"The problems we currently face can't be solved at the level of thinking that created them"
Machine Learning is all the buzz these days but have you ever wondered what machine learning is, The perfect time to use Machine Learning and what makes up a Machine Learning system.
Well join me and let's find out.
MEANING OF MACHINE LEARNING
I'll give you two meanings for the word machine learning the easy one and the complicated one.
Machine learning is simply the use of data to teach computers so they can perform operations without your programming them to do so.
This is when a computer program learns from experience E with respect to some task T and some performance measure P
if its performance P on Task T improves with more experience E then Machine Learning is said to have taken place.
MOMENTS WHERE MACHINE LEARNING SHINES...
Machine Learning is great and all but there are some times where using it makes your project not as good as it should be
and then there are times when machine learning just simply seems perfect and below are those times:
1.)Problems for which existing solutions require a lot of hand-tuning or long a long list of rules.
2.)Complex problems for which there is no good solution at all using traditional approaches.
3.)Fluctuating environments: A machine learning system can adapt to new data.
4.) Getting insights about complex problems and a large amount of data.
DIFFERENT MACHINE LEARNING SYSTEMS
Machine Learning Systems can be classified into broad categories:
1.) Whether or not they are trained with human supervision,(Supervised learning, Unsupervised Learning and
2.)Whether or not they can learn incrementally or on the fly(Online vs Batch Learning)
3.)Whether they work by simply comparing new data points to known datapoints or whether they detect patterns
in the training data and build a predictive model, much like scientists do (Instance-based versus model-based learning)
NOTE: These criteria are not exclusive; you can combine them in any way you like for instance a state of the art spam filter may learn on the fly using deep
neural networks model trained using examples of spam data this makes it an online, model-based supervised learning system.
Well to conclude;
1.)Machine Learning is simply the act of using data to teach computers how to do something without them being programmed.
2.)There are some special times where using Machine learning algorithms will be perfect.
3.)Machine Learning is broadly into categories depending on what they do and how they perform actions.