Machine Learning Technologies I want to work on in the future.

Machine learning (ML) is a very popular technology these days.

So, what is the ML basic definition?

“ML is a type of or a sub-part of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical and big or large data as input to predict new small and accurate output values, and this is being automatic when we set up all things”.

In simple language, ML automates the process of automation.

I like to make things automatic since my childhood. I learnt ML in 2017 for two months and learn a lot of things related to ML. I tried it by creating some projects like Gender Classification, Classroom Automation etc.

These days, I am working as a front-end developer in Madgical Techdom but I hope that in coming years I change it in ML.

These days, most of the IT companies working on it.

I am not working a lot in this field. I am only studying the concepts and implementing them on small to medium levels. On behalf of this experience, I want to share some of the topics and features of ML.

Preprocessing of Data

Classroom automation takes thousands of pictures of every student for attendance and changes them in a simple text file.

A data processing/mining technique that involves transforming raw data into an understandable format.

Each algorithm works differently and has different data requirements. For example, some algorithms need numeric features to be normalized, and some do not. Then there’s the complication of text, which needs to be split into words and phrases.

Look for an automated machine learning platform that knows how to prepare data for each different algorithm, recognizes and prepares text, and follows best practices for data partitioning.

Diverse Algorithms

Every dataset contains a piece of unique information that reflects the individual events. Due to the variety of situations and conditions, one algorithm cannot solve every possible problem or dataset successfully. Because of this, we need access to a diverse repository of algorithms to test against our data, in order to find the best one for our particular data.

Look for an automated machine learning platform that has dozens, or even hundreds of algorithms. Ask how often new algorithms are added.

Algorithm Selection

Having hundreds of algorithms available at your fingertips is great, but unless you are more patient than I am. You don’t have time to try each and every one of those algorithms on your data. Some algorithms aren’t suited to your data, some are not suited to your data sizes, and some are extremely unlikely to work well on your data.

So, you have to decide which algorithms make sense for your datasets and run only those. That way you will get better algorithms, faster.

Human-Friendly Insights

Albert Einstein once said, “If you can’t explain it simply, you don’t understand it well enough.”

Look for an automated machine learning platform that explains model decisions in a human-interpret able manner. The platform should show which features are most important for each model and show the patterns that are fit for each feature. Ask whether the platform can provide work examples, including the key reasons why a prediction is either high or low? Check whether the platform automatically writes detailed model documentation, and how well that documentation complies with your regulator’s requirements.

Model Monitoring and Management

In a constantly changing world, your AI applications need to be kept up to date with the latest trends. In an automated machine learning platform that proactively identifies when a model’s performance is deteriorating over time, making it easy to compare predictions to actual results, simplifying the task of training a new modal on the latest data.