Python AI: the secret to this language’s algorithms


Redacción Tokio | 19/10/2022

What do you know about algorithms? Today, they’re increasingly part of our daily conversations: we’ve all heard something about algorithms in Google or Facebook. Briefly described, we could say they’re a set of instructions that can be applied to computer programming and its languages. If you want to learn how to develop Python AI algorithms (one of the programming languages that is most valued by professionals) at Tokyo School we tell you how it works and all about the different types of automated learning.

We know this blog post might awaken your interest, and you can always deepen in this matter through our Python programming course.

Keep reading and discover all secrets about computer science today and Python AI!

AI algorithms are developed with the aim of creating machines that present similar capacities to those of human beings


How is Artificial Intelligence created?

Want to learn the main keys to Artificial Intelligence programming? This technology presents its own advantages and disadvantages. An increasingly important part of computing systems, companies are more and more in need of experts able to program it.

Artificial Intelligence refers to technology that is able to generate machines to imitate functions related to knowledge, such as problem solving or reasoning processes. Its working is based in algorithms, that is, a set of rules that facilitates the solving of a problem.

AI technology can be developed with the aim of solving a multitude of needs: medical applications, search engines, market value analysis, speech recognition, games, facial recognition, robotics, etc. 


Types of AI algorithms

Artificial Intelligence is based on automatic learning or machine learning, a field in computer science in charge of making machines “learn”.

Python AI can be developed through the different types of machine learning algorithms. Shall we go through them?

Reinforcement learning

Also known as RL, the intelligent agent learns thanks to the use of rewards or punishment, according to its success or failure, without the need for an instructor to indicate what must be done.

When it comes with this type of algorithms, agents learn by observing behaviors in a set environment, learning to adopt the right decisions to achieve set goals. This type of learning is based on a trial and error system.

An example of reinforcement learning includes that which is acquired by AI which is able to play chess, such as AlphaZero or DeepMind.

Dynamic programming, Q-Learning and SARSA (state-action-reward-state-action) are the main algorithms used in this type of learning.

Supervised learning

Supervised machine learning is based on predictive models that use training data. By employing information extracted from data, the agent is able to provide a specific exit. Thus, the model adjusts (is trained) to manage the relevant results.

Autonomous cars represent a clear example of this type of learning. It can also be very useful to solve scientific research issues where the system learns to tag (classify) certain vectors in certain categories (classes).

Decision trees, Naïve Bayes classifications, logistical regression, Support Vector Machines (SVM) or ordinary regression by least squares are the main algorithms used for this type of learning.

 Unsupervised learning

In this type of learning, modeling is performed through sets of examples created only by system entries, without any information about example categories. With all of this, the system must try to recognise patterns to tag new entries.

In unsupervised machine learning, the algorithm performs an autotraining without the need to receive external indications.

The main algorithms of this type of learning include

  • Clustering algorithms
  • Principal Component Analysis (PCA)
  • Independent Component Analysis (ICA) 
  • Singular Value Decomposition (SVD)

Semi-supervised learning

In this type of learning, the algorithm mixes notions of supervised learning with those of unsupervised learning in order to get an adequate classification. Through this system, labeled data and unlabeled data are taken into account.


Although transduction is somehow similar to supervised learning, a function is not constructed in an explicit way. The system thus is about predicting the categories of future examples through entry examples, their categories and new examples.

Multitask learning

This type of learning uses the knowledge that the system has previously acquired in order to face problems that are similar to the ones that have already been seen.


Python AI course: have you heard of Tokyo School?

The Python programming course and its specialization in Artificial Intelligence by Tokyo School represent a remote, online learning course that gets you ready for being part of the revolution that Python AI and computer programming as a whole have started.

Through this course, you’ll have the chance of getting specialized training in a a promising field in the present and the future all from the comfort of your own home and making the most of your free time. Are you interested in learning more about programming Artificial Intelligence? Would you like to learn how to develop Python AI algorithms? Well, this is the place!


What you’ll learn at our Artificial Intelligence course

With our Python course and the specialization in Artificial Intelligence, you’ll be able to meet the following learning goals:

  • Use Python language syntax and implement diverse projects
  • Work with AI Python libraries, including standard options, external and frameworks
  • Make connections with databases, manipulate data structures and archive handling
  • Intergrate new AI developments in existing computer systems
  • Design, develop and implement AI and Deep Learning techniques


Python AI: career options

Python AI doesn’t cease to grow and expand into multiple sectors. Through getting trained in this sort of specialization, many job positions at diverse companies will become available to you. Some of these career options include:

  • Designer for user and desk graphic interfaces 
  • General programmer: web, database, archive, network…
  • Technological consultant for Artificial Intelligence
  • AI developer
  • Software engineer and Artificial Intelligence programer


Learn Python programming with a specialization in AI!

As we’ve seen, Python AI presents several practical applications. All productive sectors can benefit from this advanced technology: medicine, logistics, trade, hospitality…

Would you like to devote your career to Artificial Intelligence? Then explore our Python Programming course with a specialization in AI at Tokyo School. Fill in the form below and we’ll fill you in with all the details about our course contents for you to deepen your knowledge  in this field brimming with career opportunities.

Would you like to learn more? Make Python AI your new job!

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