Python is used as the preferred language for teaching and learning ML (Machine Learning). It can be claimed that artificial intelligence, machine learning, and deep learning were tied to the Python programming language. Part of the progress and expansion in these fields should be attributed to the features and capabilities of Python. Machine learning with the Python programming language is a good starting point for ML.

You can use it to apply existing models such as linear regression, logistic regression, and SVM. If you are more serious about machine learning, you should also read and learn some mathematics books. In this article, we will learn about 12 amazing applications of machine learning with Python.

Of course, before introducing those 12 amazing applications, the question must be answered, what are the advantages of the Python language that make it the best choice for machine learning?

1. Simplicity of programming in Python

The only reason to choose Python as an introductory programming language is its simplicity. Python is simple but powerful. Python is a high-level language. High-level languages are closer to human language. Coding in this language is very easy because the writing rules of the Python programming language are similar to English. Therefore, the developer can program with fewer lines of code. Also, Python has an interpreted system, that is, the codes are executed as soon as they are written. Therefore, simplicity is a big advantage for Python.

2. Huge collection of related libraries in Python

Python has an extensive set of libraries for machine-learning purposes. These include Python NumPy, SciPy, scikit-learn, and more. These libraries are used for all the intrinsic tasks of machine learning.

Scikit-learn is suitable for data mining, data analysis, and machine learning.

pylearn – more flexible than scikit-learn.

PyBrain modular library with flexible, easy, and powerful machine learning algorithms and predefined environments suitable for testing and comparing algorithms.

orange – Open source data visualization and analysis aid, has components for machine learning, has extensions for biometric sensing and text mining, and supports data mining via visual programming or Python programming.

PyML – an interactive object-oriented framework for machine learning written in Python.

Milk_ has SVM, k-NN, random forests, and decision trees, which perform feature selection.

Shogun – Machine learning tool, focused on large-scale kernel methods and SVMs.

Tensorflow is a high-level neural network library.

Machine learning applications with Python

1. Predict music options

Systems like Genius by Apple Music monitor what you’re listening to. Those systems can then suggest a list of songs you’re likely to prefer. It also selects songs from your playlist to create similar libraries.


2. Drug discovery and disease diagnosis with machine learning algorithms

We can do the following with the help of machine learning in this field:

  • Machine learning applications with Python;
  • initial screening of medicinal compounds;
  • predicting the success rate based on biological factors;
  • Research and development technologies in related fields;
  • understand disease processes;
  • Design effective treatments for diseases;
  • personalization of medicinal compounds;
  • produce cheaper drugs with improved replication;
  • Research and development of diagnostic and treatment methods.

3. Face Recognition

Features like face recognition with the help of machine learning are often what we see with Facebook. When we want to tag a photo, Facebook automatically suggests some names. And most of the time, the name he suggests for the face he’s discovered is accurate with the help of machine learning.

4. Virtual personal assistants

Names like Siri and Alexa recall the capabilities of virtual assistants. We can ask Siri to call us or play music. You can ask Alexa for today’s weather forecast. You can even set phone alarms or send SMS. You just have to talk to it and it will listen to your command. These assistants pay attention to how you interact with them and use them to improve your next experience.

5. Social media services

This app uses machine learning to monitor your activity. This app remembers everything you do on Facebook (whether it’s the profiles you visit, the people you send friend requests to, or the people you accept, as well as the people you add to your close friend list). Facebook hopes to give you a richer experience on its platform. So you will regularly use machine learning with Python in it.

6. Self-driving cars

Self-driving cars receive data about surrounding objects and their size and speed through sensors. And based on how they behave, they classify objects as cyclists, pedestrians, and other cars. They then use this data to compare the saved maps with current conditions. Such cars use Machine Vision algorithms.

7. Online customer support

Educational websites and shopping platforms often create a live chat to help their customers with questions about their sites and platforms. A visitor with a lot of unanswered questions will probably give up buying and leaving the site. For this reason, some websites use a chatbot that can answer customer questions and keep them on the site.

8. Surveillance

Some crimes can be prevented by monitoring and identifying people’s behavior before they occur. Machine learning with Python can understand behaviors such as standing still, dozing on the couch, and chasing another person, and can warn humans through the video surveillance system.

9. Product recommendations

Shopping platforms like Amazon understand what products you are viewing and suggest similar products to you. If this product is your favorite and you like it and buy it, it’s nothing but success for them. These operating systems use your favorite list, shopping cart, and observations to recognize products with the help of machine learning.

10. Insurance pricing plans

Machine learning can check the history of someone who wants to use insurance services and predict according to that history and determine how likely it is that the taxi driver who is going to be insured will cause a lot of damage during the insurance period. This allows insurance companies to adjust the price of their insurance plans for each person.

11. Automatic translation

Machine learning with Python allows us to translate text into another language. For this, the machine learning algorithm uses the shape of how the words are placed together and then uses this information to improve the quality of the translation. With this, we can also translate text on images using neural networks and character recognition.

12. Detecting online scams

If you are familiar with PayPal, PayPal uses machine learning to defend against illegal activities such as money laundering. By comparing millions of transactions, it is possible to understand which of them is illegal.

More applications of machine learning with Python

Apart from the things we mentioned, Machine Learning can also be used for the following purposes:

  • Identifying human genes that are prone to cancer;
  • identifying products that consumers react to;
  • Algorithms can be built to predict stock transactions and stock market index fluctuations;
  • Delays in airplane flights can be predicted;
  • It helps to maintain factory equipment and machines, and also predicts and announces the time of their repairs;
  • It helps to create personalized ads for each user based on their online behaviors and interactions;
  • Site SEO becomes easier and faster with Python and machine learning.

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