machines inside your PC. This way of learning hacking can be pretty boring as there are no challenges or rewards. Also it can be quite laggy unless you have a high end machine. In addition, some people find learning Linux and installing virtual machines too troublesome. In this course, I will show you an alternative way, which is more fun and where you will enjoy the thrills of hacking real websites legally and solving each level and going from easy and gradually to more advanced levels - just like paying online games. The website we will be hacking is HackThisSite!! which is a popular hacker's playground where hackers play hacking games reminiscent of CTF (Capture the Flag) and learn at the same time. However, without some help and guidance you will find it quite challenging. Hence this course. I will walk through each level from Basic to Realistic You do not need to go through the hassle of installing virtual machines or Linux on your computer. All you need is a PC. In this course I am only using a Windows PC to do hacking. Great way to learn Web Development & Information Technology (IT): If you had always wanted to learn HTML, Javascript, PHP, MySQL, etc, what better way than to learn how to hack. So, I invite you to learn to hack and in the process, you will also learn web development and IT. This is much more fun & exciting than learning it the traditional way - the boring way through formal IT courses and books. You will be killing 2 birds with one stone, i.e. learning how to hack and also learn IT. So let's get started in your journey of hacking to learn. Bug Bounty: Last, but not least, the site creators of HackThisSite encourage you to literally hack their site to explore the security of their site and they will reward those who responsibly disclose their exploit with an entry into the HackThisSite hall of fame. This course is backed by a 30-day money back guarantee. So, go ahead and enroll in this course now and start hacking the fun and easy way! Who this course is for: Web developers who have no knowledge on hacking Students who want to learn hacking the fun way by hacking real websites (legally) Those who enjoy computer games and want to try hacking as a game 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] 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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