Machine learning is currently at the forefront of the next technological revolution thanks to its disruptive nature. If you want to become a machine learning engineer, you need to learn Python, one of the most popular general-purpose programming languages used by experts today. There are numerous learning opportunities available for aspiring machine learning engineers.
In this article, you are going to learn about the role of Python in machine learning, the timeline required to learn Python, and the importance of learning Python. Most importantly, you are going to be introduced to several education options available today, as well as tips for choosing the right option for you.
What Is Python?
Python is a general-purpose, object oriented programming language that has a range of applications in the technology industry. It is a high-level programming language that emphasizes code readability during small and large-scale projects. The interpreted programming language has a built-in data structure that enables rapid application development.
Since it was initially released by the Python Software Foundation in 1991, Python has been used for a wide range of web development, data science, and machine learning projects. Its versatility and simplicity make it ideal for new programmers who still haven’t chosen a specialization. Mathematicians and researchers may also benefit from learning Python.
What Is Python Used for in Machine Learning?
Python is used for writing algorithms and data validation during machine learning projects. Since it is object oriented, it allows machine learning engineers or data scientists to add several features to a program without having to rewrite the base code from scratch.
Python has gained popularity as the ideal programming language for machine learning because it provides access to four different libraries. These are statsmodels, Matplotlib, Keras, and Pandas. Using these diverse and versatile libraries, a machine learning engineer can perform several studies simultaneously without disrupting the flow of the project.
How Long Will It Take to Learn Python for Machine Learning?
You can learn Python for machine learning in two months or less. Python has a low barrier to entry because it is similar to the English language. Someone with prior knowledge of programming styles can learn Python in a matter of weeks rather than months.
Apart from experience, the route you take to master Python for machine learning is also going to affect your timeline. For example, an online Python course with a bootcamp like Flatiron School, or an online course provider like Udemy, might last for less than three months. A college degree could take two to four years to complete, depending on the type of degree.
Why Should You Learn Python for Machine Learning?
You should learn Python for machine learning because it has many libraries, it is easy to learn, incredibly versatile, and flexible. Python is also more scalable than R and other similar programming languages used by data scientists and machine learning experts.
With Python, you will get access to statsmodels, Matplotlib, Keras, and Pandas. These popular libraries all have useful applications in machine learning and they aren’t the only Python libraries you can use. Statsmodels is one of the best libraries for data exploration and statistical algorithms.
Pandas is the ultimate data manipulation tool for high-level data analysis and data structures. Matplotlib is a library used for interactive data visualization in Python. During machine learning projects, you can use the 2D plotting library to create histograms, 2D plots, and charts like decision trees. Keras is a high-level neural network library used for deep learning functionalities.
Easy to Learn
Machine learning is a complex field so you need to do what you can to simplify the learning process. It’s easy to learn Python regardless of your educational background. You don’t need to be a mathematician or statistician to explore machine learning as long as you know Python.
It is also important to note that you do not need to learn advanced Python to be able to apply it to machine learning. All you need is a basic understanding of the programming language that allows you to write straightforward codes.
Python is currently one of the most versatile programming languages in the world. You can choose to use Python on a variety of operating systems with ease. It runs on macOS, Windows, Unix, Linux, and operating systems similar to Linux.
Because of its cross-platform versatility, software testing during machine learning is easy with Python. All you have to do to test a piece of code is install the PyInstaller to bundle your Python applications into one package and start testing.
Compared to Java and C++, Python is flexible. It is a general-purpose programming language that is not limited to a specific template or API. Apart from machine learning, Python can also be used for web development, task automation, and even mobile application development.
Python can be used along with other programming languages to save costs, speed up the project process, and minimize the risk of bugs. You don’t need to recompile the Python source code to make the changes you need to use another programming language along with Python.
While Python may not be as scalable as Java, it is more scalable than the R programming language, another common option for machine learning. Some of the factors that make Python scalable are dynamic binding, built-in data structures, rapid development, and dynamic typing.
When used as a glued scripting programming language, Python allows engineers to add new features and improve and expand applications without compromising on functionality. Since Python is open source, you can get the information you need to improve scalability for free.
How Can I Learn Python for Machine Learning?
You can learn Python for machine learning at a coding bootcamp, through books, or by enrolling in massive online open courses (MOOCs). Python is easy to learn so it doesn’t matter if you take self-paced or instructor-led lessons.
Coding bootcamps, specifically data science bootcamps, are among the most popular places to learn Python. The best Python bootcamps don’t just teach you how to use the programming language, but how to apply it to various fields of technology. Artificial intelligence courses are also ideal for machine learning.
