Machine Learning 101

So how can we master a skill then?

Due to all these ever changing environment variables,It is said that for one to truly master a skill such that he can perform well no matter what the circumstances it takes 10,000 hours of practice.

But who has that much time, energy or patience right?

This is where a intelligent system comes into play. A machine learning agent can quickly run 1000’s of simulations and adapt to various environment conditions and learn to hit the target consistently consistently most of the time.

Three basic learning models :

1> Supervised Learning

When, Why and How:

When the system is provided input and their corresponding labels and is then expected to predict the label for some previously unknown set of input.

METHODS:

When we need the output in a continuous range then it’s called a regression problem. To Solve regression problems we aim to find the best fit curve such that root mean squared error of given data is minimum then use that curve to predict values of new data. Example-predicting price of some product in future based on past records , weather prediction etc.

Classification vs Regression

When we need to categorise some data into some finite discrete classes(like 0,1,2…) then it becomes a classification problem. To make a binary classifier i.e. which just has two possible outcomes 0 or 1 , yes or no we can use perceptron algorithm where we aim to find the equation of a line which separates given training data such that data points on one side of the line are of one class and data points on the other side are of the other. Example- classifying mail as spam or not spam , finding the presence of tumour from x-rays etc.

2> Unsupervised Learning

When Why and How:

When we only have data features but no labels and we have to find patterns in data and group data having similar features together . Unsupervised learning is adopted for problems where the answer is not known.

METHODS:

Clustering : There are various algorithms to perform clustering like K means clustering, DBSCAN and hierarchical clustering , In K means clustering algorithm we need to enter the value of K beforehand that is how many clusters we want to find in this ,whereas there is no such limitation in hierarchical model.

Clustering of data points

Used in – Recommendation system , Social Network analysis , image segmentation, market trend analysis etc.

3> Reinforcement Learning

When Why and How:

This is quite different from above two methods here we do not provide any data to train on we just give the equipment to perform a task and then set up trial and error based reward system where for each desired interaction of agent with environment , agent gets rewarded and builds upon that feedback. It is same as how a dog is trained , in the beginning he does not understand any command but when he randomly follows our command once and gets rewarded for that he repeats that action on the same command again for reward and eventually learns to perform the task without rewards.

Reinforcement Learning process

Example: Reinforcement Neural Networks(RNN), Long Short Term Memory networks (LSTM). 
Used in: Language modelling and translation, QLearning for atari games, handwriting recognition/generation, keyboard predictions etc.

Now, what is deep learning and what’s so special about it?

“Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.”

While the machine learning algorithms depend on programmer to provide some data based on which it can give results, deep learning is capable of finding out the features necessary for the task all on it’s own.

Machine Learning vs Deep Learning

As shown in above graph for less data generic machine learning techniques may outperform deep learning since it takes more time and resources to train. But given enough time and data it will outperform everything else.

One downside of deep learning is that as the model gets deep and layers get dense interpretability of the result gets lost, i.e. understanding the process based on which we are getting results.

Conclusion:

We have come a long way in this field and applications of machine learning,deep learning and AI can be found everywhere now , from classification of search results on google to facebook friend recommendations to image recognition systems to personal voice assistants to self driving cars , But it’s just the beginning and there are still immense possibilities out there that can be automated using using these techniques.

There is hope that we can one day make an ultimate AI based system which can solve any variable problem X answers to which are still unknown to humanity.

read original article at https://medium.com/@tripathi.yugandhar/machine-learning-101-aa3651171382?source=rss——artificial_intelligence-5