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Learn machine learning with Python | Introducing what you can do and libraries!


Python, which started as an eponymous hobby project by Monty Python, is now one of the most popular and widely used programming languages ​​in the world. Besides web and software development, Python is also used for data analysis, machine learning, and even design.

In this installment, we’ll take a closer look at what Python is used for and why it’s such a popular and versatile programming language. We also pick up steps to build a machine learning model, Python libraries, and some of the best ways to learn Python.

Table of Contents

  • What is machine learning in the first place?
    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • 8 things you can do with Python
    • 1. AI/machine learning 
    • 2. Data analysis 
    • 3. Data visualization
    • 4. Web development 
    • 5. Game development 
    • 6. Finance 
    • 7. SEO measures
    • 8. Design  
  • Why Python is recommended
    • Easy to learn even for beginners
    • High reliability
    • There are many libraries and frameworks available
  • Disadvantages of Python
    • Slow execution
    • Vulnerable to mobile computing
    • Likely to lead to runtime errors
  • 5 steps to build a machine learning model
    • Step 1. Understand the problem and define success
    • Step 2. Understand and identify data
    • Step 3. Data collection and preparation
    • Step 4. Determine model features and train
    • Step 5. Iterate and refine the model
  • Introduction to Python Libraries
  • Introduction of libraries by data characteristics
    • Natural language processing
    • Image recognition
    • Voice recognition
  • How to learn Python?
    • Do you need programming?
    • Learn from books
    • Learn at school
    • Learn on site
  • Summary

What is machine learning in the first place?

Machine learning is a technology that allows machines to discover patterns and rules from large amounts of data and use them for various things to make judgments and predictions. Machine learning can be divided into three main categories: supervised learning, unsupervised learning , and reinforcement learning.

The next article summarizes machine learning, so please refer to it.

Supervised learning

Supervised learning, also known as supervised machine learning, is a subclass of machine learning and AI ( artificial intelligence ). It is defined by using a labeled dataset to train an algorithm to accurately classify data and predict outcomes.

Supervised learning has helped solve many real-world problems, such as sorting spam emails into folders separate from the inbox.

Unsupervised learning

Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns and groupings of data without the need for human intervention.

It can discover similarities and differences in information, making it an ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, image recognition, and more.

Reinforcement learning

Reinforcement learning is a branch of machine learning. It is employed by various software and machines to find the best course of action or path to take in a given situation.

In supervised learning, the model learns with the correct answer because the training data has the answer, whereas in reinforcement learning, the answer is not, but the machine decides what to do to perform a given task. There is a difference.

8 Things you can do with Python

Python is a data-oriented, object-oriented, easy-to-understand programming language that was announced in 1992. It is designed to be written and understood relatively intuitively, making it ideal for those who want rapid development. coding language.

Here are eight use cases to learn more about what Python can do. Here are some examples of how to use it.

  1. AI/machine learning
  2. Data analysis
  3. Data visualization
  4. Web development
  5. Game development
  6. Finance
  7. SEO measures
  8. Design

I will explain each.

1. AI/machine learning 

Python is a stable, flexible, and simple programming language that is ideal for a variety of machine learning and artificial intelligence projects.

In fact, Python is one of the popular languages ​​among data scientists, and there are many Python machine learning and AI libraries and packages available.

2. Data analysis 

Data analytics, like AI and machine learning, is one of the fastest growing areas of Python programming. With more data being generated than ever before, we need people who can collect, manipulate and organize information.

Python is easy to learn, flexible, and well-supported, making it relatively quick and easy to use for analyzing data. Therefore, it makes sense to use Python for data science and analytics that involve working with large amounts of information, manipulating data, and performing iterative tasks.

3. Data visualization

Data visualization is another popular and developing field. Again, the strengths of Python are used here. Besides Python’s flexibility and being open source, Python offers a wide variety of charting libraries with all kinds of functionality.

Whether you want to create simple graphs or create interactive plots, you’ll find a library that meets your needs.

4. Web development 

Python is a great choice for web development. The reason is that there are many Python web development frameworks such as Django, Pyramid and Flask. These frameworks are used to create sites and services like Spotify and YouTube.

Features such as database access, content management, and data authentication are all possible and easily accessible thanks to the rich library and modules that come with the Python framework. Given Python’s versatility, it’s no surprise that it’s widely used in web development.

