Whether you want to increase your knowledge about ongoing technological trends or you want to start implementing machine learning, this beginner’s guide will help you all. I know Machine learning seems a bit daunting, but all your fears are about to get a good riddance.

What is Machine Learning?

Artificial Intelligence (AI) is a way to simulate human intelligence in machines. AI is like creating babies out of machines. Just like how you teach a baby to everything from scratch, you also have to teach machines everything from scratch. The only difference is that babies have brains that are responsible for processing all the information they absorb through their surroundings. But machines cannot do that. For machines, we have to create algorithms that can try to mimic the functionalities of the human brain. This is where Machine Learning (ML) comes into the picture. ML is one way to realize Artificial Intelligence.
Machine Learning as defined by Wikipedia is, “ Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.”
As it is evident from the definition, ML is about algorithms that are designed to carry out tasks like, analyzing high volume data, making predictions based on data, automating mundane tasks, etc. There are various approaches to machine learning which are discussed next.
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The following are the methods to achieve ML:
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning

Supervised Learning

Supervised Learning is applied if the dataset we have is labeled. It matches input to an output based on input and output pairs. For example, if we want to recognize handwritten digits, the data will have correctly labeled images of handwritten digits. After applying the necessary algorithm, the ML model will be equipped to predict digits written by any person when given as input. There are two ways to approach Supervised Learning:
Regression
Classification
Regression:

In regression, we predict continuous values. It is used to estimate value like housing prices, human lifespan, salary prediction etc. Depending upon the type of problem to be solved, various regression techniques are available like, Linear Regression, Logistic Regression, Polynomial Regression, Ridge Regression etc.
Here, I will explain Linear Regression to give you an idea of how regression works.
Linear regression is used for predictive analysis. This model is made up of linear variables. It models the relationship between a single input independent variable and a single output dependent variable using the best fit line.
The best fit line is a line that is obtained using the most common mathematical equation: equation of line.
y=a_1*x_1+a_2*x_2+a_1*x_3+⋯+a_n*x_n+b
Here, an are coefficients and xn are variables and b is bias. In it, we just realize the importance of each feature and make a prediction.
Classification.

Thus, the advent of machine learning courses are now going too up and students from all parts of the country are now embracing it.