Avoid 5 Mistakes While Writing Machine Learning Assignments


For college students, getting an assignment writing task is the biggest fear. No matter what subject or topic they are asked to write an assignment, they always get in a stressful zone hearing about it. However, students also understand that assignment submission projects are crucial if they want to score excellent academic grades. Moreover, it also enhances their knowledge and experience in that particular topic. Universities and colleges give the assignment writing tasks to the students according to their stream of education. Commerce students will be asked to submit an assignment related to it. Similarly, computer science students will be assigned tasks in various fields. If we talk deep about the computer science branch, you all know how crucial the subject is as it is entirely related to programming. 

Students studying under the programming branch are usually assigned machine learning assignments. It is difficult for them to complete the task without the help of an expert, which is why students look for professionals who offer the best machine learning assignment help. Here in this writing, we will throw light on some mistakes that students must avoid while writing the machine learning assignment and also understand the concept of machine learning. 

What Is Machine Learning?

Machine learning is a field of computer science that uses different statistical techniques to allow the computer to learn on its own by evaluating the data without programming. Machine learning is mainly used in Artificial Intelligence and primarily focuses on developing computer applications that can approach data and use it to learn without human interference. However, the learning process starts by observing or with the help of data. The main aim is to let computers know without human support automatically. 

Machine learning uses algorithms that receive data as an input and statistical techniques to forecast the output. Numerous fields use machine learning, including healthcare, financial services, fraud detection, personalised recommendation, etc. In simple terms, the process of machine learning includes the following-

  • Analysing appropriate data sets and preparing the determination.
  • Choosing the correct machine learning algorithm for usage.
  • Creating an analytical model that is by the selected algorithm.
  • Training the model on the data sets prepared for testing.
  • Running the model to generate findings.

These Mistakes Must be Avoided by Students While Training The Machine Learning Model:

Developing an AI model is not easy; it requires skills and knowledge with appropriate experience to make the model run successfully in different scenarios. However, the most crucial stage in AI development is gaining and collecting the training data and using this data while training the models. Any mistake while training the model will not only make the model perform incorrectly, but it can result in terrible decisions. While preparing the AI model, many multi-stage activities are performed to utilise the training data in the best possible way. Below we have some common mistakes that must be avoided while training the machine learning model-

  • Using unverified and unorganised data-

Using unverified and unorganised data is one of the common mistakes machine learners make while developing AI. The unproven data might have duplication, lack of classification, data conflict, errors and other data issues that could create inconsistency during the training process. Therefore, before you use the data for your machine learning training, you must carefully examine the raw data set and eliminate the unwanted data so your model can run efficiently.

  • Utilising the already used data for testing-

This is the most common mistake that you must avoid. It would help if you prevented re-using the data already used to test the model. Suppose someone has already learned something and has enforced the skill in that area. In that case, utilising the same in another area of work can cause one to be biased and repetitive in interpretation. In the case of machine learning, the same logic is applicable; AI can learn with the volume of datasets to predict the answers accurately. Using the same training data could make the model biased and assume results of the effects of the previous learning. Therefore, while testing the model, it is essential to try using the new datasets that are not used previously.

  • Applying the insufficient data training sets-

Insufficient training data is a significant reason behind the model’s failure. To make the machine learning model successful, you will need to use the correct training data set so that it can forecast with the highest level of accuracy. However, depending upon the type of machine learning model, the area of the requirement of data training is derived. For in-depth knowledge, you will need qualitative and quantitative datasets to ensure they can work with high accuracy.

  • Ensure your machine learning model is unbiased-

Any AI model cannot ensure to give hundred per cent results in different situations. Machines can be biased because of various factors such as age, gender, and orientation which affect the results in one way or another. Therefore, you will need to minimise it with the help of statistical analysis to find out how the personal factor is affecting the data and training data process.

5 Mistakes To Avoid While Writing Machine Learning Assignments:

Though there are many mistakes that students commit while writing any assignment task, and that is why they look for an assignment helper who can help them to avoid those mistakes. However, even if you seek guidance from a professional you still need to learn about the mistakes to avoid them in future. 

Here are some common mistakes students make, and they must learn how to avoid them.

  • Lack of editing-

Every student must review the project before submitting it to the professor. All the assignments must be proofread and edited before the final submission, which is the crucial step most students avoid. Academic papers submitted without proper proofreading and editing are likely to score lower grades. 

  • Irrelevant paragraphs-

The assignment paragraphs should be structured correctly with correct information that binds the reader till the end. Many students write irrelevant and unnecessary information in their assignment paragraphs to increase the word limit. However, irrelevant information or data is unacceptable to universities and colleges, leading to assignment rejection and low academic scores.

  • Lack of precise introduction-

The introduction part is considered a critical paragraph as it contains information about the whole assignment. When the reader goes through the paragraph, they get an idea about the assignment topic, why it is written and more. If you have written a poor introduction, the reader won’t be able to understand your assignment and hence will be less interested in reading the complete project. 

  • Poor referencing-

Any academic paper like an essay, thesis, research paper, dissertation or assignment is written on a specific topic or subject. Students must have deep knowledge and skill about the topic so that they will be able to write the task as per the guidelines assigned to them. There are some topics on which students need to refer to the cite, quote or even content so that the assignment does not fall under the category of plagiarised. The project is assumed to be plagiarised when students mention the information taken from a source and cannot provide proper referencing. 

  • Incorrect conclusion-

Students must include the proper conclusion so that the readers know they have concluded the paper. When you fail to include the decision, your assignment appears incomplete. It would help if you spent sufficient time to write an excellent conclusion that can clear the reader that you have tackled the topic well till the end. Apart from the closing, you must ensure that your assignment has a special body section that includes all the essential points about the given topic.

Final Thoughts:

Above in the blog, we have discussed some mistakes that scholars must avoid while training the machine learning model and writing the assignment. Whether you write a programming or any academic project, you must remember and mention the above-discussed corrections in your writing. However, programming assignment writing is challenging as it requires the knowledge and skill of how accurately and successfully you can run your machine learning model.