applied machine learning in python

All You Need to Know About Applied Machine Learning with Python

When was the last time your computer or an app offered suggestions for you based on something you might like or enjoy? That is one of the limitless benefits of applied machine learning. 

The world is advancing fast, and you do not want to be left behind. Machine learning and artificial intelligence-based projects are what the future holds. This article provides insight into the exciting course of applied machine learning with Python programming language. 

What is Applied Machine Learning (AML)?

applied machine learning in Python

The term “applied machine learning” refers to the use of machine learning (ML) to solve various data-related problems. 

It is different from theoretical machine learning – which is about understanding underlying algorithms, statistics, and a handful of other data-related subjects at the most fundamental level. Applied machine learning is not just about understanding these concepts, but understanding them enough to solve real-world problems.

It is a subset of artificial intelligence that automatically enables computer systems to learn and predict based on input data. The aim is for systems to learn automatically without human intervention and act accordingly.

History of Applied Machine Learning

Machine learning is based on a model of brain cell interaction. Donald Hebb developed this model in 1949 in his book titled the Organization of Behavior. The book discusses Hebb’s theories on neuron excitement and communication.

The first AML program was written by Arthur Samuel in 1952. The program’s goal appeared to be straightforward: play a game of checkers. But it was actually very complex because AML was in action. The program was taught to correct its mistakes and improve its checker skills.

Sure, the early days of machine learning did not solve real-world problems, but they did lay the groundwork for what is now applied machine learning. Every day, we see applications of machine learning.

Applied Machine Learning Examples 

AML has enabled our apps to see, hear, and respond, therefore improving user experience and adding value across many industries. It has also enabled more personalized recommendations and improved search functionality. Here are some groundbreaking examples of the unique power of applied machine learning:

Speech Recognition

man using SIRI

When you ask Siri to perform an action with your iPhone (like opening an app, calling a friend, or searching the Internet) you are witnessing applied machine learning in action. The program already contains text data and converts spoken words into text. Siri is programmed to constantly learn new words through text data to provide users with better experiences. 

Another speech recognition assistant that you may be familiar with is Alexa. 

Related Items

shopping on amazon

What else do you notice when you look at a product on Amazon? At the bottom of the product description, you will most likely find a section labeled “Customers also bought these items.” For example, when viewing a Laptop listing, Amazon will suggest a laptop bag, micro-USB cable, and mouse, as well as different variations of the laptop.

This is another example of AML in action known as “learning association.” Machines are taught to link one object to another. The concept can also detect malicious network activity. For example, your anti-malware software warns you about the file you’re about to download because it associates it with another malicious object.

Pattern Recognition

Pattern recognition in AML refers to the process of labeling specific data based on regularities. For example, if you keep watching sci-fi movies on Netflix, Netflix will recognize that pattern and recommend movies in the same genre.

Pattern recognition can also be used in other fields, such as detecting cancer in mammograms.

The Basic Process of Applied Machine Learning

Here’s a breakdown of the basic process of applied machine learning.  

Understand Machine Learning

You need to have a complete understanding of machine learning models, algorithms, statistics, and other data-related subjects so you can learn how to apply them adequately. Of course, this application is a step higher than ordinary machine learning, but it is best to have a basic understanding of fundamental machine learning concepts. 

Identify Problems

Gaining insight from the definition of AML as the use of machine learning to solve real-world problems, the second step in the process is to define the problem that needs to be solved. The problems usually center primarily on improving user experience and increasing value across various industries.

Gather Necessary Data

AML models are created by training on large samples of data. The data is already available. For example, the financial institution already has data when using ML to compute credit scores. It only needs to relay the information to the developers.

Choose Your Programming Language

You will need a programming background to use machine learning models to solve real-world business problems. AML is a backend programming technology. This means the language you select has to be a fully optimized backend language. Backend languages include Python, R programming language, Java and Javascript, Julia, etc. 

What Makes Python the Best Programming Language for AML?

Choosing a programming language for your product is a significant challenge. Numerous programming languages are available with dynamic features, so it is a big decision. However, only a few will work according to your goals and specific needs.

What is Python?

Example of Python code

According to the Python Software Foundation, “Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.” As a high-level programming language, it means it contains words easily comprehensible to humans. This makes debugging easy and allows developers to perform complex tasks in a couple of rather simple steps. 

Python ranks first in IEEE Spectrum’s annual ranking of popular programming languages, with over 8.2 million developers worldwide using it for coding. In addition, stack overflow programming language trends show that it is the only language that has increased in popularity over the last five years.

The increasing global adoption of machine learning is a major factor contributing to its growing popularity. About 69% of applied machine learning engineers exist, and Python has become the preferred language for data analytics, data science, and applied machine learning.

Why is Python Recommended?

