We hear about Machine Learning here, Artificial Intelligence there, Deep Learning, Clustering… Relatively new terms like the profession they are associated with: the Data Scientist. But all this still generates some confusion. Machine Learning is not about giving exterminator robots their own conscience. It is something else.
What is Machine Learning?
Machine Learning is a scientific discipline within the field of Artificial Intelligence. It is designed to create systems that adapt their behavior so that machines can make decisions on their own.
We may have read in many places that they learn automatically, but that would not be entirely true. Learning is something else, it is what a baby does by putting its feet in its mouth, it is what you do after realizing that the code you have been working on is not getting you anywhere, it is what Skynet did -or will do, depending on whether you read me from the future or the present.
So what is Machine Learning? In more technical language, Machine Learning is software capable of building an algorithm that adapts itself according to a set of data supplied to it.
The key to this software is that its improvements are without human intervention, and are improved autonomously over time (and data input) in search of better performance in a specific area we have established: book recommendation, driving alert systems, annihilation of the human race, etc.
How does Machine Learning work?
Its machine learning process consists of building different decision trees (hundreds, thousands or millions), and adapting them based on the known data set. But it does not stop there, it is also capable of applying heuristic formulas – in other words, in search of the solution to a problem – at each node of the tree in order to create a system of inferences to guide the new incoming information.
Recursively, it creates a decision tree for each branch it creates, where it will contrast the new data with those that have already served to arrive at or discard the current branch.
Let’s take a simple example. A new data is introduced, a word: “I will return”. You test it and look at the performance depending on the result you want to find. If it is positive, a decision is made to include it in the vocabulary.
If I have not explained well, do not worry, that’s why we bring you the Webinar on what is Machine Learning, to discuss all this in depth, the technologies used and more to remove any doubts you may have about it.
Where is Machine Learning applied?
Did you know that the same amount of information is generated in 48 hours as was generated from the beginning of life until the year 2000? The growth of “Data” is exponential, and therefore Machine Learning, which feeds on it, is a very broad field that is growing very fast and, being relatively new, it is normal that it is continually being divided into new specialties: face detection, object recognition, anti-virus, prediction and forecasting, autonomous vehicles, robots…
Machine Learning is designed for any field, as long as you find the right combination of usefulness and creativity. The idea is that with this technology you can move from being reactive to being proactive. For example, you can use historical data from a set of customers, properly organized, to predict future behavior.
But that’s something that’s already being done, right? Yes, but with the growth of data it is becoming increasingly difficult – if not impossible – to analyze, interpret and make a decision or prediction on a data set that is not only static, but varies. Algorithms can infinitely more easily detect patterns of behavior through the variables we provide them with. Therefore, the competitive advantage of using Machine Learning to extract valuable information is a weapon that cannot be underestimated.
When will Machine Learning be deployed?
Many businesses and many sectors not only use it today, but have been taking advantage of it for years. How do you think they recommend you a possible purchase that they think you will be interested in? Is there someone looking at your list of visits? No. Sectors such as online shopping, advertising, finance, traffic, etc., have been taking advantage of its potential for years. The technology is already living among us, only few still know about it.
What is the best programming language for Machine Learning?
Compared to other programming languages, Python is known for its readability and relatively low complexity. Machine Learning applications involve complex concepts such as calculus and linear algebra, which require a lot of effort and time to implement. Python helps alleviate this burden by allowing Machine Learning professionals to validate ideas quickly. Another benefit of using Python in Machine Learning is the pre-built libraries or also called frameworks. Different packages exist for different types of applications, as described below:
- Numpy, OpenCV and Scikit when processing images.
- When processing text, NLTK is again used with Numpy and Scikit.
- Matplotlib, Seaborn and Scikit for data representation
- Librosa for audio applications
- Scipy for scientific computing
- TensorFlow and Pytorch for deep learning applications
- Django integrated web application
- Pandas for advanced data analysis and data structure
Python provides the flexibility to choose between scripting or object-oriented programming. There is also no need to recompile code; developers can implement any changes and see the results immediately. You can combine Python with other languages to achieve the results and functionality you need.
Python is a versatile programming language and can run on any platform, including Windows, MacOS, Linux, Unix and others. When migrating from one platform to another, the code needs some minor adaptations and changes, and is ready to run on the new platform.
How to learn Machine Learning?
If you want to train yourself in the languages and tools to build machine learning models, the best option is Udemy’s Big Data, Artificial Intelligence & Machine Learning Full Stack Bootcamp where you will be able to master thousands of data and use them to create projects that incorporate machine learning algorithms.