Bootcamps provide structure, accountability, and career services. This means your chances of getting a job after graduating from a bootcamp will be higher compared to someone self-taught. Some great bootcamps you should consider for learning Python are Thinkful, BrainStation, Coding Dojo, and General Assembly.
MOOCs are viable alternatives to machine learning bootcamps and other forms of formal education. Platforms like Udemy, Coursera, Udacity, and edX have a host of online Python courses for aspiring machine learning experts. These courses are short, straightforward, affordable, and available in different difficulty levels.
Some great courses for learning Python for machine learning are Intro to Machine Learning with TensorFlow from Udacity, Machine Learning with Python from Coursera, and Machine Learning, Data Science, and Deep Learning with Python from Udemy.
If you prefer unsupervised learning, you may benefit from exploring this option. There are hundreds of online books that cover key concepts of Python for machine learning and artificial intelligence principles.
Some great books are Python Machine Learning by Vahid Mirjalili and Sebastian Raschka, and Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido.
Top Python for Machine Learning Libraries
Python libraries are collections or categories of functions used during programming to minimize or eliminate the need for rewriting codes. Using these libraries reduces the time required to code. There are libraries for data visualization, data manipulation, data analytics, and machine learning. Below is a list of the top five Python libraries for machine learning.
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Did you know that there are thousands of Python libraries in the world today? These libraries all have unique features, as well as pros and cons, and not all of them are ideal for machine learning. Even if you find one that is useful for machine learning, you still need to make sure that it is efficient.
How to Learn Python for Machine Learning: A Step-By-Step Guide
Learning Python for machine learning might seem like a difficult process on the surface but this doesn’t mean it is impossible. Whether you have experience in programming or not, you can learn machine learning. If you follow the steps below, you can learn Python as part of your journey to becoming a machine learning engineer.
Start with Basic Syntax
This is the first and most important step in learning Python for machine learning and data science. In Python, syntax refers to the set of rules and regulations that determine how programs will be written and interpreted. It is the syntax that defines the structure of the programming language.
The syntax is important because if it is not correct, the code will not be compiled. While learning syntax can be overwhelming, several resources can help you get a headstart. The Basic Python Syntax introduction is a crash course by Coursera and one of the best courses to learn about Python syntax. There are also many video and written tutorials for understanding Python syntax.
Learn to Write Python Code
After you’ve learned the basic syntax and semantics in Python, you need to learn how to code in Python. If you already have programming experience, this stage will be easy. However, even without any programming experience, Python’s similarities with the English language make writing easy.
As you learn to code, make sure you keep adequate notes of everything you learn on the go. Note-taking skills are just as important to aspiring Python programmers as writing the codes itself. Taking notes by hand is the preferred option by most programmers but typing works too.
Knowledge of Machine Learning
After you have gotten a good grasp of Python, you need to understand the basics of machine learning. Machine learning is a branch of artificial intelligence aimed at making accurate predictive models without explicitly programming. It relies on things like linear regression, multivariable linear regression, logistic regression, and scientific computing.
Read what you can about machine learning then find out the role Python plays in the field of machine learning. Some courses teach you about Python and machine learning simultaneously. These are the best courses for anyone who wants to use the powerful machine learning tool.
Learn by Doing
The best way to master Python or any other programming language is through practical applications and hands-on projects. Write machine learning algorithms on your own and add them to your portfolio. You can join Python communities and seek out peer reviews to help you improve your work.
Start with simple real-life examples like creating smart emojis and animating images, to something more complex like MNIST digit classification and stock price prediction models. There are many different aspects of machine learning that you can explore.
Start Getting Paid
If you are confident in your skills, consider making premium products or hunting for paid gigs. There is no better motivation to learn and improve than payment from a client. You can start with freelance Python development and work your way up to permanent positions. In any case, there are many tangible career benefits of learning Python for an array of machine learning projects.
Some of the top employers of Python developers in the machine learning industry are MobiDev, Indium Softwares, and InData Labs. Spotify, JP Morgan Chase, Pixar, and Netflix are also among the popular companies that use Python.
Start Learning Python for Machine Learning Today
Machine learning engineers earn an average salary of $157,676 a year according to ZipRecruiter. Since Python is one of the most useful tools for advanced machine learning experts, learning this general-purpose programming language will pave the way for a lucrative career in the technology industry. All you have to do is pick the right online course, learn Python for machine learning, and start creating projects.
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