Machine Learning: Why Use Python?
Python has become the de-facto programming language for machine learning and deep learning applications. But what exactly is machine learning, and why is Python so crucial for it?
This article is intended for anyone interested in learning about machine learning and deep learning, particularly those working in computer science or data science fields. As these technologies become increasingly integrated into our daily lives, understanding the basics of machine learning is becoming more and more important.
After reading this article, you will have a better understanding of what machine learning is, how it differs from deep learning, and why you should enroll in a Python course to make the most out of it. You will also have a basic understanding of the tools used in machine learning tasks and who should use Python.
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Table of Contents
- Why is Python Used for Machine Learning, not Java?
- Why is Python the Number One Programming Language?
- What Tools Are Used With Python in Machine Learning?
- What is Machine Learning?
- Machine Learning vs. Deep Learning: What are the Differences?
- Who should use Python?
- Why is Python important?
- Things to Note About Python
Why is Python Used for Machine Learning, not Java?
Python is used for machine learning more than Java because Python is an easy-to-learn and flexible language that has simple syntax, platform-agnosticity, and an abundance of libraries and frameworks. These characteristics make it a popular choice for machine learning engineers, particularly due to the prevalence of machine learning-related Python libraries.
In contrast, while Java is a popular programming language, it is not as popular as Python for machine learning, as it has a steeper learning curve, is more verbose, and has fewer machine learning-specific libraries and frameworks.
Why is Python the Number One Programming Language?
Python is flexible, simple, and consistent. It has a relatively easy-to-learn syntax and allows programmers access to many modules and libraries. Because of its ubiquity, it is also easy to find support and help with Python questions.
Because it is such a simple language to learn, Python is also a great first language for beginning programmers and those just getting started with coding.
What Tools Are Used With Python in Machine Learning?
Some of the most popular tools for completing machine learning tasks with Python include Tensorflow, Cy-Kit Learn, Pytorch, OpenCV, Theano, and ML Pack. Each library helps achieve a specific machine learning-related task. For example, OpenCV is an image-manipulating library used for image recognition.
What is Machine Learning?
Machine Learning is a branch of artificial intelligence that teaches computers how to recognize patterns and use those patterns to make decisions about new information. In machine learning, a “neural net”, or complex network, is fed massive amounts of data and taught to recognize patterns in the datasets.
Once the machine recognizes patterns, you can feed it new data. Then, the machine can analyze the data and provide insights about it. For example, you can train a computer to detect faces in new images or do facial recognition for authentication by feeding it image data containing faces.
Machine Learning vs. Deep Learning: What are the Differences?
Machine Learning and Deep Learning are both branches of AI that deal with training neural networks and pattern recognition. However, the primary difference between machine learning and deep learning is that machine learning requires human input, while deep learning does not.
A deep learning application can tell the computer how to categorize data without input from a human. On the other hand, a machine-learning application needs human input to learn the categories before it can start organizing data.
Because of this, deep learning is a more resource-intense process and requires more energy and CPU. As a result, deep learning has a higher up-front cost than machine learning but reaps greater rewards later with time and labor saved.
Who Should Use Python?
Python is a powerful programming language for many applications, not just machine learning. For example, frameworks like Django and Flask make Python an excellent choice for backend web developers, while libraries like OpenCV and PyTorch make it popular among machine learning engineers.
Anyone serious about understanding machine learning, deep learning, or other AI applications should know Python. Anyone working on a website and mobile application backends would do well to learn it, too.
Because it is such a simple language to learn, Python is also a great first language for beginning programmers and those just getting started with coding. The concepts you encounter learning to code with Python opens the gate for more complex concepts you encounter later.
Why is Python Important?
People interested in machine learning have no choice but to learn Python, as many libraries that enable machine-learning tasks were written for Python. Someone learning to code would be making a wise decision if they chose Python as their first language.
These days, machine learning and deep learning are not just fringe phenomena. Instead, they are fully integrated into our daily lives through things like facial and biometric recognition technology, self-driving cars, and smart devices.
Things to Note About Python and Machine Learning
While Python on its own is easy to learn, machine learning is very complex and difficult to understand. Therefore, it is important to realize that simply learning the Python syntax is not enough to be proficient at machine learning tasks.
To fully understand how to train a neural network or do other deep learning or machine learning things, you must study computer science basics and take courses in artificial intelligence and machine learning.
Python is the de-facto language for machine learning and deep learning applications. Python is simple, concise, platform-agnostic, and has a plethora of libraries available to facilitate machine learning tasks. In addition, many developers already use Python for machine learning, so support is not hard to find.
Anyone interested in getting into this branch of computer science should learn Python, however, learning Python on its own is not enough to become a machine learning engineer.
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