Often Artificial intelligence, Machine learning, and deep learning are often overlapping terms that get candidates confused. Artificial Intelligence means getting a computer to imitate human behavior. Machine learning on other hand is a subset of Artificial intelligence, and it consists of strategies that enables systems to figure things out from data and deliver applications. Meanwhile, Deep learning is a subset of machine learning that allows computers to solve highly complex problems.
While these descriptions are accurate, they are little concise. So let us explore each of these segments and provide you with a little more background. Difference between AI, Machine Learning and Deep LearningWhat Is AI? Artificial Intelligence is an academic discipline that was established in 1956. The aim was to get processors to perform tasks regarded as uniquely human, aspects that required intelligence. In the beginning, researchers worked on issues like playing checkers and solving logical problems. Early success caused researchers to exhibit almost boundless enthusiasm for the possibilities of Artificial Intelligence, matched only by the extent to which they miscalculated just how complex some problems were. AI refers to the output of systems. When computers are doing something intelligent, so it exhibit artificial intelligence. The term AI does not say anything about those issues is solved. There are numerous different techniques, including rule-based or expert systems. One of the categories of methods that started more widely used in the 1980s was machine learning. How Artificial Intelligence works? Artificial intelligence combines large amounts of data with iterative processing and fast and intelligent algorithms, allowing software to learn from features in the data automatically. AI is a huge field of study that includes many theories, methods, and technologies as well as dealing with major subfields:
What Is Machine Learning? Early researchers found issues to be much more complicated because those problems were not agreeable to the early techniques used for AI. Hard-coded algorithms or fixed, rule-based systems did not work well for areas like image recognition, extracting meaning from text. For example, when you think about reading as a skill to learn, you would not sit down and learn grammar or spelling before going through the first book. One would read an easy move and move to complex ones over time. Same way, in machine learning, you process a lot of data and learn from it. You feed an algorithm with lot of data and let it do its magic. So if you feed an algorithm a lot of data on financial transitions, it could tell you which one is fraudulent and let it work out what indicates fraud so it could predict fraud in the future. This ensures businesses to have better productivity. As these algorithms develop, they can tackle numerous problems. But there are some areas like speech or handwriting recognition that are still harder for machines. So in case machine learning coding bootcamp can help you understand imitating humans, and then why not mimic the human brain? This is the idea behind neural networks. How Machine learning works?Machine learning as discussed is a form of AI that teaches computers to think same way as humans do learning and enhancing past experiences. It works on exploring data and identifying patterns, involving minimal human interaction. Any task can be finished with data-defined pattern or set of rules that can be automated using ML. One could complete any task with a data define pattern. This ensures companies can transform processes that previously required human interaction like customer service calls, reviewing resumes, bookkeeping, or any other job. Machine learning bootcamp can help you learn all dynamics of machine learning working. What is Deep Learning? Deep learning is all about making use of neural networks with more neurons, layers, and interconnectivity. While we are still a long way from mimicking the human brain in its complexity, we are still moving in that direction! By reading about advances in computing from autonomous cars to go-playing supercomputers to speech recognition, deep learning is under the covers. One may experience some form of AI. Behind these scenes, AI is supported by some form of deep learning. Now let us look at some issues to see how deep learning is different from simple neural networks or other forms of machine learning. How Deep Learning Works? If you are given an image of dogs, you recognize them as dogs, even if you have never seen that image before. And it does not matter if the dog is lying on sofa or playing outside. You could recognize a dog because you know about the several elements that define a dog: shape of its muzzle, size, tail, and placement of legs, and so on. Deep learning can easily do this. And it is vital for numerous things like autonomous vehicles. Before a car can determine its following action, it needs to know what is around it. It must be able to identify people, bikes, road signs, other vehicles, and more. And do so in challenging visual circumstances. Standard machine learning techniques cannot do this. Further, natural language processing is used for chatbots, smartphones voice assistants, to name few. Conclusion So hoping the first definition made in the beginning made more sense. AI refers to devices exhibiting human-like intelligence in some manner. There are several techniques for AI, but one of the biggest subset is machine learning that lets the algorithms learn from data. Deep learning is a sub-part of machine learning, using many-layered neural networks for solving the hardest problems. In case you wish to learn about machine learning then machine learning coding bootcamp is the place to check. Source: https://machinelearningprogrammer.blogspot.com/2022/01/whats-difference-between-artificial-Machine-Learning-and-Deep-Learning.html
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While many advanced machine learning tools are hard to use and need a good deal of knowledge and sophistication in statistics, mathematics, and software engineering. As far as beginners are concerned one can opt for a machine learning coding bootcamp to gain wide knowledge and get accessibility to career opportunities.
