Some of the most important question we first ask about any topic are What ? Why ? How ?. You will find a lot of paid courses on Machine learning. Here I will creating a complete series of Blogs on Machine learning after that deep learning and Artificial Intelligence. So lets start with the base question of any topic. What is it ?
“ A computer would deserve to be called intelligent if it could deceive a human into believing that it was human “ – Alan Turing
What is Machine Learning ?
Machine Learning is a branch on science that deals with programming the system in such a way that they automatically learn and improve with experience.
To put it in simple words think of any task a human does like arranging a desk or shelf. First we put thinks in order how good they look. but, after some time we arrange it according to how frequently we use things on the shelf. Like, we put the book that we are reading in a place which is easily accessing an not behind a pile of Books having good cover page. This is done when you get the experience and come to a conclusion that accessibility is more important that appearance in case of arranging the books.
Similarly, In Machine Learning we use data for gaining experience ( Experience is ML might be as simple as changing value of few variable in a formula by using the data).
Learning Means recognizing and understanding the input data and making wise decisions based on the supplied data. Algorithm builds knowledge from specific data and past experience with the principles of statistics, probability theory, reinforcement learning, etc.
What are the two broad categories of Machine learning tasks ?
1. Supervised Learning
2. Unsupervised Learning
Supervised Learning
Supervised learning is where you have Input variables ( x ) and an output variable ( y ) and you use an algorithm to learn the mapping function from the input to the output.
y= f(x)
The goal is to approximate the mapping function so well that when you have new input data ( x ) that you can predict the output variable ( y ) from that data
In Supervised learning the model is trained on a labelled dataset. Labelled dataset is one which has both input and output parameters.
It is called supervised learning because the process of an algorithm learning from the training data set can be thought of as a teacher supervising the learning process
The algorithm iteratively makes prediction on the training data set and is corrected by the teacher ( correct answer ).
There are basically two type of supervised learning
- Classification
- Regression
1. Classification
The main goal of classification is to predict the target class ( Yes/No ), ( True/False ), ( animal/human )
These algorithms are used when the value of target output variable is discrete as { Yes| No }
Binary or binomial classification is the task of classifying the elements of a given set into two groups on the basis of a classification rule
Multi-class or multi-nomial classification is the problem of classifying instances into one of three or more classes
Example : classifying email to predict if the email is a Spam mail or not
2. Regression
Regression is a technique for predicting the value of dependent variable as a function of one or more independent variable in the presence of random error.
In simple terms Regression is used to predict continuous values. It is a statistical processes for estimating the relationships between a dependent variable and one or more independent variables.
Example: Predicting house prices of a particular area.
Unsupervised Learning
Unsupervised learning is where you only have input data ( x ) and no corresponding output variable
The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data
It is called unsupervised learning because unlike supervised learning there is no Labelled data that means no correct answer or no teacher.
Unsupervised learning problems can be further grouped into two parts
- Clustering
- Association Rule Mining
1. Clustering
A clustering problem is where you want to discover the inherent grouping in the data such as grouping customers by purchasing behavior
In simple terms Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups
Example : Suppose you have data of all the products sales from your store and you want to assign priority to customer in order to boost sale by giving offers according to priority. you can use clustering algorithms to generate cluster of people and assign priority accordingly.
2. Association Rule Mining
An association rules learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to by Y.
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.
Example: Market basket analysis – It is used in deciding the location of items inside a store. ex.If someone buys a packet of milk also tends to buy bread at the same time.
We will get into details of explanation of each algorithm and task Blog by Blog.
Now lets get to the second important question …
Why Machine Learning ?
Most of us know the answer to this question. I will try to example in too. Humans, most of the time have a lot of things to focus on. Now just image if we were made to do one and only one task and now if we could do that task without the need to take break or rest.
With the help of machine learning we can focus on a single task with more precision than human and machine do not need rest or time off. Machine learning is a way to reduce human efforts.
Some of the task are very complex for a human mind and some are easy but takes time. Example take simple calculation but with big number. Best of best would still require a few second. But, for machine 1 second is similar to eternity depending on the processing power the machine has.
The base reasons are machine is faster, Machine needs no rest and if we have the resources to make machine do our activity we could focus on much more important problem in the world.
There are a lot of reasons to this why statement. Give some of your reasons in comment section.
The final question
How does it work ?
I will be creating a Series of Blogs in which I will try and explain most of the models of machine learning and how to implement them in python and how the algorithm works. Stay Tuned…
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