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Machine Learning Applications that can be built with Python


By NIIT Editorial

Published on 25/12/2020

6 minutes

Machine learning (ML) enables computers to learn using certain algorithms and techniques. Python is used to build several machine learning applications. It leverages machine learning algorithms to enable the computers to understand user intent, learn, make predictions and refine the results. In this article, we’ll go over some ML applications that can be built with Python.

Machine learning algorithms in Python

 

The tasks of Machine Learning Algorithms in Python can be divided into two major categories — Supervised and Unsupervised Learning.

Supervised Learning

Supervised learning is a task where a Python machine learning algorithm is trained to find patterns using a sample dataset. The supervised learning algorithm then uses this training to classify entirely new inputs (without targets) on future datasets.

There are three provisions under Supervised Learning — Semi-Supervised learning, Active Learning, and Reinforcement Learning. The popular supervised machine learning algorithms for beginners include Decision Trees, Support Vector Machines, K-nearest neighbour, Naïve Bayes, Linear regression, among others.

Unsupervised Learning

In unsupervised learning, the Machine Learning Algorithm does not receive any labels, and the machine is only given an input set. The algorithms are trained using unlabelled datasets and they must rely on themselves to find structure in the input without any supervision.

Unsupervised learning is further classified into two types — Clustering and Association. K-means clustering, Hierarchical clustering, Anomaly Detection, Apriori algorithm are some of the popular Unsupervised Machine Learning Algorithms.

Applications of Machine Learning with Python

There are several applications of Machine Learning with Python. We list some of them.

Refining search results

Machine learning with python can also help better your experience with search engines. It learns from the time you spend on a result to understand its relevance to the search query. For example, if after searching for something, you click on a result and stay there for a period, the search engine gauges its usefulness and learns from it to better its results for future searches.

Filtering out malware

Spammers are always innovating. Hence, spam and phishing emails can go unnoticed with the traditional method of filtering. Machine learning ensures that the spam filters keep on updating. In this email, clients make use of Deep Learning, which assimilates data from7 the users and processors to determine the nature of emails. A couple of the most used techniques are C 4.5 Decision Tree Induction and Multi-Layer Perceptron.

Marketing

Machine learning plays an important role in marketing. Shopping giants like Amazon use these algorithms to suggest you products based on your past purchases and search history.

Virtual Assistants

Virtual assistants like Alexa, Siri, Cortana help you with your everyday tasks like calling someone, setting reminders, and searching for things on the internet. They make your work easier, especially when your hands are full and you cannot physically type or do tasks. These virtual assistants are constantly collecting information about the interactions and learning from them.

Customer service

The online customer support has undergone a complete overhaul. You are usually greeted by a virtual chatbot when you click on help. This chatbot trains itself and is able to help with your queries by using machine learning algorithms.

Protection from fraud

There are millions of transactions taking place every day online- from payments for goods and services to bank transfers. Monitoring every one of them is almost impossible. Hence, companies make use of Machine learning to distinguish illegitimate transactions from legitimate ones.

Translation and text identification

Machine Learning learns how words in languages fit together and can be used to translate in real-time. Also, it can read the text in an image using neural networks.

Surveillance

Surveillance requires a large and dedicated task force. Cameras equipped with machine learning can help identify suspicious behaviour and help prevent any untoward incident. This can help save time, money, and even lives.

The contribution of Python in machine learning is quite significant. Hence, it is an excellent and in-demand field to make your career in. To train you in the basics of this programming language, NIIT offers you a ‘Basic Python Programming and Data Exploration in Python’ course. It trains you on the basic concepts of the language so that you are prepared to learn the advanced techniques and are skilled for a career as a Python developer.



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