Machine Learning

What is Machine Learning (ML)?

Machine Learning is programming machines to make it capable of taking decision itself based on their experience.

Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer. The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results.

Machine learning combines data with statistical tools to predict an output. This output is then used by corporate to make actionable insights. Machine learning is closely related to data mining and Bayesian predictive modelling. The machine receives data as input, use an algorithm to formulate answers.

Machine Learning v/s Traditional programming

Traditional programming differs significantly from machine learning.

In traditional programming, data and program are run on the computer to produce the output.

In Machine Learning, data and output are run on the computer to create a program. This program can be used in traditional programming.

Types of Learning

Learning is the process of converting experience into expertise or knowledge.

Learning can be broadly classified into categories, as mentioned below, based on the nature of the learning data and interaction between the learner and the environment.

•  Supervised learning:

o   The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.

o   Supervised learning can be further classified into two types:

Ø  Regression

Ø  Classification

o   Supervised learning is commonly used in real world applications, such as face and speech recognition, products or movie recommendations, and sales forecasting.

• Unsupervised learning:

o   The program is given a bunch of data and must find patterns and relationships therein.

o   It is the opposite of supervised learning.

o   Unsupervised learning is used to detect anomalies, outliers, such as fraud or defective equipment, or to group customers with similar behaviours for a sales campaign.

•  Semi-supervised Learning:

o   If some learning samples are labelled, but some other are not labelled, then it is semi-supervised learning.

o   It makes use of a large amount of unlabelled data for training and a small amount of labelled data for testing.

o   Semi-supervised learning is applied in cases where it is expensive to acquire a fully labelled dataset while more practical to label a small subset.

•  Reinforcement Learning:

o   Learning data gives feedback so that the system adjusts to dynamic conditions to achieve a certain objective.

o   The system evaluates its performance based on the feedback responses and reacts accordingly.

o   AI types like it, it is the most ambitious type of learning.

Similarly, there are four categories of machine learning algorithms as shown below −

• Supervised learning algorithm
•  Unsupervised learning algorithm
•  Semi-supervised learning algorithm
•  Reinforcement learning algorithm

However, the most commonly used ones are supervised and unsupervised learning.

Machine Learning Process

Purpose of Machine Learning

Machine learning can be seen as a branch of Artificial Intelligence (AI), since, the ability to change experience into expertise or to detect patterns in complex data is a mark of human or animal intelligence.

As a field of science, machine learning shares common concepts with other disciplines such as statistics, information theory, game theory, and optimization.

As a subfield of information technology, its objective is to program machines so that they will learn.

However, it is to be seen that, the purpose of machine learning is not building an automated duplication of intelligent behaviour but using the power of computers to complement and supplement human intelligence. For example, machine learning programs can scan and process huge databases detecting patterns that are beyond the scope of human perception.

Applications

Below are some of the applications of Machine Learning:

• Web search: ranking page based on what you are most likely to click on.
•  Finance: decide who to send what credit card offers to. Evaluation of risk on credit offers. How to decide where to invest money.
•  E-commerce:  Predicting customer churn. Whether or not a transaction is fraudulent.
•  Space exploration: space probes and radio astronomy.
•  Robotics: how to handle uncertainty in new environments. Autonomous. Self-driving car.
•  Social networks: Data on relationships and preferences. Machine learning to extract value from data.
•  Debugging: Use in computer science problems like debugging. Labour intensive process. Could suggest where the bug could be.

Challenges

The primary challenge of machine learning is the lack of data or the diversity in the dataset. A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time. A machine needs to have heterogeneity to learn meaningful insight. It is rare that an algorithm can extract information when there are no or few variations. It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.

Conclusion

Machine learning is used by many known organizations in many different fields. However, this technology is quite new and not very mature, so it contains many security and ethical problems. However, this technology can help us in dealing with a lot of problems such as security. Machine learning has a lot of scope and with advancement in technology, it will get better n better.