5. Game development 

Although hardly an industry standard, Python is also used in the gaming industry. But making your own computer games in Python is a great way to learn Python.

Making games requires the use of many core programming skills. In short, game development uses variables, loops, conditionals, functions, object-oriented programming, and many other programming techniques and algorithms.

By doing so, you can develop different skills and improve your knowledge in different areas.

6. Finance 

Python is increasingly used in the financial world and can be a valuable tool in determining asset price trends and forecasts, as well as automating workflows across disparate data sources.

As mentioned earlier, Python is an ideal tool for working with big data sets, making it one of the languages ​​of choice in the financial industry.

7. SEO measures

A slightly surprising use of Python is in the area of ​​Search Engine Optimization (SEO). SEO is the process of making your website appear higher when users search for it.

While markup languages ​​such as HTML and CSS and programming languages ​​such as JavaScript both affect how websites are built and displayed to end users, Python can be used directly for technical SEO. I can do it. For example, Python is leveraged to implement changes across multiple pages and categorize keywords.

8. Design  

Python can be used to develop graphic design applications. Although not widely known, Python is used in various 2D image processing software such as ‘Paint Shop Pro’ and ‘Gimp’.

Other 3D animation software such as Lightwave, Blender and Cinema 4D also use Python, which shows just how versatile the language is.

Why Python is recommended

Here are three reasons why Python is the preferred programming language.

  1. Easy to learn even for beginners
  2. High reliability
  3. There are many libraries and frameworks available

I will explain each.

Easy to learn even for beginners

The Python language’s simplified syntax and emphasis on natural language make it easy for beginners to use and learn.
As such, python code is relatively easy to write and runs faster than other programming languages.

High reliability

Python is a language that was created almost 30 years ago, so there are plenty of materials such as documentation, guides, and video tutorials for learning the language. These materials can be used by learners and developers of all skill levels and ages to help them increase their knowledge of the Python programming language.

In any programming language, it’s difficult to grow your skills smoothly without developer support and guidance.
However, Python has been around for a very long time and by a lot of people, so it doesn’t have that problem.

There are many libraries and frameworks available

With Python’s corporate sponsorships and large support community, python has excellent libraries that can save you time and effort in the initial development cycle.

There are also many cloud media services that are cross-platform with tools like libraries, which can be very beneficial.

Disadvantages of Python

Slow execution

Because Python executes code line by line, it often runs slowly. As such, Python is not used where speed is critical.

Vulnerable to mobile computing

Python is commonly used in server-side programming. It is memory inefficient and processing power is slow compared to other languages. As such, you don’t often see Python in client-side or mobile applications.

Likely to lead to runtime errors

Python is a dynamically typed language, so you can change the data type of your variables at any time. Variables that store integer values ​​may store strings in the future, which can lead to runtime errors.

Therefore, you should thoroughly test your application when using Python.

5 steps to build a machine learning model

Here, we will introduce the steps to build a machine learning model in Python in five steps.

  1. Understanding the problem, defining success
  2. Understanding and identifying data
  3. Data collection and preparation
  4. Determine the features of the model and train it
  5. Iterate and adjust the model

I will explain each.

Step 1. Understand the problem and define success

The first step in any machine learning project is understanding the problem. Before we try to solve it, we need to know what kind of problem we are trying to solve.

For example, asking the following questions will help you understand the problem and define success:

  • What are the “success” criteria for a machine learning project?
  • What are the expected inputs and expected outputs to the model?
  • What is the quickest way to solve the problem?
  • How is the model benefit measured?

Step 2. Understand and identify data

Understanding the problem and defining success doesn’t give you the data you need to build a machine learning model. You can’t build a model without data, and having access to data is not enough.

Building a model should focus on data identification, initial collection, and quality identification.
For example, asking questions like the following will help you understand and identify your data:

  • Where is the source of the data needed to train the model?
  • How much data do you need for your machine learning project?
  • For supervised learning, is there a way to label that data?
  • Is it possible to use a pre-trained model?

Step 3. Data collection and preparation

Once you have properly identified your data, you need to shape it so that it can be used to train your model.
This step involves collecting the data and labeling the data necessary to build the datasets that will be used in your modeling efforts.

Step 4. Determine model features and train

Once the data is ready for use and the problem to be solved is known, it is time to move on to determining and learning the model features.

When training a model, apply various techniques and algorithms to train the model so that it can learn from the good data you prepare.

