This is ovberwriting?

[100% OFF] Image Recognition using CNN: Keras & TensorFlow in R Studio Free

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in R, right? You've found the right Convolutional Neural Networks course! After completing this course you will be able to: Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course. If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create an image recognition model using Convolutional 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 300,000 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 (Section 2)- Setting up R and R Studio with 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 (Section 3-6) - ANN 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 (Section 7-11) - Creating ANN model in R In this part you will learn how to create ANN models in R. 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. 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 (Section 12) - CNN Theoretical Concepts In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models. In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model. Part 5 (Section 13-14) - Creating CNN model in R In this part you will learn how to create CNN models in R. We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part. Part 6 (Section 15-18) - End-to-End Image Recognition project in R In this section we build a complete image recognition project on colored images. We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition). By the end of this course, your confidence in creating a Convolutional Neural Network model in R will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition 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 Deep Learning journey Anyone curious to master image recognition from Beginner level in short span of time

[100% OFF] Learn Complete Python-3 GUI using Tkinter Free

Through this Course master in Python Tkinter &... More Create real world projects! Less
Expired
160 used

[100% OFF] The Complete Android App Development Masterclass: Build Apps Free

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

[100% OFF] The Complete Guide Angular 8 for Java Developers 2020 Free

Angular is a TypeScript-based open-source front-en... Mored web application platform led by the Angular Team at Google and by a community of individuals and corporations to address all of the parts of the developer's workflow while building complex web applications. Angular is a complete rewrite from the same team that built AngularJS. Angular is a framework for building client applications in HTML and either JavaScript or a language like TypeScript that compiles to JavaScript. Angular combines declarative templates, dependency injection, end to end tooling, and integrated best practices to solve development challenges. Angular empowers developers to build applications that live on the web, mobile, or the desktop. This course is for existing Java developers who want to learn the popular Angular framework for developing front-end Web interfaces. In the course we cover all of the main principles of building websites in Angular, including the Typescript Language. This is taught with reference to Java and Java web technology examples, making it a very quick and easy way for Java developers to upgrade their skills to Angular. No prior knowledge of Javascript or Typescript is needed, although some basic HTML is useful. Who this course is for: web developers, .net developers, java developers, python developers Less
Expired
78 used

[100% OFF] SVM for Beginners: Support Vector Machines in R Studio Free

You're looking for a complete Support Vector Machi... Morenes course that teaches you everything you need to create a SVM model in R, right? You've found the right Support Vector Machines techniques course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines. Why should you choose this course? This course covers all the steps that one should take while solving a business problem through SVM. 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. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy Who this course is for: People pursuing a career in data science Working Professionals beginning their Data journey Statisticians needing more practical experience Anyone curious to master SVM technique from Beginner to Advanced in short span of time Less
Expired
28 used

[100% OFF] Decision Trees, Random Forests, AdaBoost & XGBoost in Python Free

You're looking for a complete Decision tree course... More that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right? You've found the right Decision Trees and tree based advanced techniques course! After completing this course you will be able to: Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning. Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result. Confidently practice, discuss and understand Machine Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost. Why should you choose this course? This course covers all the steps that one should take while solving a business problem through Decision tree. 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 decision tree based model, which are some of the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: Section 1 - 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 2 - Python basic This section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Pre-processing and Simple Decision trees In this section you will learn what actions you need to take to prepare it 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. In the end we will create and plot a simple Regression decision tree. Section 4 - Simple Classification Tree This section we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python Section 5, 6 and 7 - Ensemble technique In this section we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. In this course we will discuss Random Forest, Baggind, Gradient Boosting, AdaBoost and XGBoost. By the end of this course, your confidence in creating a Decision tree model in Python will soar. You'll have a thorough understanding of how to use Decision tree 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 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 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 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 where we actually run each query with you. Why use Python for data Machine Learning? Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Who this course is for: People pursuing a career in data science Working Professionals beginning their Data journey Statisticians needing more practical experience Anyone curious to master Decision Tree technique from Beginner to Advanced in short span of time Less
Expired
61 used

