With large torrents of continuously varying and evolving data driven by scientific/industrial advancements and commercial developments like never before, conventional statistical/analytical methods have become inadequate, which tells us why Machine learning is so relevant and inevitable today.
So, what is Machine Learning? Machine Learning is the science in data analytics that makes the computer respond to data intelligently as a human being naturally does; i.e., it makes informed decisions based on what it learns from the information it handles, instead of blindly acting upon a certain readymade set of instructions. The algorithms used in machine learning are capable of recognizing patterns in the data received and continually learn from them. Machine learning finds its application everywhere – medicine, manufacturing, business and commerce, computing, banking and finance, R&D… you name it.
Types of Machine learning algorithms
Machine learning algorithms can be broadly classified into two types: Supervised learning and Unsupervised learning.
- Supervised learning algorithm model is built on a set of historical data with inputs and desired outputs. A machine can learn from it to make precise predictions as responses to current data. This type finds use in predicting future values of a continuously varying parameter, as in share prices in the stock market.
- Unsupervised learning uses no previous data for reference. Instead, the machine identifies inherent patterns and structures in the incoming data and classifies them into different clusters based on characteristic differences. This type of learning is particularly useful to explore data to study their nature and organize them.
Machine learning methods
Some of the standard methods data scientists have developed in machine learning are:
- Regression. A Supervised method in which an output is predicted based on some input and the input-output relationship of historical data. When the previously available values are plotted graphically, an extension of the plotted data can tell us what results to expect for future values, of course with some degree of error.
- Classification. Data is segregated and categorized based on characteristics and behavioral patterns. The knowledge about past data helps in deciding the categorization.
- Clustering. An unsupervised version of ‘Classification.’ Data is grouped into clusters by grouping values of similar characteristics, without referring to any previous guidelines as to how to classify them.
- Ensemble method. This is not one single method; it combines different predictive machine learning methods to get the best result. It makes use of the best feature of each constituent.
Deep learning, reinforcement learning, etc. are some other machine learning methods.
Programming languages for Machine learning.
Numerous languages can be used for machine learning. The choice will depend upon the kind of application and programmer preferences. Some most popular ones are listed below:
- Python. An open-source language with a considerable number of libraries and resources for Machine learning, making it the most popular Machine learning language.
- R. It is popular with many common Machine learning methods like ‘Regression’ and ‘Classification.’ Someone with previous programming knowledge can pick up R easily.
- C++. Its dexterity in handling large amounts of resources and high processing speeds make it an excellent Machine learning language.
- Java. Good at implementing algorithms, this language also boasts the speed in execution, which also makes it suitable for Machine learning.
How to learn machine learning
Here are a few of the resources for those enthusiastic about becoming an expert in Machine learning:
- Online Courses: Google AI, Coursera etc. provide complete courses in Machine Learning covering all essential aspects.
- Communities: Lively online communities like Stack Overflow and GitHub can answer your queries and avail knowledge sharing.
- Preparatory guides: Sites like ‘Elite Data Science,’ ‘Machine Learning Mastery,’ etc. provide valuable advice to prepare yourself well before embarking upon your journey of learning Machine Learning.