A variety of supervised and unsupervised learning models could be made use of using R and Python, which are freely available and could be straight away set up on the system and even simpler models like linear or logistic regression could be used for performing interesting and crucial machine learning tasks. If you are a fresher to machine learning, then having math and data analysis skills is essential. One needs to have skills to crunch data for deriving useful patterns and insights that are the foundation of machine learning. Some essential steps in data analysis are from loading large data sets, cleaning it for finding missing data, and slicing and dicing data sets for finding patterns and correlations. So investing in a Machine Learning Bootcamp in California can be beneficial. Beginning a Career in Machine LearningWhen it comes to the subject of machine learning, having some knowledge can take you far. A variety of supervised and unsupervised models can be implemented using R and Python. These can be implemented on a system. Even simple models like logistic regression and linear could be used to perform important machine learning tasks. These essentially can be learned at a coding bootcamp. Some essentials skills your bootcamp would expect you to learn are probability, statistics, linear algebra and calculus for grasping fundamentals of machine learning for working with data matrices. If you are planning to learn advanced tools, you would require deeper knowledge of mathematics, software engineering, and statistics. These are required for gaining understanding and making use of algorithms and tools to make real-time projects and gain confidence in the discipline. In case you are about to take first in machine learning, starting with key mathematical concepts and moving onto coding aspects from there is wise. By doing this you could easy grasp associated languages with artificial intelligence like Python. This can make your journey easier. If you previously have familiarity and strong hold on mathematics, then the right step for you in a machine learning bootcamp is to get yourself familiar with the framework. There are numerous libraries to select from when creating models in Scikit-Learn, Pandas, and Numpy. These toolkits vary in difficulty level as per the project. One could also start with a simple framework to start growing and developing a great portfolio. The salaries and wide range of professions to consider once you have completed a machine learning course through a coding bootcamp are wide and promising. So in case you are looking for the best platform look no further! Source: https://machinelearningprogrammer.blogspot.com/2022/01/machine-learning-great-career-pathway.html The term machine learning was coined was American computer scientist Arthur Samuel in 1959. Machine learning is often interchangeably used with artificial intelligence, but it is actually a subfield of it.
It centers on the ability of the system to improve its performance over time without any human intervention. Machine learning algorithms are the foundation of machine learning models and play an essential role in training the model and handling data. ML uses programmed algorithms that receive and analyze the data to make predictions. The system learns and optimizes as new data is fed into it and develops intelligence over time. Categories of Machine Learning AlgorithmsML Algorithms can be sorted into three broad categories: Supervised, semi-supervised, unsupervised, and reinforced learning. Let’s look at these machine learning types in detail below. Supervised LearningIn supervised ML, the machine is taught through an example. Here, the operator gives the ML algorithms a known dataset. This dataset includes both the desired input and output. Now, the algorithm has to identify the method to arrive at the desired input and outputs. The algorithm identifies the patterns, learns from the observations, and makes predictions. As the operator knows the answer to the problem, it corrects the predictions. This process is repeated until the algorithm achieves desired accuracy level. Supervised ML is further classified into Classification, Regression, and Forecasting. Semi-supervised learningSemi-supervised learning is somewhat similar to supervised learning. Though, it uses both labeled and unlabeled data. Labeled data is the information with meaningful tags to help the algorithm understand the data, while unlabeled data does not have any information. In this approach, a small amount of labeled data is combined with a large amount of unlabeled data to help the algorithms learn. Unsupervised learningHere, the algorithm learns by interpreting the large data sets. It determines the relationship and correlation between the available data to identify patterns. In this case, there is no operator to provide help. The algorithm organizes data in some way to find the pattern or structure. The end result here is not controlled and is totally unpredictable. Unsupervised learning is of two types: Clustering and dimension reduction. Reinforcement learningReinforcement learning is a form of learning in which the component of the system known as agent learns in a simulated, virtual learning environment through trial and error. The result is reinforced using a reward-punishment system created through feedback from its own actions and experiences. It adapts its approach and learns from past experience to achieve the best possible result. Popular Machine Learning AlgorithmsThe machine learning algorithm you choose depends upon several factors such as data size, quality, diversity, the result you want to derive from data. Other considerations are accuracy, parameters, training time, data points, etc. Machine learning training will familiarize you with the popular ML algorithms and their application. The list of some ML algorithms you will learn are: Naïve Bayes Classifier Algorithm (Supervised Learning- Classification)The Naïve Bayes algorithm is based on Bayes’ theorem assuming independence between predictors. It is best suited for predictive modeling and allows to predict a category/class based on a given set of features using probability. It’s a simple