[100% OFF] Core Java Interview Bootcamp To Handle Interviews With Confidence. Free

Hi everyone :) Having you stop here simply mean... Mores you are either preparing for a core java interview or want to know the most widely asked questions in the room. This course is intended for you folks, perfectly. The course basically aims at providing an in-depth explanation to most continuously asked questions through very engaging animated PPTs. Whether it's string, or exceptions, or multi-threading, I have got your back on most asked questions from these topics. The much important thing is the clear and concise clarity in explanations that you will get from this course. Note: 1. The course is not intended for complete beginners! You need to have at least basic to intermediate knowledge of core java. 2. I will keep on adding brand new most asked questions regularly. So, this course is not just limited to a fixed number of questions! Rest assured, you will enjoy this awesome Bootcamp with me. See you in the course... Over n Out :) Less
Expired
118 used

[100% OFF] Java Parallel Computation on Hadoop Free

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

[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
Expired
22 used

[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
Expired
121 used

[100% OFF] JavaScript, Bootstrap, & PHP - Certification for Beginners

A Comprehensive Guide for Beginners interested in ... Morelearning JavaScript, Bootstrap, & PHP Less
Expired
68 used

[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
Expired
72 used

[100% OFF] Linux Shell Scripting: Bashing, Automating Commands, Updated Free

Welcome to this Comprehensive Linux Shell Scriptin... Moreg Laser Targeted Course "Updated”. In short, this is the one stop shop for everything you need to become a Master in Shell Scripting. With 70+ Long, but Laser Targeted Videos, over 21 packed hours, we will leave no stone unturned. Are you ready to start your path to becoming a Master in Shell Scripting and learn one of employer's most demanded skills for 2020 and beyond? This is by far the most comprehensive, yet condensed and straight-forward, course bundle for Linux Shell Scripting on Udemy! Whether you have never had any knowledge on Shell Scripting before, already know some basic skills, or want to learn about the advanced features of Shell Scripting, this is the course you are looking for! Don’t miss this Limited Time Offer. ACT NOW! You will Learn by Practice: By the end of this Unique Course, you will go from #Newbie to #Advanced as a #Shell_Scripting_Expert. Here is what you’ll learn: Chapter 01 - Introduction 0101 - About this Course 0102 - Course Structure 0103 - What is the UNIX Shell 0104 - Which Shell 0105 - What is a Shell Script Chapter 02 - Your First Shell Script 0201 - A Basic Script 0202 - The echo Command 0203 - The read Command 0204 - Shell Basics Revisited 0205 - Special Characters 0206 - Comments 0207 - Chapter Exercises 0208 - Exercise Solutions Chapter 03 - Running a Shell Script 0301 - Running a Script on the Command-line 0302 - Running a Script from within vi 0303 - Your PATH and bin 0304 - Script Interpreters 0305 - CGI Scripts Chapter 04 - Shell Programming Features 0401 - Shell Variables 0402 - Environment Variables 0403 - The Trouble with Quotes 1 0404 - The Trouble with Quotes 2 0405 - Grouping Commands 0406 - Line Control 0407 - Chapter Exercises 0408 - Exercise Solutions 0409 - Introducing the Course Project 0410 - Course Project Solution Chapter 05 - Conditional Code 0501 - True and False 0502 - Conditional Command Execution 0503 - The if Statement 0504 - The else Clause 0505 - The elif Clause 0506 - Using test 1 0507 - Using test 2 0508 - Using test 3 0509 - The case Statement 0510 - Chapter Exercises 0511 - Exercise Solutions Chapter 06 – Loops 0601 - The while Loop 0602 - break and continue 0603 - Numerical Calculations 0604 - The for Loop 0605 - Chapter Exercises 0606 - Exercise Solutions Chapter 07 - Text Processing 0701 - About Filters 0702 – grep 0703 - Regular Expressions 0704 – sort 0705 – sed 0706 - awk 1 0707 - awk 2 0708 - Chapter Exercises 0709 - Exercise Solutions Chapter 08 – Functions 0801 - Program Structure 0802 - Defining and Calling a Function 0803 - Function Parameters 0804 - Function Return Values 0805 - Functions in Other Files 0806 - Case Study- The yesno Function 0807 - Chapter Exercises 0808 - Exercise Solutions Chapter 09 - Command-line Parameters 0901 - Using Command-line Parameters 0902 - Using shift 0903 - Using set - - Command 0904 - Using IFS 0905 - Usage Messages 0906 - Chapter Exercises 0907 - Exercise Solutions Chapter 10 - Advanced Scripting 1001 – Debugging 1002 - Default Values for Variables 1003 - Temporary Files 1004 - Preventing Abnormal Termination 1005 - Chapter Exercises 1006 - Exercise Solutions 1007 - The End Chapter 11 - Additional Content Part 1: Exercises and More 1101 – Please download the attached Exercises Files 1102 – Learn EMACS 1103 – Learn VI and Vim 1104 – Very Valuable Documentation Additional Content Part 2: Grand Finale Bonus Lectures. Enjoy the Benefits You could also end up using these skills in your work for Your #Clients, and much more. You'll Also Get: ✔ Lifetime Access to course updates ✔ Udemy Certificate of Completion Ready for Download ✔ A responsive instructor in the Q&A Section ✔ This courses’ bundle comes with a 30 day money back guarantee! If you are not satisfied in any way, you'll get your money back. So wait no more! Learn Linux Shell Scripting, increase your knowledge, become a Shell Scripting Expert and advance your career all in a fun and practical way! I really hope you found this course valuable, but either way, please leave a review and share your experience... Less
Expired
143 used

