Introduction

The techniques and algorithms employed by machine learning give this subject a wide range of applications in numerous domains. With the help of machine learning algorithms, we are able to predict results with a high degree of precision. This high level of accuracy is extremely important when it comes to the future potential of machine learning. A Gartner report predicts that machine learning will become the most innovative discipline and will give rise to major scientific breakthroughs in the next decade. This gives an indication of the integration of machine learning into our academic as well as industrial systems. As of now, only about 33% of the total functional industries in India are dependent upon machine learning techniques. The proportion of dependency is relatively high in Delhi and ranges from 44 to 60%. One main reason behind this dependency is the machine learning courses in Delhi that serve as important resources for preparing professionals for the future industry.

Let us take a look at some verticals of machine learning that will dictate the future of our technological progress.

Web engine accuracy

It is an open fact that mining information from the search engine consumes a lot of time. We need to navigate across various pages and websites before landing on the perfect one. Machine learning techniques have a great potential to boost the accuracy of the search engine. We know that search engines operating in the present times use more than 200 factors for ranking different types of web pages. Google uses a PageRank algorithm to form a hierarchy of web pages and give desirable results to users. The machine learning algorithms as well as deep learning techniques like artificial neural networks can go a long way in optimizing search engine results and providing the user the exact outcomes for his queries.

Quantum computing

Quantum computing is described as a future technology that will cater to the requirements of voluminous data sets and our ever-growing data needs. Although this technology is not commercially ready, it is one of the best computational methods to perform rapid processing tasks in the future. Quantum computing works by sourcing data as raw material and subjecting it to interference, superposition, and entanglement techniques. By virtue of these techniques, the largest data sets ever can be processed within a fraction of a second. However, in order to make this technology commercially viable, a lot of research and development is still needed in this field.

Automated systems

In the future, we may see the growth of automated systems that are capable of self-learning and improving their own functioning and accuracy. Open learning frameworks like PyTorch and TensorFlow will be widely used in such systems. Needless to mention, machine learning techniques will not only help in the modernization of automated systems but will take their power of computing and processing to a different level.

Concluding remarks

The applications of machine learning ranging from geology to space will make this discipline a prospective research repository for scientists and researchers in the future.

RELATED ARTICLES

Leave a Reply

Your email address will not be published. Required fields are marked *