Machine learning fundamentals in Python

Understanding machine learning fundamentals is a way to approach the future of artificial intelligence, a time that so many pioneers of this technology have dreamed of and that is none other than machines that present similar capacities to those of humans, so that it is possible for them to learn by themselves. Knowing and studying machine learning fundamentals with Python will help you understand how programmers can develop this type of technology.

Would you like to be one of them? At Tokio School you can learn through our Python programmer course with an specialization in artificial intelligence, a training designed for students interested in exploring this type of technology, able to generate intelligent machines through one of the most widely used programming languages today.

To fully understand how this type of artificial intelligence works, we are going to go through some key concepts you must understand before getting down to work. Do you want to know the most important machine learning fundamentals in Python? Keep your eyes on the screen!

A first approach to Machine learning

Machine learning (sometimes referred to as automatic learning) is defined as a computer science discipline that falls under the umbrella of artificial intelligence and which enables the development of systems or machines with the capacity to learn on their own, without any human intervention.

It refers to, so to speak, the ability of a specific machine or software to learn and offer certain answers, actions or predictions based on the adaptation of algorithms that are part of its programming and a certain type of data that is entered into the system.

The automation of certain operations makes it possible to reduce human intervention in this type of systems and processes, so that it is possible to get the machines to develop and learn by themselves.

Machines also use past experiences to make these decisions, a very graphic example of this being the spam filtering processes in the automatic classification of incoming mail. Machines figure out what spam is and automatically deliver it to the correct inbox based on a ton of data from hundreds of thousands of users who previously marked specific types of emails as spam.

Taking this example to a larger scale, what actually happens is that certain algorithms are modified via the input of massive amounts of data, which makes it possible for machines to predict scenarios and make decisions automatically, based on certain conditions.

Somehow, this increasing amount of input data makes it possible for machines to program themselves through algorithms and develop new computational responses to the information fed to them. Therefore, the greater the amount of input data, the more complex the system’s calculations can become and the more accurate, effective and precise the predictions or actions will be.

To understand machine learning fundamentals in Python, it’s important to consider that machine learning combines concepts and techniques from different areas of knowledge such as mathematics, statistics or computer science.

Why use Python in machine learning

Python is a multi-paradigm programming language that is able to support object-oriented processes, as well as imperative and functional programming. So to speak, Python has become the Swiss army knife of any professional programmer thanks to its versatility.

The main advantage of this programming language is its accessible learning curve, one of the keys that facilitates this is that its guiding principles include offering an enormous level of code readability.

As such, many more programmers are interested in learning Python through a dedicated course and not just using it as an initiation language, since it is actually part of an endless number of platforms, applications, companies, servers, etc.

Another crucial advantage of this programming language is the growing and prolific community of developers that are using it, meaning that it is possible to access a lot of libraries (that is, sets of functional implementations for a certain language) with which to expand its functionalities.

Every day, there are more and more libraries related to this language and specially designed to apply artificial intelligence, so it is increasingly easier to program machine learning with Python.

Advantages of Python

• Simple and fast to write, designed to make the program easier.
• Flexible, as it offers many facilities thanks to its high level of readability.
• Neat and clean.
• Easily adapted to almost any system.
• Widely used, with a big user community of users pushing its development forward, as well as its effectiveness and growth.

The evolution of this programming language over the last 30 years and its simplicity have turned it into an essential component for the future of big data and the use of machine learning algorithms. This requires specialist professionals that have specialized knowledge of these disciplines and who are capable of developing them using Python.

In addition to this, Python represents a programming language that is very present in the field of computer science education and is undoubtedly the reference language when approaching not only machine learning specifically, but programming in general.

And just as its presence in the educational field has evolved, it is also achieving great advancements in the research field, making it possible for many new Artificial Intelligence and Machine learning tools to be developed in Python.

Would you like to learn Python from scratch and be able to create systems with machine learning capabilities?

Venture into the world of AI through our Python programming course with a specialization in artificial intelligence and learn to design, develop and implement these techniques from our expert teachers and multiple resources and material at your disposal.

Would you like to learn more about our course? Do not hesitate to contact us through the form below and let us help you achieve your goal.