Python and Machine Learning

Python is well on its way to becoming the most popular programming languages in the world. It is favored for its applications ranging from web development to scripting and process automation. Additionally, Python is becoming the chosen language among developers for artificial intelligence (AI), machine learning and deep learning projects.

Let’s start with the differences of AI, machine learning and deep learning

In short, deep learning is a subset of machine learning and AI is the main category that houses machine learning.

AI is exhibited as any intelligence shown by a machine that leads to a best-case solution given to a problem. Machine learning now takes this a step further by using algorithms to parse data and learn from it to make better informed decisions.

Deep learning though does basically the same but with very different capabilities. It has the capability to draw conclusions resembling human decision making. It does this via a layered structure of algorithms inspired by our own human brains neural network. This in affect, teaches deep learning multiple levels of response to different levels of input.

So why Python for AI

Vast collection of libraries and frameworks

One of the biggest reasons Python is a great choice is in its abundance of libraries and frameworks that facilitate code and saves on time.

Scientific computation (NumPy), advanced computation (SciPy) and data mining and analysis (scikit-learn) are among the most popular libraries compatible with strong frameworks like TensorFlow, CNTK, Apache Spark. These frameworks are essentially Python-first with the exception of PyTorch, which was written specifically for Python.

Simplicity in its complexity

Python is renowned for its concise readable code and is almost without rival when it comes to its ease of use and simplicity, especially for new developers. This is advantageous for machine learning.

Both Machine learning and Deep Learning rely on complex algorithms and multi-stage workflows. The less a developer worries about the details of coding the more they can focus on finding solutions to the problems and closing out a project quicker, meeting all goals.

A simpler syntax is also Pythons advantage in that it is also faster in development compared to other languages and allows the developer to test algorithms quicker without having to implement them. 

Examples of projects

The list is growing each year. It is safe to say we are on the cusp of a change in the way we see or interpret our interaction with computers and robots in the future.

For better or worse, the following are only a few of the more media savvy projects on the go, though to be honest, it’s the ‘non-exciting’ projects that will develop the industry in the end!

·        Text-to-text synthesis – Generating images from text

·        Generating human faces – Dreaming up new celebrities or ‘people’

·        Image-to-image translation – Turning zebras into horses in one picture or winter into summer in another.

·        Human Activity Recognition – using smartphones sensors to classify human activity.

·        3D facial recognition – detecting facial features from 2d and 3d images

·        Emotion and gender classification – Face detection and gender detection classificatio 

Summary

For a myriad of benefits mentioned earlier, Python is the most popular of choices made for machine learning projects. Its extensive selection of libraries and frameworks simplify the development process significantly.