Challenges before existence of Machine Learning:
Before machine learning became so widely used AI systems were mostly rule based which means that these rules failed in real life scenarios, most of these state representations are been manually coded which is hand coded as everyone know hand coding is quite difficult and the main disadvantage was that these problem scenarios or test scenarios would fail outside for what the rules were coded for that means they would not be useful in real life application even though work well in their controlled environment.
Why Machine Learning:
Machine Learning has changed our lives in several significant ways, it allows powerful processing which means it can process through far more complicated data hence decision making awaiting will much more well founded and the predictions are much more accurate, it not only allows powerful processing but also quicker processing that is more work is done in quicker time. When we think about storage of large sum of data, with machine learning we can perform affordable data management . Machine learning method is also considerably less expensive. Finally the important thing is when we get more and more data the more and more complexity it becomes, machine learning allows us to analyze these complex big data.
What is Machine Learning:
Machine learning is a method that allows computers to imitate and adapt to human life behavior, so the machine analyzes the past data learns from the data and make decisions or predictions so the computer is creating its own logical solutions all of which without any human assistance, these systems will grow, develop themselves when exposed to new data.
Categories of Machine Learning Algorithms:
This algorithm stands on two primary pillars known as supervised learning and unsupervised learning. Some humans also remember a new discipline of study—deep learning—to be break away the question of supervised vs. Unsupervised learning.
Supervised learning is when a computer is offered with examples of inputs and their favored outputs, the purpose of the laptop is to be taught a common formulation which maps inputs to outputs. This can also be further broken down into:
Semi-supervised studying : which is when the computer is given an incomplete training set with some outputs missing
Active learning: which is when the laptop can most effectively receive coaching labels for a very limited set of instances? When used interactively, their training sets will also be awarded to the consumer for labeling.
Reinforcement learning : which is when the learning information is simplest given as feedback to the software’s actions within the dynamic environment, equivalent to using a auto or playing a sport in opposition to an opponent
In contrast, unsupervised studying is when no labels are given at all and it’s as much as the algorithm to search out the structure at it’s enter. Unsupervised finding out generally is a purpose in itself after we best ought to detect hidden patterns. Deep studying is a brand new field of study which is encouraged by using the structure and performance of the human mind and based on artificial neural networks as an alternative than just statistical principles. Deep finding out can be used in both supervised and unsupervised strategies.
Future of Machine Learning:
Deeper Personalization: It means Direct marketing towards your interests and advertisements based on your profiles.
Self Driving Cars : Although it’s the concept that’s been worked out right now it’s not one that reaches completion but in the near future we might have cars that can run flawlessly even on the most crowded roads.
Smarter Investment Opportunities : Machine learning could ensure that your profit is maximized taking into consideration of your past purchases and the current market scenario to suggest smarter investment opportunities.
Machine learning provides greater opportunities in terms of the job that it can provide. Google trends shows increased interest in machine learning, machine learning market size is also expected to grow through 1.03 billion dollars to 8.81 billion dollars by the year 2022; this indicates a bright and clear future for individual skill in the concept of Machine learning.