Python is the right choice for anyone who is excited about and wants to get into machine learning. Achieving results using machine learning techniques doesn’t take years of study or advanced mathematical knowledge---just persistence and common sense.
In this article, we’ll dive into the basics of machine learning and how learning Python can set you apart from the competition.
What is Machine Learning?
Machine learning is the process of training a computer program to perform tasks without giving it explicit instructions. Instead, through repeated processing of data, the computer program identifies patterns and is then able to predict outputs based on new inputs. Below, we’ll briefly review both deep learning and classical algorithms and discuss which methods are better suited for which problems.
Machine learning will soon transform every industry and now is the time to learn the skills employers will be looking for tomorrow. The key thing about machine learning techniques is that they are not one-size-fits-all; a system for providing product recommendations is different from a system that recognizes faces.
Why is machine learning important?
With machine learning, we can now tackle problems that we couldn’t have dreamed of solving previously. Regardless of which industry you work in, you can take advantage of modern machine learning tools to improve processes, enhance productivity, or even better human lives! Here are a few examples of how machine learning is being used in different industries:
Agriculture: Penn State University is helping farmers detect cassava diseases: farmers can use a mobile app that employs a phone camera to capture photos of plants and determine whether or not they have diseases–and how to manage that disease.
Medicine: Mount Sinai is using machine learning to help diagnose breast cancer. Doctors can now get a second opinion on a mammogram instantly by leveraging the power of machine learning in order to better diagnose breast cancer.
Journalism: Fake News Challenge is applying machine learning to assess truthfulness of articles. Readers shouldn’t have to depend on fact-checkers having the bandwidth to review that exact article they’re reading; instead, they should be able to use a free machine learning tool that can help them determine whether what they’re reading is real or a hoax.
Aviation: General Electric is leveraging machine learning to analyze aircraft engines and improve safety. By taking data directly from the engine, deep learning algorithms can detect whether maintenance is needed much more accurately than ever before.
What is Python?
In technical terms, Python is a high-level, dynamically typed, and interpreted programming language that supports structured, object-oriented, and functional programming paradigms. In short, it’s a language that’s easy to learn and flexible to use. For years, Python has been one of the most popular programming languages to use, especially for those first learning how to program. Now, with the popularity of machine learning, Python is more widely used than ever.
You cannot go wrong with learning Python. Python offers a ton of features that appeal to a wide-range of people:
If you’re just learning how to program, the simplicity of the Python interpreter makes it super easy to get started.
If you’re coming from an object-oriented language like Java, Python fully supports that programming paradigm and will feel super familiar.
If you’re more comfortable with C or C++, Python also supports structured programming–and the built-in garbage collection will make your development experience much safer and much faster!
Machine Learning & Python
Why Python?
Python is easily the most preferred language for machine learning. However, there is nothing particularly special about Python that makes it better suited for machine learning than any other modern language–what’s different about Python is how easy it is to learn.
Because of this, academics and data scientists embraced Python as their language of choice years ago, and this powerful and knowledgeable community created tons of free, open-source tools that anyone else can pick-up and use. Tools such as TensorFlow, PyTorch, and Scikit-Learn can enable anyone, even if they have little-to-no development experience, to utilize the power of machine learning.
Why do we need machine learning?
With traditional programming techniques, a developer uses a programming language to write rules that take in data and produce a result. However, there are problems for which there are not well-defined rules–these problems are not suitable for traditional programming techniques. Luckily, we now have machine learning tools that can be utilized to handle exactly those scenarios! Here’s one:
Imagine we wanted to create a program that could read text and determine the genre of the input. A naive approach could be to calculate the number of words in the text and, using that value, deduce the genre. Text with less than 100 words would result in an output of “poetry.” Text that’s between 100 words and 1,000 words would result in “essay.” Anything longer, our program would deduce to be a novel.
Obviously, this program is not very accurate–but that’s because there are not well-defined rules for how to categorize literature. Instead, academics can and do argue about whether a particular work belongs to genre A or genre B, or whether it’s a completely original work that doesn’t fit well in any particular known genre. But, where traditional programming techniques fall short, machine learning excels and by training a model on a wide range of inputs, we can create a program that can accurately determine the genre of a work with ease.
How can I get started with machine learning with Python?
To get started with machine learning using Python, one good choice is the TensorFlow library. While industry giants might use hundreds or even thousands of dedicated tensor processing units (TPUs) to train models like GPT-4, it’s possible to get started using nothing but your current computer. Once Python and it’s package manager, Pip, are installed on your computer, all you have to do is install the CPU version of TensorFlow to your computer by running the following command:
pip install tensorflow-cpu
Next, you can use TensorFlow in your Python code simply by importing the dependency:
import tensorflow as tf
Now, you should be able to use the high-level Keras API to begin training the data required to solve any problem you can think of!
Which job opportunities use machine learning?
With machine learning in your toolbelt, you’ll have many more job opportunities available to you than ever before. In the current environment, companies are trying to use machine learning techniques wherever and however they can. But, that doesn’t necessarily mean that there are going to be millions of openings for machine learning engineers.
In fact, many companies may not be hiring anyone with the title “machine learning engineer” at all, but instead may be hiring software engineers broadly and giving preference to those who have shown they are capable of enhancing current processes by employing modern machine learning techniques. With that said, look out for job postings for the following roles:
Machine Learning Engineer: In this role, an employee builds and manages platforms for machine learning projects. They might not be training the models themselves, but will be highly involved in keeping the process as efficient as possible.
Data Scientist: A modern data scientist is in charge of collecting, analyzing, and interpreting data–this more often than not will involve using a machine learning model.
Software Engineer: This employee is responsible for the entire software development process. Look for roles who have “AI,” “Machine Learning,” or “Deep Learning” in the description.