ML algorithm yet very powerful and effective for a large number of complex problems. K Means Clustering Algorithm (Unsupervised Learning- Clustering)The K Means Clustering algorithm is used to categorize unlabeled data. It works by finding groups within the data, where the number of groups is represented by the variable K. It then allocates every data point to the nearest K group based on the features. Linear Regression (Supervised Learning- Regression)Linear regression is a simple approach to supervised learning and the most basic type of regression. It helps understand the relationships between two continuous variables. Logistic Regression (Supervised learning- Classification)Logistic regression is perfect for binary classification problems. It focuses on the probability of the event occurring based on the previously available data. It is used to evaluate discrete values from a set of independent variables. Support Vector Machine Algorithm (Supervised Learning-Classification)It is used to analyze data used for regression analysis and classification. It filters data into categories, achieved by providing a set of training examples. Here, each set is marked as belonging to one or the other categories. The algorithm then builds a model that assigns new values to one or the other category. Decision Trees (Supervised Learning – Classification/Regression)A decision tree is a known ML algorithm with a flow-chart-like tree structure. It uses a branching method to demonstrate every possible outcome of a decision. It graphically represents decisions. The tree is explained through decision nodes and leaves. The leaves are the final outcomes, while each node within the tree signifies a test on a specific variable. Machine learning is a vast field, and these were only a few popular machine learning algorithms. To go deeper into ML algorithms and learn about their application, you must consider going to a machine learning bootcamp.A bootcamp is a great way to learn machine learning in a short span and make a career as in it. Source: https://haleyjena.tumblr.com/post/663646254596833280/types-of-machine-learning-algorithms-and-their Machine learning is part of artificial intelligence, where computer algorithms are initially used to learn from data and information. Machine learning computers do not require to be programmed and can change and improve their algorithms by themselves.
Machine learning algorithms currently help computers communicate with humans, autonomously drive cars, write and publish sport match reports, and find suspects. Machine learning severely impacts most industries and the jobs revolving around them. Which is one of the reasons considering Machine Learning Bootcamp is a wise idea for IT candidates. Let us see how machine learning originated and covered various milestones over the years. In 1950, Alan Turing created the “Turing Test” for determining if a computer has real intelligence. For passing the test, a computer had to fool a human into believing it is also human. In 1952 Arthur Samuel wrote the first computer learning program. The computer played with a game of checkers and improved the game as it played; studying the game made some winning moves and strategies and incorporated these moves into its program. 1957 Frank Rosenblatt designed the neural networks for computers or the Perceptron that simulated the thought processes of the human brain. A decade later, in 1967 algorithm was written, which allowed computers to begin using fundamental pattern recognition. Later in 1981, Gerald Dejong introduced the concept of Explanation Based Learning (EBL), in which a computer analyzed training data and created a general rule it could follow by discarding unimportant data. By the 1990s, machine learning shifted from a knowledge-driven approach to a data-driven approach. Scientists started creating programs for computers for analyzing large amounts of data and learn from it. 2006 Geoffrey Hinton coined the term “Deep Learning” to explain new algorithms that could aid computers in observing and distinguishing objects and text in images and videos. In 2015 Amazon launched its machine learning platform and Microsoft in the same year created distributed machine learning toolkit that enabled the efficient distribution of machine learning problems across multiple computers. Also, over 3000 AI and Robotics researchers, endorsed by Stephen Hawking, Elon Musk, and Steve Wozniak, signed an open letter warning of the danger of autonomous weapons which choose and connect targets without human intervention. In the following year, Google’s AI algorithm beat a professional player at the Chinese board game Go, which is considered very difficult and more complex than chess. So while there are many advantages to AI, some scientists believe that computers might never think the same way humans do. So comparing computational analysis and algorithms of the computer to machinations of the human mind is vague. While it is true that the computer’s ability to perceive and interact with the surroundings is growing at a high rate, simultaneously is the quantity of data produced by humans. So the demand for learning machine learning is vast and considered to have a greater reach in the times to come. Considering Machine Learning Training is a great way to begin a journey into an advanced and productive future. SynergisticIT is a leading name in Machine Learning Bootcamp with a dynamic curriculum and industry expert mentorship to provide you an edge over the competition. Contact today! Also, Read This Blog: What Is Machine Learning and Why Is It Important? Machine Learning is popular and widely used these days. People of all walks of life use Machine Learning without explicit knowledge of it. This field of technology automates analytical model building, which allows systems to use data, study patterns, and make unsupervised decisions with minimum Serrors. Although there has been a sudden upsurge in the use of Machine Learning in different fields, there is still a rampant shortage of professionals who can work on it. Therefore, becoming a Machine Learning Programmer will be beneficial in career growth and give a spike in your salary graph.
