What is machine learning?
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in computer gaming and artificial intelligence. Also at this time the term self-teaching computer was used synonymously.
In the early 1960s, an experimental "learning machine" with punched tape memory called the Cybertron was developed by the Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using elementary reinforcement learning. It was iteratively "trained" by a human operator/trainer to recognize patterns, and it was equipped with a "goof" button to reevaluate incorrect decisions. In the 1960s, there was a representative book on research on machine learning. Nielsen's Learning Machine book, which mostly deals with machine learning for pattern classification.
A computer is given the ability to learn anything without having to write a program about it in advance—this is machine learning. Computers can do anything very easily because of their ability to learn by themselves. In other words, if the computer's winning rate increases as the number of games it plays increases, then the computer is actually learning. It means he is learning by playing, and this ability to learn by himself is called machine learning.
Why learn machine learning?
At the top of today's IT skills list is machine learning or ML skills. Experts say that the future holds immense potential in things like machine learning and artificial intelligence. So it is important to acquire skills in such technology sector.
Such technologies are making our lives easier without realizing it. We are becoming dependent on such technology. For example, when you give a voice command on the phone or ask to search for an image on the Internet, machine learning can show results according to your needs.
Currently, the use of machine learning is increasing in almost every field of life. Doctors, engineers, defense forces, meteorologists who are not! Politicians can also analyze voter behavior. Overall they can also figure out their probability of winning or losing. Even who can be bought with money.
Meteorologists can provide faster and more accurate weather warnings by applying machine learning. Agronomists can make decisions by analyzing previous data. As a result, it will be possible to take early warning against various calamities including food shortage in the country.
So by now you must have understood why you need to learn machine learning! At the same time, you can definitely understand that machine learners will take over the industry in the near future.
How many types of machine learning?
Machine learning can be mainly divided into four categories:
1. Supervised learning
2. Semi supervised learning
3. Unsupervised learning
4. Reinforcement learning
The future of machine learning
If you have read the above, you may understand that the use of machine learning will be noticed in most digital devices in the future. As technology improves, the use of machine learning will increase.
The tasks in which the use of machine learning can be observed.
Data mining
Machine learning and data mining often use similar methods and overlap significantly, but while machine learning focuses on making predictions based on known features learned from training data, data mining focuses on the discovery of (previously) unknown features in the data (this is knowledge discovery in the analysis step database). Data mining uses many machine learning methods.On the other hand, machine learning employs data mining methods as "unsupervised learning" or as a preprocessing step to improve the learner's accuracy.
optimization
Machine learning is also closely related to optimization: many learning problems are formulated as minimizing some loss function on a training set of examples. Loss functions express the mismatch between the predictions of the trained model and the actual problem instances (for example, in classification, one wants to assign a label to the instances, and the models are trained to correctly predict a set of predefined labels.
Generalization
The difference between optimization and machine learning stems from the generalization goal: while optimization algorithms can minimize loss on a training set, machine learning is concerned with minimizing loss on unseen samples. Generalization properties of various learning algorithms are an active topic of current research, especially for deep learning algorithms.
Careers in Machine Learning
The worldwide demand for machine learning is increasing. Bangladeshi software companies will start working in this category soon. The reason for this is that Bangladeshi companies mostly make products dependent on foreign clients. With all these aspects, the local machine learning-data science job market is growing rapidly.
Again, looking at eCommerce, many sites are relatively stable and many are struggling. Many new eCommerce startups have started their journey. Major ecommerce companies have started using advanced machine learning technology. Today's Deal has a separate AI team.
As the days go by, the work of machine learning (Machine learning) will increase in the tech industry. From advertising to marketing, machine learning will be used everywhere. Whether the user will bounce or not, how the user's behavior will be, how the demand for a product will be is all seen with machine learning.
All in all, we can say that the machine learning
related job market will be trending in the next 3-4 years.