Introduction to Machine Learning

What is machine learning?

Machine learning is an application that can make the system to give the output from the past experiences. it is the process of making the system to learn from the past experiences without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.


Why machine learning?

Machine learning models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.


Opportunities in machine learning

There are many opportunities for machine learning in the modern world. The demand is increasing day by day


  • Software Engineer
  • Data scientist
  • Software developer
  • Designer in Human-Centered Machine Learning
  • Machine learning engineer


According to a report, machine learning engineer is the best job of 2019 due to growing demand and high salaries. The career boasts a current average salary of $146,085 with a growth rate of 344 percent last year


What is machine learning models?

A machine learning model can be a mathematical representation of a real-world process. To generate a machine learning model you will need to provide training data to a machine learning algorithm to learn from. A machine learning (ML) algorithm is essentially a process or sets of procedures that helps a model adapt to the data given an objective

While training for machine learning, you pass an algorithm with training data. The learning algorithm finds patterns in the training data such that the input parameters correspond to the target. The output of the training process is a machine learning model which you can then use to make predictions.


Why R and Python is popular?

Python is very accessible and easy to learn. Python boasts a high number of libraries for data munging, manipulation, collection, and machine learning.  Scikit-learn is package in python that contains tools for data mining and analysis that boost Python’s excellent machine learning usability. Another package called Pandas offers developers high-performance structures and data analysis tools, which helps shorten the development time.

R is perfect for data analytics and visualization allows the user to build machine learning models prototype which can be used for future use.

It consists of many libraries and tools – just like Python, R has plenty of packages that improve its performance in machine learning projects.  Caret is package in R that gives a boost to R’s machine learning capabilities with its set of functions that make creating predictive models more efficient

Likewise may packages are available in python and R ,so it is highly preferred