17 Top Applications of Machine Learning with Python

Why Python for Machine Learning Used?

Before we proceed to the applications of Machine Learning with Python, you’re probably asking yourself- why Python? Among tools like R programming and SAS, here’s why we’ll go with Python-

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a. Simple

The sole reason why Python is often chosen as an introductory language to programming is its simplicity. It is simple, yet powerful. Python is easy to write, and simple to understand. This behaviour of its makes it intuitive. Situations like getting your code from another developer that uses third-party components mean you need very little cognitive overhead. It is also true that code is read more often than it is written. Therefore, simplicity serves to be a great asset to Python.

b. Huge Set of Relevant Libraries

Python has a wide collection of libraries for machine learning purposes. These include Python NumPy, SciPy, scikit-learn, and many more. These are good with all intrinsic tasks of machine learning.

• scikit-learn- Good for data mining, data analysis, and machine learning.
• pylearn2- More flexible than scikit-learn.
• PyBrain- Modular ML library with flexible, easy, and powerful ML algorithms and predefined environments to test and compare algorithms.
• Orange- Open-source data visualization and analysis, has components for machine learning, has extensions for biometrics and text mining, has features for data analytics, supports data mining through visual programming or Python scripting.
• PyML- The Interactive object-oriented framework for machine learning, written in Python.
• Milk- Machine learning toolkit, has SVMs, k-NN, random forests, decision trees, performs feature selection.
• Shogun- Machine learning toolbox, focuses on large-scale kernel methods and SVMs.
• Tensorflow- High-level Neural Network Library.

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Applications of Machine Learning with Python

a. Virtual Personal Assistants

Names like Siri and Alexa bring to mind the capabilities of virtual assistants. We can ask Siri to make a call for you or play music. You can request Alexa for today’s weather forecast. You can even set an alarm or send an SMS. What makes this easier on you is that you only need to speak to it and it will listen to your command. This comes in handy for those differently abled. Such assistants take note of how you interact with them and use that to make your next experience with them better.

b. Social Media Services

By now, you would have noticed several features of Facebook- ‘People You May Know’ and ‘Face Recognition’. It uses machine learning to monitor your activity- what profiles you visit, which people to send requests to, which ones you accept requests of, the people you tag, among much more. With this, Facebook hopes to provide you with a richer experience on its platform so you will use it regularly.

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c. Online Customer Support

Websites like educators and shopping platforms will often pop a live chat up to help you with your questions. A visitor with a head full of questions is more likely to leave than stay and possibly make a purchase. Some websites use a chatbot instead to pull information to the website and try to address the customer’s queries.

d. Online Fraud Detection

If you’re familiar with PayPal, you realize your trust with it. It uses machine learning to stand in defence against illegal acts like money laundering. By comparing millions of transactions, it can find out which ones are illegitimate.

e. Product Recommendations

Shopping platforms like Amazon and Jabong notice what products you look at and suggest similar products to you. If this gets a favourite product across to you and results in a purchase you make with them, it’s a win for them. For this, it also uses your wishlist and cart contents.

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f. Refining Search Engine Results

The searches you make in search engines like Google monitor your response. Do you visit a top listing and stick around for a while? Do you get to the third page and leave without clicking any link? Google makes note of the findings and aims to improve your search next time.

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