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Six Mistakes Students Make When Outsourcing Their Programming Homework

Six Mistakes Students Make When Outsourcing Their Programming Homework. So let us get initiated and address these mistakes one by one.

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When you reach out to an expert online to get assistance on your programming homework, there are several things that you need to be careful about. This is vital to ensure that you do not make silly mistakes like most other students. But what are these mistakes? This guide will address some critical errors students tend to make when they outsource their programming homework. So let us get initiated and address these mistakes one by one.

Mistake 1 – Not enquiring who will work on the assignment

When outsourcing your Java homework, you would expect a renowned Java expert with multiple years of experience behind them to do the task for you or a Java professor who knows the perfect solutions that can fetch you top marks to handle the job. On the other hand, you surely do not want an amateur writer or freelancer studying in college and taking up homework assignments for pocket money to do the task for you. It is not to say that the student freelancer may not create a good job.

If they are well equipped with the Java lessons, they, too, can make perfect copies, which can fetch you good marks, but there is no guarantee that the platform with such amateur writers will have done ample research to ensure that they only hire knowledgeable students for your task. Thus, it is quintessential to ensure that you inquire who will be working on this task when you approach a homework platform and get them on board only if they assure you that only reputed and recognized experts will handle your homework.

In this respect, TopAssignmentExperts can be a good choice of platform as they have dedicated experts for every subject. So, when you head to them for your Java assignment, only an industry professional or a Java professor with a reputed industry will do the task for you.

Mistake 2 – Not questioning about their hours of operation

When you think of it, it might seem unlikely that you will wait until the last minute or late in the night to reach out to a homework platform to do the task for you. This is true. You will not surely wait until the last moment, but consider a situation wherein you completely forget an assignment submission due? Now, when you are packing your stuff for the next day, looking at the timetable, you realize that there is a Python assignment due at 8’o’clock in the morning.

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If you are reminded of it only at 11 in the night, you have just 9 hours before the submission. Any regular homework platform that operates strictly during business hours will either refuse your assignment or not even reply to your query in the odd hours. This can leave you in a fix. Hence, before you shortlist the best homework help platform for your programming task, you must inquire about their operation hours.

It is wise to go with a provider operational, accessible, and available 24/7. In this manner, be it a new assignment, a doubt on the recently solved assignment, or some revisions in the submitted work, everything can be handled even in the odd hours of the night. Now, the question is, how does a provider make it possible to have their experts available 24/7 for you? We came across this platform, ThanksForTheHelp, which has excellent Java and other subject experts.

The team has hired experts from around the world. So, when you need expert guidance in the late hours of the night, a professional from maybe a different part of the country, aware of the guidelines and the solution patterns applicable in your university or assignment, will take care of the task for you.

Mistake 3 – Not asking questions about the revision policy

When you pay for a service, you expect to receive the best quality for it. Why should you compromise over quality when you are paying for it? The same applies to when you outsource your programming homework. Of course, an expert takes care of the homework, but that does not mean they cannot make mistakes. Now, the mistakes are not always factual errors. At times, the mistakes might be formatting or structural flaws.

So, even if the questions are solved correctly, you may lose marks if the format is not correct. This should not be acceptable to you. Hence, it is vital to approach a platform, which is okay with revisions. EduWorldUSA is an excellent platform as they have an unlimited revision policy for a set number of days from receiving the assignment. You do not have to pay anything extra for these changes. This is a part of their customer satisfaction policy.

Mistake 4 – Not questioning what happens when you do not like the quality?

As it is not you but someone else doing the task for you, and you are paying for the service, there is always a probability that you may not be happy with the final product received. When you reach out to a provider, this should be an essential question that you ask. A good provider will place their client’s interests over anything.

They would not want you to be dissatisfied with the quality, but they will surely take measures for it if you are. An excellent repayment policy, in this case, would be not charging the customer when they do not like the quality, despite the revisions. Some excellent online homework assistance platforms have a 100 percent moneyback guarantee. This is again a vital aspect of customer satisfaction.

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Now, there is another aspect to this problem. Your homework is still pending. So, what should you do about it? In this case, you can check out the Unifolks platform. They have an array of solved previous year programming questions with their solutions. In addition, they also have practice questions, exercises, and quizzes, which can give you a precise idea of how you must solve the questions to fetch a top grade.

Mistake 5 – Not ascertaining about their data handling

In America alone, suicide is the second leading cause of death amongst college students. Of course, stress is the critical cause of students taking this extreme step. They either fail to keep up with their expectations or end up scumming to their parent’s expectations. Ending your life over anything is not worth it. Hence, if ever you feel overwhelmed by multiple things on your plate, it is good to head to an expert to do the task for you.

However, you do not want this private information to be divulged when you outsource your assignments from an expert. In no way should your professors or anyone from your college or university find out about this. Hence, ensuring that the homework help platform you reach out to guarantees, your secrecy is quintessential.

Several top-notch platforms will guarantee privacy and confidentiality to you. So, before selecting any platform, ensure that they guarantee you that your association with them remains a secret between you and them. No other third party will ever find out about this association.

So, these are the five critical mistakes students make when delegating their programming homework to an expert. Have you made these mistakes? Know of more such errors, which have cost you heavily? Please share your experiences with us in the comment box below.

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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

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Will AI render Software Developers Obsolete

Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

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