In Unsupervised Learning; data have no target attribute. In this learning algorithm takes as training examples the set of attributes/features alone. This is our second post in this sub series "Machine Learning Types". Our master series for this sub series is "Machine Learning Explained". Unsupervised Learning; is one of three types of machine learning i.e. This post is limited to Unsupervised Machine Learning to explorer its details.

This is part 1 of 2 parts story on DeepLearning & basic terms which revolve (may evolve around as well) around it. Deep Learning is a very young field, where theories aren't strongly established and views quickly changes almost on daily basis. Deep Learning is at the cutting edge technology break through. This depicts what machines can do (still very new and at basic level), and developers and business leaders absolutely need to understand what it is and how it works. "I think people needs to understand that deep learning is making a lot of things, behind the scenes, much better" – Sir Geoffrey Hinton With lots of noise I can say "Deep learning is undeniably mind-blowing" and "deep learning can be used with too much of ease to predict the unpredictable".

This is part 1 of 2 parts story on DeepLearning & basic terms which revolve (may evolve around as well) around it. Deep Learning is a very young field, where theories aren't strongly established and views quickly changes almost on daily basis. Deep Learning is at the cutting edge technology break through. This depicts what machines can do (still very new and at basic level), and developers and business leaders absolutely need to understand what it is and how it works. "I think people needs to understand that deep learning is making a lot of things, behind the scenes, much better" – Sir Geoffrey Hinton With lots of noise I can say "Deep learning is undeniably mind-blowing" and "deep learning can be used with too much of ease to predict the unpredictable".

Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you give, the better it is – Sir Geoffrey Hinton (Google). The true challenge to Artificial Intelligence is to prove and solve the tasks that are easy for human to perform but hard to describe formally. Problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images. In deep learning this is the task we try to solve at AILabPage research. At the same time I also claim It is absolutely wrong to call Deep Learning as Machine Learning (in my opinion).

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. 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.

This is our first post in this sub series "Machine Learning Type" under master series "Machine Learning Explained". We will only talk about supervised machine learning in details here. Machine learning algorithms "learns" from the observations. When exposed to more observations, the algorithm improves its predictive performance. Supervised Learning is becoming a good friend for marketing business in particular.