Overview of Machine Learning
Machine learning is a technology that enables computers to learn from data and make decisions or predictions without being explicitly programmed. This is a significant advancement in the field of computer science and has the potential to revolutionize various industries and applications.
It is widely used in everyday applications such as search engines to rank web pages, social media to recognize and label pictures, and video streaming services to recommend movies. This shows how machine learning has become an integral part of our daily lives without even realizing it.
The applications of machine learning are not limited to consumer applications, but it is also making their way into big companies and industrial applications, such as optimizing wind turbine power generation and computer vision in factories to inspect for defects. This indicates how machine learning has vast potential in different fields.
Applications of ML
The state of the art in machine learning refers to the current best practices, techniques, and methods being used in the field of machine learning. It encompasses both the theoretical foundations and the practical applications of machine learning, including areas such as deep learning, reinforcement learning, natural language processing, computer vision, and more. The state of the art in machine learning is constantly evolving as new research is conducted and breakthroughs are made.
Machine learning grew up as a sub-field of AI, and it's used to build intelligent machines because we couldn't write explicit programs to do many interesting things, such as speech recognition and self-driving cars, so machines need to learn how to do these tasks by themselves.
Many people are working on machine learning applications in various industries, and it is likely to touch almost every industry in a significant way now or the near future.
Artificial General Intelligence (AGI) is a long-term goal for many researchers, but machine learning is seen as the best way to make progress toward that goal.
AI and machine learning are estimated to create an additional $13 trillion of value annually by 2030, and there is a vast unfulfilled demand for this skill set.
What is Machine Learning?
Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. Arthur Samuel, who wrote a checkers-playing program in the 1950s, is credited with defining machine learning. The checker's program was able to improve its gameplay by playing tens of thousands of games against itself, learning what is good or bad support positions.
The more opportunities a learning algorithm has to learn, the better it will perform.
There are two main types of machine learning algorithms:
supervised learning and
unsupervised learning.
Supervised learning is used most in real-world applications and has seen the most rapid advancements and innovation.
The ability to effectively apply machine learning algorithms is just as crucial as possessing excellent tools for machine learning. In other words, having a great set of tools is not enough, one must also know how to use them properly in order to achieve the desired results.
References
DeepLearning.AI & Stanford University. (2023). Supervised Machine Learning: Regression and Classification. Coursera. Retrieved from https://www.coursera.org/learn/machine-learning/