As someone interested in technology, as I have been when starting in this world, getting familiar with some of the most popular machine learning tools used by professionals can expand your understanding of this exciting field.
In this post, I am breaking down 10 versatile machine learning libraries and platforms worth knowing as a beginner. I’ll explain how each tool works in simple terms along with real-world examples, so you can grasp the basics.
Let’s get started!
First up is TensorFlow — an open-source library for building machine learning models, especially neural networks for deep learning.
Deep neural networks mimic the workings of the human brain. They take input, process information through layers of “neurons”, and produce an output like a classification label or numeric prediction.
For example, you can build an image classifier that recognizes everyday objects like cats, dogs, cars etc. accurately by training a deep neural network model on thousands of images with TensorFlow.
Instead of coding a neural network from scratch using linear algebra and calculus, TensorFlow provides pre-made modules and functions, so you can set up, train and run these models in a more straightforward way.
TensorFlow simplified these techniques for a wider audience beyond academics, making deep learning more accessible to developers.
Engineers and data scientists at companies like Google, Airbnb, Uber, Twitter, etc. use TensorFlow to incorporate AI into actual products. These include recommendation systems and self-driving car technology.
Becoming proficient in TensorFlow is important for those who aim to work in machine learning engineering positions at major tech companies. It’s arguably the most widely adopted deep learning framework currently with tons of tutorials and guides online to level up.
While TensorFlow makes deep neural networks more approachable, Scikit-Learn focuses on providing simple yet efficient tools for “traditional…
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