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How to Perform Quality Assurance and Testing for ML Projects?

Testing and Quality Assurance are the two most crucial steps that are more often than not neglected or underestimated. With newer and more advanced technologies engulfing the IT industry, it is important to devise new ways of testing and make sure that software applications remain bug-free and robust.

Machine Learning is changing the way software products and applications think and respond to queries. In the strife to provide our machines with our intelligence, we are empowering our software to think for them and proactively learn based on experiences, just what we do!

Machine Learning results in areas of image, speech, and text recognition, disease detection, human extension, error reduction, and stock prediction. It has a wide array of applications in all major industries in the world like Healthcare, Retail, Life Sciences, Manufacturing, Enterprise, Education, Medicinal sciences, research and development, and Finance and Accounting.

When we talk about testing these Machine Learning projects, we are often confused about how this can be tackled. Software Testing Services, by definition, is a fairly straightforward task. For every input, there is a definite output. We enter values into the software application, choose to make some processing, and then check the outcome. On the basis of the expected outcome and the actual outcome, we ensure rightness or wrongness of a software products feature.

The main point here is that we already have in mind the expected output. But, Machine Learning is one scenario that does not depend on the output in a test case. Machine learning is based on neural networks which are layered algorithms whose variables are adjusted according to a learning process. The learning process effectively comprises of using known data inputs to get the outputs which are then compared to the known results. When the algorithm code reflects the known results with the desired degree of accuracy, we freeze the algebraic coefficients and generate the production code.

Main points to be taken care of by testers:

It would be easy to learn it through a video, so let's watch it.

You may follow these steps to ensure efficient testing of ML applications:

For a start, these steps are good to go. Machine learning applications require that the testing practices and results are changed to accommodate their differences from traditional software applications.

You can learn some Machine Learning Projects by the end of this year. The growth of AI has motivated software engineers, data scientists, and other professionals from the IT world to explore more possibilities of a career in ML. However, beginners mostly focus on theory instead of practical application. If you get success, you need to start developing ML projects sooner than later.

The tricky part is to decide where to start, thus it is always better to take advice from experts. Here, we will share some real-world instances of machine learning projects that will surely guide and make you understand how a completed project should look like.

1. Identifying tweets on Twitter (beginner)

When we talk about worldwide phenomena, social media hate speech and fake news comes at the top. Offensive posts create an issue but the inaccurate posts through fake profiles are even worse.

NLP is a popular natural language processing application that lets thousands of text documents to be scanned within seconds. For instance, Twitter can process posts related to topics like racist or sexist and separate them from others.

Several stages are involved in the process:

2. Detecting the frauds (Intermediate)

While many believe that going cashless is the best trend ever followed by people worldwide, the banking sector is under threat. The cases of credit card fraud are continuously increasing and it is expected to soar above $32 billion by 2020.

It only takes a minute of the fraction to make fraudulent transactions every day. This brings another issue of imbalanced data.

In Machine Learning, fraud is seen as a classification issue, and when the user deals with imbalanced data, the issue to be predicted is within a minority. This leads to a predictive model to struggle to produce real business value using the data, which at times can go wrong.

Three strategies are applied to fraud transactions:

3. Barbie with Brains (Advanced)

What if your child could have a real conversation with a doll? Modern dolls that can ‘talk’ play a key role in shaping the minds of children. Standard dolls available in the market have a limited set of phrases that cannot conduct a real-time conversation with the child.

Hello, the Barbie project is the perfect example that demonstrates the power of ML and AI. Barbie can deliver interactive logical conversations via NLP and a few advanced audio analytics. There is a microphone on her necklace that records the said phrases and then transmits them to the ToyTalk servers.

There are three basic types of ML:

You need to grasp these applications to know how to use ML to solve your problems. We understand that it’s never easy to start your first ML project idea, yet something great can be created with the proper guidance of experts.

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