In the dawn of the digital age, data is the modern currency. Businesses rely on consumer data to enhance their growth. In the age of automation, the system collects data and, processed, and then used as a tool for data analysis. Whenever you visit a website and spend some time over it. Web-Cookies collects data describing your browsing behavior in a particular application. Google knows your location all the time. E-commerce platform keeps track of the choices that you make or even those that you didn't make. It helps them to offer user attractive and enticing deals on the products that you desired to purchase. Day to day the science of data is advancing, making machines more capable and independent.

Machine Learning is the science of enabling machines to learn on their own without being emphatically programmed for each distinctively similar tasks.

Lets further break down the word Machine Learning. The machine would be any programmed computer or SOC (System on Chip). Learning whereas indicates the degree of smartness a particular system achieves based on the gained knowledge. For any hardware, its degree of smartness lies in how well i'ts programmed.

Say a system needs to determine whether the input picture provided is of the boat or not? Now there are two approaches to observe this problem.

  1. We have pre-access to the picture and, all we have to do is to write a code to have a quick check with the provided input. There is a strict constraint with this solution, this model works only for a particular image of the boat. Any image (other than the predetermined image provided) as input the model will fail.

Training Image

Test Image

  1. Secondly, we use the techniques provided by machine learning to determine whether the image is of the boat or not. By giving a set of training images and sometimes not even that, our system based on a learning model trains itself to be self-reliant to make an efficient decision of determining the desired result.

Learning is an intelligent byproduct developed on the basis of past experinces. This is how we as humans learn and grow. This is the approach an efficient machine learning model suffice.There are three key points to keep in mind while understanding an ML approach i.e. Experience, Task, Performance in the particular order.

Therefore, we can define machine learning more formally,

"A model is said to be an efficient Machine Learning Model if and only if a computer, on the experience based on the past task improves it's current performance while conducting a new task,on it's own."

Approach of ML Model

After looking up to a problem ask yourself three important questions-

Learning is successful when a consistent improvement reflected after each trial. For prediction and recognition, we frequently use Machine Learning algorithms. At first, know the purpose of predicting a solution for a particular task? Further, based on the provided data, lay down all the possible approaches that can be applied to fulfill the aim. Finally, identify the most suitable method for a working ML model. The higher efficiency of our machine learning model increases the probability of predictions.

Machine Learning Examples

Learning algorithms are working almost everywhere in this modern data-dependent world. Whether it is Database solutions (Medical records, human disease detection) or Product recommendation systems (Amazon, Netflix), machine learning is everywhere.

Some real-life examples of machine learning are:

  1. Spam Detection - You might have been using e-mail services (Gmail, yahoo) for a long time. There is a folder where the application accumulates all the spam emails. Once you find any received mail to be fraudulent or unworthy, you chose the option to spam the mail. Once a particular type of e-mail is under a spam scanner, the application spams all other similar emails from different sources. This spam detection model trains on machine learning algorithms.
  2. Google News - In today's world where millions of tweets are done every single day carrying all types of news from a variety of forte, machine learning helps in organizing them elegantly in order to become easily accessible and readable at the same time. Clustering is one of the techniques to group similar news from different sources in one place so as to avoid confusion.
  3. Google News - In today's world, every single day, users post millions of tweets carrying all types of news from a variety of forte, machine learning helps in organizing them elegantly to become easily accessible and readable at the same time. Clustering is one of the techniques to group similar news from different sources in one place to avoid confusion.
  4. Voice Recognition - You have seen voice assistants in smartphones like Siri in apple, Google Voice in Android, or Cortana in Microsoft. All are good examples of NLP (Natural Language Processing) learning models. These voice bots are smart enough to assist people with their daily basic tasks taking commands over voice. The model learns on its own using deep learning and ANN (Artificial Neural Network) daily.

Further, it is very much important to understand the demands of a particular learning problem. Next, we will discuss the types of machine learning.

Types of Machine Learning

Developing a machine learning model requires understanding the nature of the problem. There are numerous ways to look up to a task but only one efficient way to resolve it. The performance of an ML model lies upon the efficiency percentage i.e. possibility of success on the test data. The performance of the efficient learning model improves with each iteration.

The important question is What should be the process of learning? The difference is in the approach to make a model that enables a machine to learn itself. Let's envision it with a relative example.

On this note we categorize machine learning. There are mainly three types of machine learning - Supervised Learning, Unsupervised Learning, Semisupervised Learning and Reinforcement Learning. I will breifly define them here and while proceeding later in the course we will understand them in much detail.

Supervised Learning

In Supervised Learning, we know the possible outcome. We adequately provide historical data to the standard algorithm and get the desired result. We trained the model on a labeled dataset. The labeled dataset means we have both input and output parameters.

Image Classification represents a classic example of Supervised Learning. The algorithm trained enough to identify the image accurately. When the user provides the image to the system, based on its data, it will recognize the type of image.

Supervised Learning Algorithms

Let's understand it with the help of an example. Let's say you have a car. And your kid recognizes it. One day you decided to go on a long drive. During the journey, the child yet identifies the vehicles driving on the way without your help based on the information he accumulates from home (like four tires, steering, wipers, etc.).

It is unsupervised learning.

Unsupervised Learning Algorithms

Semisupervised Learning

Supervised learning is often time-consuming because it expects to label the data every time. In practical scenarios, it is not possible to always have a labeled dataset. We have partially labeled data. When we train the model using the partially labeled data points, it is traditionally called semi-supervised learning.

Voice Recognition is a classic example of semi-supervised learning models. Labeling audio files remain a time-consuming and highly resource-intensive task. Using the semi-supervised learning improves the accuracy of the voice recognition model.

Reinforcement Learning

In Reinforcement Learning system learns from experience. For every action, the system gets the rewards or penalties based on the move. If it has an adverse effect, then the system avoids it next time. All advanced robots implement Reinforcement Learning Algorithms. There is no concept of label or unlabeled data but an initial state from where the model begins.

These are very brief ideas about the types of machine learning. The deeper insights shall be explained in subsequent sections.


In a typical Machine Learning Project, we need data and a model to train. Eventually, we applied the model to predict, whenever a new case or data is feed into the system.