Future Scope of Machine Learning
Scope of Machine learning:
Machine learning is a subcategory of artificial intelligence (AI) that enables software programs to improve the accuracy of their predictions even if they are explicitly designed to do so. To predict accurate results, machine learning algorithms take advantage of older datasets. Junk mail filtering, fraud recognition, smart healthcare organizations, speech acknowledgment, computer vision, and smart transportation are some of the standard uses of machine learning technology.
What is the Classification of Machine learning?
Basically, Machine Learning is characterized into four dissimilar parts termed as Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning.
- Supervised Learning: In supervised learning, the algorithm is trained with labeled datasets. In addition, the input and output are already defined in the algorithm.
- Unsupervised Learning: In supervised learning, algorithms are trained with unlabeled datasets. The algorithm goes through all the data to make some meaningful predictions. Here are some popular examples of unsupervised learning: K-algorithms, potential clustering, etc.
- Semi-Supervised Learning: In semi-supervised learning, it is a mixture of both supervised and non-supervised learning. The model is skilled with labeled datasets but in this case the algorithm is flexible to make its own choices and the predictions will be based on its own understanding.
- Reinforcement Learning: It’s like supervised learning, but the main difference between the two is that data reinforcement learning training is not done with labeled datasets but this model has trained itself with trails and errors. A series of successful outcomes will be reinforced to establish the best solution or strategy for a particular situation.
Future of Machine Learning:
There is no doubt that the expansion of machine learning in every domain is a burning issue nowadays. The integration of machine learning techniques has revolutionized industries such as automotive, entertainment, gaming, finance, and healthcare. The evolving scope of ML will increase the efficiency of low human intervention machines. The main purpose of integrating machine learning across different domains is to reduce error function and improve real-time results at minimal cost and time. Below are some of the future scope of machine learning technology that will have a tremendous impact in the years to come.
- Robotics: In the years to come, this technology will expand the robotics domain. As much research is being done in this area, for example, Japan built its own robot “Erica” which became the first robot news anchor. In Dubai, he created “Sofia” and became the first robot to receive Saudi Arabian citizenship. With advances in AI and ML, robots mimic human signals and perform tasks with high accuracy.
- Computer Vision: As the name implies, “Computer Vision” does what it is known to do. It provides vision to a machine or computer. This technique enables machines to recognize and analyze digital images, videos and graphics. Based on the restructuring and its analysis will provide the output with minimal error.
- Quantum Computing: The ability to transform and innovate through quantum algorithms in the discipline of machine learning. It can digest data much faster, allowing it to draw conclusions and consolidate knowledge more quickly.
- Automotive Industry: With the help of ML, the concept of “safe driving” came up. In the current context, well-known firms such as Google, Tesla, Mercedes-Benz have already put a lot of effort into designing the concept of “autopilot”. With wireless sensors, the Internet of Things, HD cameras, and audio / video recognition systems, this concept can be properly implemented.
- Cyber Security: Nowadays, banks and financial institutions implement machine learning to prevent corruption. Phishing is a major concern these days, with various classification and regression techniques applied to correct phishing emails to protect consumers from online fraud.