What are the advantages of Machine Learning?
What high-level positions can candidates apply to after Machine Learning Training programs? As already mentioned, there has been a steep increase in Machine Learning usage in various fields. Once students acquire knowledge of the core concepts and the best practices related to this technology, they can apply to multiple high-level positions, some of which are:
Source: MACHINE LEARNING: A START-TO-FINISH GUIDE Before you venture into the field of machine learning and data science, you need to understand why machine learning’s importance has increased in the last few years. Almost every company is run by data nowadays. The collected data is then used to derive better outcomes from increasing revenue and boosting customer satisfaction. Knowing why this field is important can also help you focus better during the learning process. Ml and artificial intelligence are keys to solving complex data problems these days, so getting acquainted with this field can turn your career around.
Machine learning It is a segment of artificial intelligence and helps computers and machines learn on their own. In simpler terms, it is about designing a computer or software to derive patterns on its own from the supplied data and analyze it correctly, which leads to the expected output. Why is ML essential? ML provides tools to help machines make decisions from the given data so that you can attain specific goals. Here is why these tools or algorithms are essential: · Automate: By using ML algorithms, various complex processes can be automated to reduce errors and save time. Once you set a model in place with desired requirements, the computer will take care of the task independently. · Fast: Managing big data is a complicated process. ML methods save you time and expedite the entire data analysis task, which would be time-consuming when done manually. · Accuracy: ML algorithms are more precise than humans when handling a massive amount of data. Automated algorithms can run for as long as you want them accurately, making the end-result accurate. · Scale: Using ML tools, you can find solutions to problems you could not solve independently. These methods can later be improved and applied to other similar situations and help many other programmers. Why is ML crucial in present times? The primary reason why most organizations use ML is to make better decisions and drive the business in the right direction. When machines begin to learn independently, they can improve customer experiences and assist in the more efficient analysis of their shopping patterns to make accurate predictions. · Timely assessment: Timely analysis of your business requirements is crucial to achieving goals. ML tools and algorithms can help you analyze customer behavior better, allowing you to devise customized activities to boost sales. · Fast processing: ML algorithms are fast, which makes data processing easier and quicker. Due to this, apps can make real-life predictions based on consumer behavior and keep them hooked. This step is quite essential for customer satisfaction and retention. · Revolutionizing Industries: ML is revolutionizing industries and all business sectors as it can provide valuable insights that even a human can miss. Financial companies are using ML tools to prevent fraud and offer customized plans to each customer. The health sector uses ML to provide a faster and more accurate diagnosis of severe health diseases, and the retail industry is utilizing it to drive more sales by enticing customers. ML is changing the world every day. For progrjenahaley54.medium.com/why-machine-learning-matters-c69369a108bdammers who are looking for an exciting field to work in, ML is a great choice. If you are looking for machine learning bootcamps, you can consider enrolling for SynergisticIT. You only need to have a certain familiarity with math and statistics, and you can sign up for the online bootcamp and let the industry experts teach you about ML in detail. The up-to-date curriculum helps you gain the required expertise so that you can find better jobs. Source: https://jenahaley54.medium.com/why-machine-learning-matters-c69369a108bd Machine learning is the buzz these days. If we look around, it is everywhere, from our daily use of mobile apps to autonomous vehicles. And at the rate the advancements are happening, it is safe to assume that the growth isn’t going to slow down in the coming years either. All of these factors have put additional pressure on an average programmer to learn the skills to stay viable in the market. Now, there is a sudden rush in the sector to become the best machine learning programmer. Despite all this development, it is not easy for every coder to venture onto this path with the required confidence and skills, hence they face many challenges.
