Machine Learning – ML is currently dedicated to the completion of mundane tasks. Almost in all cases it does its work in a centralised infrastructure. Exceptionally it also runs on distributed infrastructure. In machine learning various specific algorithms and services are managed at the most efficient place between the actual source of the data and even on the cloud.
This post is an attempt to create a short text movie of machine learning timelines.
What Is The Primary Goal Of Machine Learning?
AILabPage defines machine learning as “A focal point where business, data, experience meets emerging technology and decides to work together”. If you have not unfolded machine learning jargon already then please take look on our machine learning post series library.
Machine learning is the process of a machine attempting to accomplish a task, independent of human intervention, more efficiently and more effectively with every passing attempt i.e learning phase. At this point, AI- a machine which mimics the human mind, is still a pipe dream. In the middle we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.
Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. Machine Learning, however, has been a reality in our lives for quite some time on the other hand Deep learning is a subcategory of machine learning algorithms that use multi-layered neural networks to learn complex relationships between inputs and outputs.
The most basic rule and meaningful point to understand anything about machine learning is, quality of data it gets served with. So this means its purely driven by data sets to provide answers. It follows GIGO method. The set of algorithms are as important as underlying data sets.
Machine learning has evolved from Artificial Intelligence Subset to Its own domain. It has reached an inflection point – at least in terms of messaging. I remember in my school days as part of statistics class we were told something about AI and ML and we laughed then in the 1990s.
From Turing Machine, Arthur Samuel to current very highly intelligent robots. How much machine learning has come far till now difficult to judge and measure but results are clear and visible though. In recent years, the term ‘machine learning‘ has become very popular among developers and business alike, even though research in the field has been going on for 5 decades plus. For me defining ML is even simple and easy which is as below.
“ A focal point where business, data, experience meets emerging technology and decides to work together”.
Classic example of ML which we all use almost on daily basis. For managing our email box. Each time clicking the “not junk” or “junk” button on a mis-categorised email, the machine learns a little bit more. It becomes more efficient at discerning the two from each other. Every time this happens the machine learns from its mistake and adjusts the attributes it looks for in each email accordingly.
Machine Learning Approach
The approach of developing ML includes learning from data inputs based on “What has happened”. Evaluating and optimizing different model results remains focus here. As on date Machine Learning is widely used in data analytics as a method to develop algorithms for making predictions on data. It is related to probability, statistics, and linear algebra.
Machine Learning is classified into three categories at high level depending on the nature of the learning and learning system.
- Supervised learning: Machine gets labelled inputs and their desired outputs. The goal is to learn a general rule to map inputs to the output.
- Unsupervised learning: Machine gets inputs without desired outputs, the goal is to find structure in inputs.
- Reinforcement learning: In this algorithm interacts with a dynamic environment, and it must perform a certain goal without guide or teacher.
How Machine Learning got evolved over the period
Some of the evolutions, which made a huge positive impact in real-world problem solving, are highlighted in the above picture with title Machine Learning and some use cases
ML develop its own encompassing strategy from the experience it come across over the period of time. As sir Andrew Ng says AI is a new form of electricity so in my opinion ML is no different; This has been one of the most active and rewarding areas of research due to its widespread use in many areas.
Some of major break through in ML includes Natural Language Processing and DeepLearning.
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Conclusion – Machine Learning is the branch of computer science unlike statistics which is graphical branch of mathematics. ML deals with the development of computer algorithms that learn and grow themselves. Machine Learning can be very specific task oriented as well.
In a specific environment machine can be forced to learn just the rules of a game. It had to try out millions of different strategies for each situation in this case before it prepare self to play against its opponent. For example game of chess or backgammon.
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Categories: Machine Learning