In the world of software and IT, two buzzwords (four actually) you will hear a lot today are Artificial Intelligence (AI) and Machine Learning (ML). As areas of Computer Science, both AI and ML have been around for quite a while – nearly since the 1950s. But they have remained theoretical concepts by and large, due to computing resources being largely prohibitive – both AI and ML require significant computing power and memory to produce optimum results. The drop in computing costs over the last several years has seen the focus in Computer Science and programming coming back to practical AI and ML applications.
What are AI and ML? And why should you bother?
AI and ML are exciting areas in terms of computing. Here’s why. Computers are largely dumb devices – they only carry out the tasks that humans tell them to. Software (code) is what humans (programmers) use to tell computers what to do. Programmers write code and computers execute that code to “do something”. This is how it has been up until now. Human intelligence, then, is what has been driving computers so far. Artificial Intelligence is intelligence attributed to computers – i.e. intelligence, which helps computers make their own decisions. Machine Learning is closely tied to AI, because the computer, to be able to do AI needs to “learn” from large sets of data that programmers provide to it as fodder.
As you can imagine, the earlier notion of programming is undergoing a drastic change in the world of software. There will be a day in the future when computers will not really need the human programmers of today. Computers will simply be able to write their own programs. In the interim though, the race is on to get computers to learn newer and newer data sets. This is why “data” and Machine Learning are getting all the importance.
If you are a grad student or a young programmer, then at the beginning of your software career, would you not want to be where the jobs are? Machine Learning is clearly that field. And because Python is so good at dealing with large complex data sets and mathematical applications, it is the language everyone wants to learn. A good place to start is picking a Machine Learning Using Python course from amongst the many available courses online.
It was as if Python was made for machine learning. Python is one of the most popular programming languages today, having displaced many of the traditional machine learning languages. This is because Python contains a whole set of libraries and modules which help machine learning tasks. Also, Python is extremely efficient at mathematical processing.
In this article, we will briefly explore the different Python libraries used in machine learning. To delve deeper, you should enrol in a Machine Language Using Python training.
This library helps in processing of large multi-dimensional arrays and matrices. It also has a big collection of high-level mathematical and scientific functions. The library is useful for Fourier transforms, linear algebra and random number capabilities.
The Scipy library contains modules for optimization, linear algebra, integration and, statistics. It is also used in image manipulation applications. This is a popular library amongst Python enthusiasts.
This is a very popular library for those starting off in Python. Scikit-learn combines the Numpy and Scipy libraries and supports the supervised and unsupervised learning algorithms. It is also used in data mining and analysis.
This library is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays efficiently. It is a powerful library ideally used in large-scale computationally intensive projects. But at the same time, it is friendly for smaller applications too. Its use in computationally extensive applications is achieved via optimum utilization of the CPU and the GPU. The Theano library is also used heavily in unit-testing and errors identification.
This is an open-source library that was developed by Google. It is popular for high-performance mathematical computations. TensorFlow is used in computations involving tensors, which are mathematical objects in space. This library is popular in neural networking to train and run neural nets used in deep learning applications.
Keras is a high-level neural networks API which can run on top of TensorFlow or Theano. It can be a quick prototyping tool, especially useful for ML beginners wanting to design and build a neural network.
PyTorch is based on Torch, an open-source ML library built in C. Its many tools support Computer Vision, Natural Language Processing and other ML programs. Programmers using PyTorch can perform tensor computations and create computational graphs.
This is a popular data analysis library. Pandas was developed for data extraction and provides high-level data structures for data analysis. In that sense, it is not directly related to ML.
Mat-plot-lib, as the name suggests, is used for data visualization. Programmers use it to visualize data patterns, as it provides different kinds of plots like bar charts, histograms, etc. Again, being a data visualization tool, it is not directly related to ML.
As we have shown you, Python supports machine learning through a host of libraries and modules. Because of the popularity of Machine Learning in the job market, Python too is basking in the limelight. If you are thinking of programming as a career, learning Python would not be a bad choice!