Here are some of the obstacles that programmers face and how they can overcome them: The math connection Not everybody is brave enough to embrace math, it is a subject that still scares a lot of people. When we talk about the daily functions of an average programmer, it does not involve the use of a lot of math but to master ML, it is mandatory to be familiar with it. To be specific statistics, probability, and linear algebra are what you need to know. So start revising your high school math. Data analysis The second most dreaded thing about this field is the analysis of data. The ability to analyze data and turn it into useful insights is the core duty of anyone working in the machine learning field but not every developer has a knack to do it. Cleansing, organizing, and finding missing data is a difficult task and hence not many developers are keen on becoming an ML programmer. So to begin, you need to develop a power of visualization before you jump into the data analysis process. The debate of Python vs. R The best machine learning programmer not only knows how to carry out data analysis but has a strong foundation of one of the supporting programming languages: Python, R, or Julia. But coders are often stuck in the debate of which one to learn first in order to ensure a smooth learning process. The choice becomes even more difficult for developers who don’t have any idea about the field. Python is still a favored language as its libraries and frameworks help develop ML algorithms easily but R is also preferred by another group of traditional statisticians. Julia is gaining popularity but python seems to be enjoying a top spot. Diversity of frameworks Even if you are a good programmer and have decent coding skills, one of the challenges you will face is to choose the right framework to figure out an ML problem. There are plenty of frameworks available these days that apply differently to different situations and your success will depend on making the right choice. Out of all the libraries available, NumPy, Pandas, Caffe2, Microsoft Cognitive Toolkit, Apache MXNet are the main ones. So gaining an understanding of how these libraries and tools work will help you handle different tasks easily. Multiple approaches Once programmers gain an understanding of various tools and frameworks, the next problem they face is to decide which approach to follow and how to deal with a particular problem. The choice sometimes will be right but can be wrong too which could become a reason for discouragement for many programmers. So you need to learn the concepts clearly and gain certain familiarity so that you can start to predict better solutions. For this, you need to build evaluation skills that can be achieved by enrolling in a coding bootcamp. Too many learning resources With self-paced learning methods, online tutorials, and coding bootcamps, it is not easy to decide which is the best machine learning training path. This has lead to creating even further confusion in the minds of developers. To figure out which is the suitable learning path for you, you need to evaluate the pros and cons of each one. Out of all, coding bootcamps are the most effective and quickest way to become a certified ML engineer. They are fast-paced and provide the right kind of training within a short time span. If you are looking for a credible suggestion, SynergisticIT is a great place to start. They have a team of certified experts that enable every student to begin a career in this ever-growing field. You learn through a series of projects and assignments along with gaining real-world experience. So, don’t let these obstacles stop you from pursuing this path and begin your machine learning journey now. Also, Read This Blog: Why Join A Machine Learning Bootcamp? The advanced technology of Machine Learning is a subset of Artificial Intelligence. It allows computer systems to learn from data, identify repetitive patterns, & make accurate decisions without being explicitly programmed by a human being. The global market growth of ML is expected to grow at an annual rate of 42.08% CAGR by 2024. It will create thousands of career opportunities for qualified professionals. So, if you enroll in one of the best Machine Learning Bootcamps, it will safeguard your career prospects. However, if you’re thinking whether an ML Bootcamp is worth taking or not, let’s help you make an informed decision.
What does Machine Learning Bootcamps teach you?You will get to learn a different set of things when you sign up for a Machine Learning training such as:
Also, Read This Blog: Programming Languages used in Machine Learning! Presently, one can find the application of Machine Learning in many aspects of lives be it, smart speakers, GPS navigation systems or product recommendations everything works on ML algorithms. The algorithms teach machines to segregate & complete their tasks automatically with minimal human support. The more data you fed in a system through algorithms, the more accurate predictions it will make. Over the past few years, the massive scalability of information has become feasible, which enabled machines to generate precise outcomes. However, to make use of ML algorithms effectively, you need to take a Machine Learning Certification course.