Python is the preferred programming language for some IT industry titans, including Google, Instagram, Facebook, and Netflix. It is also the best language for applied machine learning today. Here is why:

Extensive Collection of Libraries and Frameworks

Programmers use Python frameworks and libraries to reduce development time. Python’s built-in libraries and frameworks provide pre-written code, allowing machine learning engineers to avoid starting from scratch. 

Continuous data processing is required for machine learning, and Python has built-in libraries and packages for almost every task to solve common programming tasks. When working with complex machine learning applications, this helps machine learning engineers reduce development time and increase productivity. 

The best thing about these libraries and packages is that there is no learning curve. Once you understand Python programming fundamentals, you can begin using these libraries.

  • Working with textual data? Use NLTK, SciKit, and NumPy.
  • Working with images? Use Sci-Kit image and OpenCV.
  • Working with audio? Use Librosa.
  • Implementing deep learning? Use TensorFlow, Keras, and PyTorch.
  • Implementing basic machine learning algorithms? Use Sci-Kit- learn.
  • Want to do scientific computing? Use Sci-Py.
  • Want to visualize the data? Use Matplotlib, Sci-Kit, and Seaborn. 

Implementing AML algorithms can be difficult and time-consuming. You can develop your product more quickly with these solutions. Your team would not have to reinvent the wheel and would be able to use an existing library to implement the required features.

Code Readability

Example of Python code for machine learning

Python is widely regarded as a highly effective coding language due to its simple syntax and readability. Python is an easy-to-learn language, so a large enough talent pool exists. The math behind machine learning is typically complex and obscure. As a result, code readability is critical for successfully implementing complex machine learning algorithms and versatile workflows. This allows machine learning engineers to focus on what to write rather than how to write.

Python’s readability is a built-in feature. This facilitates the exchange of ideas, algorithms, and tools among machine learning practitioners. As a result, Python is not only popular among machine learning engineers, but it is also a popular programming language among data scientists.

In addition, when you need to update a Python project, it is relatively easy to find someone who can look at the code and make the necessary changes even if they have never seen it before. As a result, developers do not have to spend a lot of time thinking about language complexities or deciphering someone else’s code.

Powerful and Fast

Python is a powerful programming language that can be used for almost anything. To begin with, it can be used for anything because it includes many features by default and standard libraries that cover almost any programming task.

Python saves time and effort by presenting ready-made solutions when performing scientific calculations, processing images, or developing operating system interfaces or protocols. This is an advantage for startups and established businesses because it means a faster return on investment (ROI) is made possible, and the ability to tailor the product based on reviews and feedback from real users.


Python is capable of interacting with code written in other programming languages. You can integrate your Python project into frameworks written in other languages and vice versa. This means you can create projects that combine Python with other programming languages (such as C++) to get the best of both worlds.

Additionally, Python is available for almost every operating system, including UNIX, Windows, macOS, iOS, and Android. It is also available on various platforms, including IBM, AIX, Solaris, and VMS.

Great Community and Popularity

applied machine learning in python

Python was among the top five most popular programming languages in the Developer Survey 2020 by Stack Overflow. As a result, you can easily find and hire a development company with the necessary skill set to build your AML-based project.

Python AI community has expanded globally. Python forums and an active exchange of experience with machine learning solutions are available. In addition, developers can provide advice and guidance.

If you turn to the Python community, you will not be alone. You will almost certainly find the best solution to your specific needs, as over 140,000 custom-built Python software packages can be found in online repositories. 


Python is an open-source programming language managed and supported by the Python Software Foundation, a non-profit organization. One of the primary benefits of open-source software is that it is completely free to use, modify, and distribute.

Because the Python community is open to all and encourages diversity, millions of experienced developers from all over the world contribute to it and contribute to the growing Python open-source database.

There are numerous conferences, meetups, and a great deal of code collaboration. This makes Python less prone to bugs and more secure than other popular programming languages. It also aids in the reduction of development costs.

Platform Independence

Platform independence refers to a programming language or framework that allows developers to create things on one computer and use them on another without (or with minimal) changes.

Python’s popularity stems from the fact that it is platform-independent. As a result, you can use Python code to create standalone executable programs for most common operating systems, allowing Python software to be easily distributed and used on those operating systems without needing a Python interpreter.

Developers typically use computing services such as Google or Amazon. Companies and data scientists, on the other hand, frequently use their machines with powerful Graphics Processing Units (GPUs) to train their ML models. And the fact that Python is platform-independent makes this training much more affordable and simple.

Final Thoughts

Applied machine learning has become an essential component of modern business and research in many organizations. It has evolved into a critical response tool for cloud computing and eCommerce, and it is now used in a wide range of cutting-edge technologies. 

Python remains the best-optimized programming language to help you easily navigate the world of applied machine learning.

Leave a Reply

Your email address will not be published. Required fields are marked *