Human brain functioning is quite complex and largely unknown to humankind - the way it capture, process, store and retrieve information is still beyond human grasp esp. the cognitive, unconscious and deeper layer of brain functioning.
Scientists, scholars and data science practitioners from different streams have been trying to understand and have made substantial progress in last few decades. Numerous scientific research has helped gain broader understanding of human brain. In addition there has been intense focus on developing practical applications which mimics brain functioning - observe, process, learn and decide.
Robotics, autonomous vehicles, image processing, speech recognition, search engine ML solutions are already in production and have shown great potential for better human experience and value.
ML, Deep learning and related topics are most discussed and sought after across industry and academia. So much so that it has secured peak spot in the Gartner 2017 Hype cycle.
Lets understand what is ML and how do we get familiarity with the topic without getting lost in the numerous courses, articles or freaking out by technical jargon.
In layman language Machine learning(ML) is a practice which helps develop applications to design & encode real life processes using programming, modelling. ML applications doesn't require explicit instructions to learn and update itself as it processes new information.In other words Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
If you are one of those reader who tend to accept definition only from authentic sources, here is what you can refer:
Retail: Market basket analysis, Customer relationship management (CRM)
Finance: Credit scoring, fraud detection, revenue forecasting
Manufacturing: Optimization, troubleshooting
Medicine: Medical diagnosis
Telecommunications: Churn prediction, Quality of service optimization
Web mining: Search engines, programmatic
There are many more use cases which are in proof of concepts phase and many of them will be reality in not so distant future.
ML is going to stay and influence future generations. It will be wise to understand ML as a data science practitioner or otherwise to cope up with the fast changing technology world.
In the next section we will explore key terminology from ML and delve deep in each of these topics.
Scientists, scholars and data science practitioners from different streams have been trying to understand and have made substantial progress in last few decades. Numerous scientific research has helped gain broader understanding of human brain. In addition there has been intense focus on developing practical applications which mimics brain functioning - observe, process, learn and decide.
Robotics, autonomous vehicles, image processing, speech recognition, search engine ML solutions are already in production and have shown great potential for better human experience and value.
ML, Deep learning and related topics are most discussed and sought after across industry and academia. So much so that it has secured peak spot in the Gartner 2017 Hype cycle.
Lets understand what is ML and how do we get familiarity with the topic without getting lost in the numerous courses, articles or freaking out by technical jargon.
In layman language Machine learning(ML) is a practice which helps develop applications to design & encode real life processes using programming, modelling. ML applications doesn't require explicit instructions to learn and update itself as it processes new information.In other words Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
If you are one of those reader who tend to accept definition only from authentic sources, here is what you can refer:
- “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – Nvidia
- “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford
- “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co.
- “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington
- “The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” – Carnegie Mellon University
Retail: Market basket analysis, Customer relationship management (CRM)
Finance: Credit scoring, fraud detection, revenue forecasting
Manufacturing: Optimization, troubleshooting
Medicine: Medical diagnosis
Telecommunications: Churn prediction, Quality of service optimization
Web mining: Search engines, programmatic
There are many more use cases which are in proof of concepts phase and many of them will be reality in not so distant future.
ML is going to stay and influence future generations. It will be wise to understand ML as a data science practitioner or otherwise to cope up with the fast changing technology world.
In the next section we will explore key terminology from ML and delve deep in each of these topics.
