Hello, readers! Today, we would be having a look at one of the most in-demand topics in the domain of Data Science and Automation — Introduction to Machine Learning in R programming, in detail.
So, let us begin!!
Table of Contents
What is Machine Learning?
Machine Learning is an important aspect of data science wherein we make use of various algorithms to work on real life scenarios and make predictions on the cases used to put the work at ease.
Machine Learning offers us various algorithms that learn from the historic data values and then make predictions on the data that is to be tested. By this, we understand that the predictions made by the algorithms can help us in various types of analysis and understanding of the patterns in the various sectors of the market.
Let us consider the below example to make ourselves comfortable with the idea and concept of Machine Learning:
Consider an online business store such as Amazon. What do you think, how do they analyze what is your most liked or looked for products from their available products?
How do you get personalized choices of products every time you look for something in the application?
This is when Machine Learning comes into the picture. The algorithms are used to understand and detect patterns from the search history of the customers to give a more personalized touch to the application.
These algorithms can be improvised as and when needed with regards to the outcome values that are to be predicted. Thus, it makes the entire idea of learning and prediction look like a user-friendly process to work with.
Having understood about the concept of Machine Learning, let us know focus on the main variants of the same.
Variants of Machine Learning
Machine Learning algorithms can be broadly classified into the below three types:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforced Machine Learning
Let us have a look at each one of them in detail!
Supervised Machine Learning Algorithms to know!
Supervised Machine Learning is a branch of Machine Learning wherein the model learns from the historic data. That is, it detects patterns and understands the concept from the historic data fed to it and then makes predictions on the fresh data fed to the built-up model.
Moreover, Supervised Machine Learning algorithms make use of labeled data to learn from the provided historical inputs.
For the same, we feed the model with historic data or trends which we want the model to understand and learn from. The model deals with it in the below manner:
- At first, it is very important to check whether the data is categorical or numeric in nature. Though Supervised Machine Learning models work on both types of data, they do have different types of algorithms to deal with them.
- We then perform cleaning and pre-processing on the data values. This is done to prepare the data for modelling.
- After which, we segregate the data into training (learning) and test data sets. We then apply the training data set to the model for it to detect the patterns and learn the trends.
- At last, the model is fed by the test data to test the accuracy of the model for the unknown values.
Supervised Machine learning algorithms are further divided into the below types:
These types of algorithms deal with categorical data. That is, it is used when the target or the response variable is a categorical value such as ‘yes’ or ‘no’, ‘0’ or ‘1’, etc.
Few mostly used Classification algorithms,
These algorithms work only on numeric data values i.e. only when the response value is numeric and continuous in nature. For example, the continuous values such as the age of customers or count of citizens in a particular city.
Popular Regression algorithms,
Unsupervised Machine Learning concepts!
Unsupervised Machine Learning algorithms do not make use of labeled data. That is, it does not learn from the labeled data that is usually fed to the models.
Rather, it makes use of unlabeled data to train the models for predictions. That is, the model learns from the underlying patterns and dependencies based on the end criteria.
For example, the idea of identifying the products which would be bought together. Like, the chance of a customer who has bought bread would also go for Jam or butter? Yes, it is achieved through Unsupervised Machine Learning algorithms.
Popular Unsupervised Machine learning algorithms,
- Clustering (K means Clustering)
- Association Approach
Reinforced Learning includes algorithms that make use of a trial and error approach to learn, detect patterns and grow progressively.
By this, it learns from the reward-punishment approach. The model is fed with the rewards and penalties from the input and the corresponding output at stages.
By this, we have come to the end of our today’s topic on Machine learning with R programming. Feel free to comment below, in case you come across any question.
For more such posts related to Machine Learning with R programming, Stay tune and till then, Happy Learning!! 🙂