Machine Learning – The Fundamental Of Artificial Intelligence


As mankind entered the 21st century, the most revolutionary technology which took the centre stage of the scientific forums first and then found its commercial application is artificial intelligence. As we are moving into the 2nd decade of this century various spin outs of this technology such as machine learning, automation, machine vision, virtual and augmented reality etc. have already become very popular. They are hovering to dominate the world. Many activities that need human intelligence and manual control are now replacing by automatic systems which can adapt to the conditions and take own decisions.

Machine learning

Machine learning is the technology which enables the systems to make appropriate decisions or conclusions by themselves without external directives. Here without explicit programming, the computer can learn and grow in terms of the performance. For example, if an application calls for a certain type of data selection from a wide database. We do not fix the characteristics of this data but could change in the process of analysis. Then the computer program has to learn and adapt to these characteristics which are different from the originally defined characteristics. Several machine learning service companies are working on many several applications of this technology.

Advantages of Machine learning

The main advantage of machine learning is that where human intelligence finds it extremely cumbersome to analysis highly voluminous data. It makes inferences and conclusions, a computer program equipped with machine learning codes can perform the job with a much shorter time. Machine vision technology provides much more efficient solutions where human intelligence struggles because of challenges such as subjectivity, fatigue etc. In today’s world, there is no dearth of data and systems which are capable of rapid analysis of these data can simplify many complex decision-making jobs. A well-trained computer algorithm can perform several multidimensional analyses, and using these data such programs can precisely predict the futuristic trends and suggest course corrections.

Process and applications of machine learning

The major stages of machine learning are data collection, data preparation, training, evaluation and tuning. The first two phases involve data acquisition and arrangement in a format to enable the extraction of key features. The next three phases involve teaching the program the rules of selection or rejection of data; test the learning against the data. For which we can predetermine the results and finally the evolution of the algorithm with a large pool of data. There are various learning types such as supervised, unsupervised, reinforcement, semi-supervised. Today, several machine learning solution providers are available across the globe. And we use this technology in applications such as pattern recognition, image and speech recognition, self-driving vehicle, effective search in the internet, automatic diagnosis etc.


Machine learning has immense potential to bring in highly efficient automated solutions.  This technology is essential to handle the huge data which is beyond the capability of human intelligence. However, it is equally important to ensure that no one uses this technology for wrong purposes.

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