Right Learning path for Machine Learning Algorithms There are different types of algorithms in Machine Learning that broadly falls under two categories; learning style and similarity further, they get categorized into different subparts. Here are some of the most used algorithms: Algorithms grouped by Learning Style Supervised Learning Unsupervised Learning Semi-Supervised Learning Algorithms grouped by Similarity Regression Algorithms Deep-Learning Algorithms Decision Tree Algorithms There are many other algorithms apart from the above-listed categories such as Support Vector, Classification, Clustering, Association, Polynomial, Artificial Neural Networks, etc. It can be quite difficult to learn & implement all types of ML algorithms on your own, so you should consider joining the best Machine Learning certification course to learn the major ML concepts & algorithms. The course can help you develop a deep understanding of beginners to advance level modules. After attending Machine Learning Certification training, you will become competent in building algorithms using regression, classification, supervised, unsupervised, & deep learning. Also, you will be able to use Python to draw several predictions from collected data. The Machine Learning Certification course gives you insights & enables you to apply algorithms for creating smart robots which otherwise would not be possible in self-learning. Even from a career perspective, it is utmost essential to take a certification in ML as the companies offer higher-salary packages to certified professionals as compared to others. ML certification equips candidates with premium knowledge & best industry-practices needed for companies to lead the way in the dynamic sphere of Artificial Intelligence. By developing data-driven models & fast algorithms for real-time data processing, you can enable Machine Learning systems to produce accurate results. Learn to deploy ML algorithms the right way at the best Machine Learning Certification Bootcamp, SynergisticIT. With end to end assistance, you will become future-ready for the next step of your professional journey. Also, Read This Blog: Is MERN Stack Development in Trend? One of the most in-demand tech jobs of the 21st century is data science engineers. Data science has seen a lot of progress in the last couple of years mainly because the majority of IT companies are now incorporating it into their daily business to automate data-mining and structure. As per the current number of data scientists, there are 28% more jobs in the market and this demand is only going to increase in the coming years.
Demand for Data Science engineers in 2020 If we compare the statistics from last year, there were 2.9 million data science job openings and in 2020 the job requirement is projected to boom by 364,000 openings. According to a survey by the U.S. Bureau of Labor Statistics, there will be 11.5 million new jobs by 2026 which makes for a very compelling reason to join data science boot camps. From data engineer, machine learning engineer to data engineer, and statistician, you have a plethora of career options as well after completing your certification. What is Data Science? It is a field that combines both statistics and computer science. The main goal to apply data science is to pull insightful knowledge out of large databases and use that information to build or update an already automated system. Best Data Science Coding Bootcamps To make sure you get up to date, in-depth, and thorough training, we have put together a list of the best data science coding boot camps for you. NYC Data Science Bootcamp Learn data science in 12 weeks full time (online/offline) course with R, Python, Machine Learning, Data Analysis, Big Data, and Deep Learning. Their up to date curriculum includes corporate training from leading industry experts. They even have a Pre-work course that includes Intro to Python, R, and Python for Data Analysis to prepare for the upcoming course. General Assembly They offer data science immersive course full and part-time both that features expert instructions, one on one career counselling, and connections to top IT companies to help you get hired. You get an inside look at the industry and learn from top data science specialists that offer you hands-on support. They use SQL, Tableau, and Excel to analyze and illustrate real-world data. SynergisticIT They are an online camp that offers a full-time course in data science and machine learning. They have a structured and advanced level training module that covers everything from Python, AI, computer science to Business analytics, and deep learning. You can unlock a plethora of career paths in leading sectors after completing their course. Flatiron School They have a full and part-time data science immersive course to help you build a solid foundation with Pandas, SQL, and Python. They teach you in-demand skills with the help of passionate industry professionals. They train you in experimental design and problem-solving abilities so you can make better use of the data at hand. If you want to up-skill and are looking to get your big break in the IT industry then data science is one direction you can definitely follow. So sign up with one of the leading data science boot camps and be a part of the growing community. Also, Read This Blog: Top 5 Online Data Science Courses for 2021 - Learn Data Science! |
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