[100% OFF] How to build successful project strategy Free

You're in the right place. You can learn through t... Morehis course how to start, build and implement successful project strategy. You will get advice how to measure and follow-up your project strategy. This will certainly make your current or coming project successful from the beginning. This course will help you 1. Understand project strategy principles 2. Start your project strategy from zero, linked to enterprise expectation 3. Build successful project strategy according to planned objectives 4. Implement planned and selected strategy into practice 5. Measure your success, recognize problems before failure 6. Avoid mistakes from start until end. Have you had problems in your project scope, schedule or budget? Have you ever thought there could be better ways to success in projects? Enroll this course to learn more, be more successful and take your projects into next level. It's important to understand both enterprise and project KPIs (Key Performance Indicators) and how they link to each other for expected outcomes. Project management is a function which will glue together many departments, teams and people for desired outcome. To make your project successful it needs to be strategic. This course is designed for beginners and more advance project managers or project members. It will also help stakeholders like sponsors to understand how projects fail or success. You can take tips and tricks from this course into your toolbox and create a success! Enroll today! Who this course is for: Project team members Project managers Project sponsors/other stakeholders Less
Expired
25 used

[100% OFF] Image Recognition using CNN: Keras & TensorFlow in R Studio Free

You're looking for a complete Convolutional Neural... More Network (CNN) course that teaches you everything you need to create a Image Recognition model in R, right? You've found the right Convolutional Neural Networks course! After completing this course you will be able to: Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course. If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create an image recognition model using Convolutional 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 300,000 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 (Section 2)- Setting up R and R Studio with 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 (Section 3-6) - ANN 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 (Section 7-11) - Creating ANN model in R In this part you will learn how to create ANN models in R. 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. 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 (Section 12) - CNN Theoretical Concepts In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models. In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model. Part 5 (Section 13-14) - Creating CNN model in R In this part you will learn how to create CNN models in R. We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part. Part 6 (Section 15-18) - End-to-End Image Recognition project in R In this section we build a complete image recognition project on colored images. We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition). By the end of this course, your confidence in creating a Convolutional Neural Network model in R will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition 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 Deep Learning journey Anyone curious to master image recognition from Beginner level in short span of time Less
Expired
56 used

