Astonishing Hierarchy of Machine Learning Needs

Machine Learning is hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need.

The Brief

Machine Learning is the hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need. The real need for today’s time and business is to clarify, demonstrate and extract real values to benefit every one from this golden key word “Machine Learning”.

As on date sadly most of the machine learning methods are based on supervised learning. Which means we still have long long way to go. In Fintech domain we say MachineLearning is the future (actually that future is now) of Ecommerce & DataScientist will work as a batman for FinTech & InsureTech.

Lets Define Machine Learning

Arthur Samuel coined in 1959. He called it a “field of study that gives computers the ability to learn without being explicitly programmed.”

Machine Learning is a focal point where business needs and experience (Mathematics, Statistics & Algorithmic logic/thinking) meet emerging technology and decides to work together to put useful results on table for real business.

Some History of Machine Learning

ML Arthur Samuel coined in 1959. Some of the  timeline  of machine learning. 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.

Major discoveries, achievements, milestones and other major events are included as below.

Although Machine Learning has now gained prominence owing to the exponential rate (1990 was the flying gear year) of data generation and technological advancements to support it but it has roots from old days.

Data Science & Machine Learning

Thanks to statistics, machine learning became very famous in 1990s. The intersection of computer science and statistics gave birth to probabilistic approaches in AI. This shifted the field further toward data-driven approaches. Machine Learning is about the use and development of fancy learning algorithms. Data science is more about the extraction of knowledge (KDD) from data through algorithms to answer particular question or solve particular problems

In large-scale data available systems, intelligent systems with best suitable algorithm  analyze, detect patterns and learn to inform decision and information. Machine learning helps data science by making a provision for data analysis, data preparation and even decision making. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters.