Let's Start With the M of Machine Learning

What Is Machine Learning?

Machine learning is something which is similar to the humans. Basically, humans learn from their past experience so machines do the same. First, the machine is trained with data, next it learns how to predict based on the past experiences,i.e it gives an output for a new task or new data.

To put in simpler terms, think of it this way, we have been trained to do a few routine tasks in a way-like eating, sleeping, brushing teeth in the morning etc.. the knowledge increases with experience and so does for the machine. It learns from past experiences and the data which is given.

Machine learning is a subset of artificial intelligence that helps in predicting the output more accurately based on a few computer algorithms. 

The aim of the algorithm is simple, take the data, read it, apply a few models to see which one suits the best for the given problem and finally predict the output for a new data.

Why Machine Learning?

⦁ To reduce the work load for humans.

⦁ To do the complex task in an easy way.

⦁ Easy prediction or to classify the data easily.

A complex algorithm or source code is developed and trained in the computer, which is used to identify data and predicts the output for the new data given by the user.

It helps us in analyzing the complex data easily by developing algorithms which predict accurate results and analysis. 

It helps us in taking decisions when there is a complexity in data known called as classification. Mostly, machine learning helps organisations in creating the system that can learn, adapt, predict and operate on their own.

Where Can Machine Learning Be Applied?

⦁ Decision making in any companies, industries, institutions. For example, bank loans whether the customer is able is take loan or not with data (credits, income). 

⦁ Predictions in sales and marketing, stocks marketing, medical diagnosis.

⦁ Machine learning can be applied in a variety of areas, such as in 

• Speech recognition, Image recognition.
• Face detection, Fraud detection, Email fraud detection. 
• Traffic prediction, Marketing, Trading.
• Automatic Language translation, self-driving car.
• Recommendations, google search.

Types of Machine Learning

Machine learning is classified into four types

⦁ Supervised Machine Learning

⦁ Semi-supervised Machine Learning

⦁ Unsupervised Machine Learning

⦁ Reinforcement Machine Learning 

Supervised Machine Learning

Supervised learning is a type of machine learning where machine is trained with the labelled dataset so that if any new data is given as input it can easily predict the output. 

Say X is the input then you train the data in such a way that it should give Y as the output.

Here, algorithm generalises the inputs with the help of trained data and predicts the accurate results.

There are two types of supervised learning problems

⦁ Classification – Supervised learning problem that involves predicting class label.

⦁ Regression – Supervised leaning problem that involves predicting numerical label.

For example, classifying the type of animal whether it is cat or dog or rabbit.

Unsupervised Machine Learning 

Unsupervised learning is a type of machine learning where machine is trained with the unlabeled dataset so that if new data is given as input it identifies the similar data and categories accordingly.

There are two types of unsupervised learning problems:

⦁ Clustering – Unsupervised learning problem that involves finding groups in data.

⦁ Association – unsupervised learning problem that involves summarizing the distribution of data.

For example, colours of the flowers according to the colour of the flower clusters will be formed.

Semi-Supervised Machine Learning

Semi-supervised learning is a type of machine learning where training data contains both labelled data and unlabeled data. It falls between the supervised learning and unsupervised learning. This makes the available data more efficient. 

For example, speech analysis labelling the audio, image analysis labelling the images with the help of known images. 

Reinforcement Machine Learning

Reinforcement learning is a type of machine learning where it learns from the environment and predicts or make decision by itself. It always learns from its previous experiences and predict the future events.

For example, if there is a fire it learns that we should not touch it. So, it will not touch the fire.

The goal of the robot is to get reward that is diamond and avoid the fire. The robot learns by trying all the possible paths and then choosing the path which gives him the reward with least hurdles.

All in all, machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. But, it is believed that it's the next big thing.

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