Machine Learning: A different approach for better Business Decisions

Making machines think like humans and solve problems as we do is one of the key tenets of Artificial Intelligence. Artificial intelligence is a far more complex term when it comes to defining just the way it's hard to define a human being. Well, Artificial intelligence is comprised of Machine Learning and Deep Learning where Deep learning is a subset of machine learning and machine learning is the subset of AI.

Deep learning is deterministic and Machine Learning is probabilistic. With Machine learning computers were able to move past doing what they were programmed and began evolving with each iteration. Machine learning can be only taken care of and only if it has been fed with training data that is highly reliable. Any type of AI is dependent on Datasets. A good flow of organized and variable data is required for a machine-learning algorithm to flow. With this, that machine gets the ability to learn itself and predict and evaluate decisions. ML algorithms are trained using three prominent methods. Those three are rightly mentioned in the title.

Supervised Learning

It can be referred to as task-driven Learning where the ML algorithm is fed training data where data is labeled accurately and serves the algorithm with the right problem, solution, and data points to be dealt with. It provides several parameters once the labeled data is provided to the algorithm thus finding the relationship between input and output values. This solution is then deployed for the final dataset. Each time new training data is labeled and fed the relationships get stronger.

Unsupervised Learning

Algorithms are left to their own devices to discover and present the interesting structure in the data. It holds the advantage of being able to work without labeled data. There are no correct answers and there is no teacher. One can call unsupervised learning to be data-driven

Reinforced Learning

Reinforcement learning takes inspiration the same way how human beings learn from data in their lives. It features an algorithm that improves upon itself and learns from new situations using a trial-and-error method. Favorable outputs are encouraged or ‘reinforced’, and non-favorable outputs are discouraged or ‘punished’.

In typical reinforcement learning use-cases, such as finding the shortest route between two points on a map, the solution is not an absolute value.

for example real-time Decisions, Skill acquisitions, or Game AI

To conclude one can determine what algorithm their business model supports to grow big, better, and wise.

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