[100% OFF] Ultimate Hands-On English Phrasal Verb Guide: Spoken+Written Free

If you want to be fluent in English and sound nati... Moreve, you need to know phrasal verbs well. There's a ton of information on them, but it's all in tiny pieces spread across grammar books, dictionaries, and various websites. I spent a year putting this information together and building this course to make phrasal verbs as easy to understand as physically possible. By taking this course you'll save tons of time and the frustration involved in digging for bits and pieces of necessary information. This course breaks down what phrasal verbs are, why they matter, what kinds of phrasal verbs exist, and what is the best way to learn them. You will learn 10 of the 1.5% most common phrasal verbs in the English language: what they are, which of their meanings are the most/least common (with the % of their usage and examples), and a variety of exercises to help you cement them in your memory. This course will give you the NECESSARY knowledge to transform your "good English" into "native English" - as long as you follow all recommendations and do all the exercises provided. Note that the theoretical part (video) of the course is 40 minutes long, but the activities section is expected to take 5-6 hours. This is because I have created custom exercises for you to practice what is taught in the video. Who this course is for: Pre-Intermediate (or higher) English learners who want to think and speak more naturally. Students trying to write better university papers in English. People that have/are looking for a job that requires English. People who deal with English on a daily basis. People that know that their English is not perfect and there’s space for improvement. People that know that their English is almost perfect and there’s a tiny bit of space for improvement. ;) People that struggle to understand native speakers. Less
Expired
45 used

[100% OFF] GraphQL from Scratch - Realtime MERN Stack with React Node Free

Learn GraphQL from Scratch with MERN Stack (Mongo ... MoreExpress React Node) and Firebase to build Truly Realtime Web Apps. So if you are looking to Build Lightning Fast Realtime Web Apps using GraphQL with MERN Stack, don't waste your valuable time wandering around and trying to learn it from 10 different resources. I have packed everything into this course for you to fully understand all the practical GraphQL concepts... from basic to advance. Ready to go FullStack GraphQL with Node React MongoDB Firebase Authentication CRUD Realtime Subscriptions Deployment and more Join me :) Less
Expired
154 used

[100% OFF] Learn Next.js Free

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

[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
Expired
11 used

[100% OFF] Fundamentals of Machine Learning[Hindi] Free

This course is designed to understand basic Concep... Moret of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. Data pre processing and Simple Linear Regression Concepts are covered in detail. Coding part is not covered, however wherever possible I have attached the code in the resources. Now question is why this course? This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it. Less
Expired
115 used, 100% success rate

[100% OFF] AWS Certified Solutions Architect Associate Practice Exams Free

This AWS Certified Solutions Architect Associate P... Moreractice Exam Course covers EVERYTHING you need to pass the exam on your first try About this Practice Exam: With the help of this practice exam for the Certification of AWS Certified Solutions Architect - Associate, students will gain the Practice test of SAA-C01. The training test, in view of the real test, includes 390 inquiries. In the genuine test, you will be required to finish these inquiries inside 130 minutes. Skills Measured: Effectively prove information on the most proficient method to deploy and architect robust and secure applications on AWS technologies Explain an answer utilizing engineering structure standards dependent on client prerequisites. Give usage direction dependent on best practices to the association for the duration of the existing pattern of the project This practice exam will prepare you for AWS Certified Solutions Architect - Associate Certification. Abilities Validated by the Certification Effectively demonstrate knowledge of how to architect and deploy secure and robust applications on AWS technologies Define a solution using architectural design principles based on customer requirements Provide implementation guidance based on best practices to the organization throughout the life cycle of the project Recommended Knowledge and Experience Hands-on experience using compute, networking, storage, and database AWS services Hands-on experience with AWS deployment and management services Ability to identify and define technical requirements for an AWS-based application Ability to identify which AWS services meet a given technical requirement Knowledge of recommended best practices for building secure and reliable applications on the AWS platform An understanding of the basic architectural principles of building on the AWS Cloud An understanding of the AWS global infrastructure An understanding of network technologies as they relate to AWS An understanding of security features and tools that AWS provides and how they relate to traditional services Who this course is for: AWS Developers Solutions Architecters Less
Expired
27 used