Introduction to Machine Learning: Where to Start? 🤖

What is machine learning?

Machine learning (ML) is a technology that allows computers to learn from experience and automatically improve their results without explicit programming. Imagine that you want a computer to learn how to recognize cats in pictures. Instead of writing rules for each part of the cat's body, you just show the computer a lot of images of cats and non-cats, and it learns to find the differences.

Machine learning can be compared to the work of a teacher who explains something to his student. However, in this case, the student is a computer, and the explanations are data 📊. Depending on the type of task, the computer can learn to classify objects, predict values, find hidden connections, and much more.

Machine learning is divided into several types:

• Supervised Learning - when we have data and known answers. For example, predicting the price of a house based on its characteristics.

• Unsupervised Learning - when the answers are not known in advance, and the task of the model is to find hidden patterns. For example, clustering users by behavior.

• Reinforcement Learning - when the model learns through interaction with the environment and receiving rewards. This is, for example, a way of teaching robots or game AI.

What programming languages are used?

In fact, machine learning does not require any special, super-complex programming languages. Most specialists use languages that are already known to many:

• Python 🛠️ is the most popular language for machine learning due to its simplicity and a huge number of libraries, for example, scikit-learn, TensorFlow, PyTorch and others. Python is often chosen for its readability, which is especially important for beginners learning the basics.

• R 🔢 — often used for data analysis and statistics. This language has powerful tools for data visualization, which helps to better understand the processes occurring in the model. It is also suitable for creating machine learning models, especially in an academic environment.

• JavaScript 🕹️ — with the help of libraries such as TensorFlow.js, you can apply machine learning in web applications. This opens up opportunities for developing interactive and educational projects directly in the browser, making machine learning more accessible to a wide range of people.

In addition to these languages, you can also find the use of Java, C++ and others, especially in high-performance systems that require maximum speed.

Simple examples for beginners 🐣

Machine learning sounds complicated, but let's look at some simple examples:

Example 1: Weather forecast

Imagine you want to predict whether tomorrow will be sunny. You have weather data for the past few weeks — temperature, precipitation, pressure, etc. You can create a simple machine learning model that will try to predict the weather for tomorrow based on this data. The main idea here is that the model “learns” from previous data and tries to make the best prediction based on the experience gained.

This model can also be used for more complex tasks, for example, to predict the level of air pollution or to assess the risk of natural disasters.

Example 2: Handwritten number recognition ✍️

Another example is the recognition of handwritten numbers. We have a lot of images where people have written numbers from 0 to 9. The machine learns to recognize numbers by looking at these images. Over time, it gets better and better at this. This example is part of the so-called of the neural network, which copies the work of the human brain, trying to understand what is depicted in the picture.

Number image	Model forecast
	0
	5
	7

Such models are used in banking systems for signature recognition, in voting systems, and even in phones for handwriting recognition.

Example 3: Recommendation systems 🎥

Another example of machine learning is recommendation systemsWhen you watch movies or listen to music on platforms like Netflix or Spotify, a machine learning system analyzes your preferences and recommends content you might like. This is done by analyzing what users like you watch or listen to.

How to start learning machine learning? 🚀

You can start with the simplest steps. Here are some recommendations:

1. Learn the basics of programming. If you are not yet familiar with Python, start by learning the basics of this language. It is very convenient for beginners and has many educational resources. The main topics include variables, loops, conditional statements, and functions.

2. Learn how to work with data. Data is the foundation of machine learning. Try working with tables, learn how to use pandas for data analysis. Working with data includes cleaning, transforming, and visualizing it. These skills will help you better understand what is happening at each stage of the model.

3. Take the initial courses. There are many machine learning courses for beginners on the Internet. However, very soon the Kodik application will offer a detailed course on machine learning, which will help you master this technology step by step and learn how to apply it in practice.

4. Use ready-made libraries. Python has many libraries that make it easy to work with machine learning: scikit-learn for simple models, TensorFlow for neural networks, Keras for rapid prototyping, and PyTorch for flexibility in research. Using these libraries will allow you to quickly create models and learn by doing.

5. Solve projectsTo truly understand how machine learning works, you need to solve real problems. Start with small projects, such as predicting housing prices, analyzing movie reviews, or recognizing images. Gradually complicate the tasks, and you will see how your skills grow.

6. Join the community. Machine learning is an area where it is very important to share experiences and learn from others. Forums like Stack Overflow, communities on Reddit, and the Kaggle platform will help you get answers to your questions and learn from others' experiences.

Our assistant: the Kodik app 👨‍💻

If you find it difficult to start on your own, we recommend trying our app Code. It will help you take your first steps in the IT world and teach you the basics of programming. In the near future in Code There will also be a course on machine learning, which will make learning even more accessible and interesting. With the help of our application, programming and machine learning will become not only accessible to you, but also fascinating!

Try Code, and you will see that programming is interesting and exciting! Together we will take the first steps into the world of technology and open up new opportunities for you!