Step 5. Iterate and refine the model

Even if the model is operational and you are continuously monitoring its performance, you are not done yet.
What you do next should always be iterating on the process and improving in time for the next iteration.

Real-world data changes in unexpected ways. Data changes may create new requirements for deploying models to different endpoints or new systems.

Introduction to Python Libraries

A Python library is a collection of useful functions that eliminates the need to write code from scratch. Python libraries play an important role in developing machine learning , data science, data visualization, image and data manipulation applications, and more.

Rather than writing code from scratch, it is more efficient to develop using a library.

The following article summarizes the Python library, so please refer to it.

Introduction of libraries by data characteristics

From here

  1. Natural language processing
  2. Image recognition
  3. Voice recognition

I will introduce the library according to the data characteristics of .

Natural language processing

Until now, only experts have been able to participate in natural language processing projects that require excellent knowledge of mathematics, machine learning , linguistics, etc. Off-the-shelf tools are now available to simplify text preprocessing so developers can focus on building machine learning models.

There are many tools and libraries made to solve NLP problems, such as:

  • Natural Language Toolkit (NLTK)
  • TextBlob
  • Core NLP
  • Gensim
  • spa Cy
  • polyglot
  • scikit-learn
  • Pattern

Image recognition

Images must be processed before they can be fed into a machine learning model. Tools and libraries are needed to process images efficiently and quickly without compromising results.

There are many tools and libraries made to solve image recognition problems.

  • OpenCV
  • Scikit-Image
  • SciPy
  • Pillow/PIL
  • NumPy
  • Mahotas
  • SimpleITK
  • Pgmagick

Voice recognition

Speech recognition enables the elderly and the physically and visually impaired to interact quickly and naturally with cutting-edge products and services.

Tools and libraries created to solve speech recognition problems mainly include the following.

  • speech recognition
  • pyaudio
  • Julius

How to learn Python?

Do you need programming?

No programming may be required.
For example, if the model you want to create doesn’t require any coding.

However, as I mentioned earlier, Python can do many things, and by combining it with programming, it becomes possible to develop various products and services.

Learning programming skills is not easy, but it is a skill that most people can acquire if they learn it properly.
In this way, programming has the merit of learning, and it is possible to acquire skills, so if you want to learn Python, it is a good idea to learn programming as much as possible.

Introduction of services that can use machine learning without programming

Machine learning and AI technologies have exploded in their capabilities and applications in recent decades. But until recently, you had to be a data scientist or computer engineer to really use a teaching machine.

The market is flooded with machine learning tools and platforms that don’t require coding skills. These platforms democratize machine learning, and some can train sophisticated models for specific needs.

For example, Google AutoML harnesses the power of artificial neural networks to build effective predictive models from ordinary text and image data, and integrates with Google Sheets, Google Slides, etc., making it easy to can be used.

Learn from books

If you’re new to Python, you’re probably in one of two situations:

  • Those who are new to programming and want to start learning Python
  • Those who have some programming experience in other languages ​​and want to learn Python from now on

The article below focuses on the first of these two scenarios, and provides a comprehensive list of the best books for readers new to both programming and Python.

Learn at school

If you’re looking for a new career, learning to code can equip you with skills for new digital jobs, including working from home.

If you want to become a Python programmer from scratch, you should start with a course to learn the basic concepts of Python. Once you’ve mastered your coding skills, move on to more specialized and advanced courses.

The following article summarizes the recommended schools for learning Python, so please refer to it.

Learn on site

There are sites you can use to learn Python for free. Among other things, Google offers a good set of Python tutorials for beginners.

The course includes written tutorials, lecture videos, and lots of code exercises to practice coding in Python. In the first exercise you will learn basic Python concepts such as strings and lists, and in the next exercise you will write a complete program that works with text files, processes and HTTP connections.

As mentioned earlier, Google itself uses Python in many of its projects. And these materials are commonly used internally at Google to teach Python to people who are just starting to code or have little programming experience.

The best feature of this teaching material is that the lecture videos are available on Youtube. Therefore no other account is required.

Below is the official site. If you are interested in learning Python, let’s start now.


This time, with the theme of “building a machine learning model with Python”, I have explained in detail from “what is machine learning” to “how to learn Python”.

Currently, the demand for Python programmers and developers is on the rise. Python is used in many different programming contexts, from games to web applications. The future of Python looks very bright in 2022 and for many years to come.



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