Machine Learning, Data Science and Artificial Intelligence
Nowadays it is very common to come across these terms: data science, artificial intelligence, machine learning, deep learning and much more.
But what do these words actually mean?
Data Science is a generic term that refers to several disciplines: machine learning, data mining, data analysis, and statistics.
In addition, it includes activities related to working with big data,such as the process of extracting, transforming and loading data (ETL) into storage repositories.
The more data you have, the more business information you can generate. Using data science, you can discover patterns in data that you didn't even know existed.
Data science is widely used in multiple scenarios.
Companies – for example – use data science to predict user behavior and much more.
The main goal of Data Science is to make sense of data.
Gaining this understanding includes multiple steps depending on the project you are working on.
This can include from collecting and processing large amounts of data to predictive analytics using tools such as machine learning algorithms and deep learning neural networks.
Artificial intelligence – AI – has been around since the mid-50s but has become very popular in recent years thanks to increased data volumes,advanced algorithms, and improvements in computing power and storage.
The implementations of the algorithm have improved so much that we can run them on common hardware, even on your laptop or smartphone.
But what is artificial intelligence?
Artificialintelligence (AI) allows machines to learn from experience, adapt to new inputs, and perform human-like tasks.
In this way computers can be trained to perform specific tasks by processing large amounts of data and recognizing patterns in the data.
Until you come to conclusions or make decisions of high accuracy.
Machine Learning is a branch of artificial intelligence (AI) that is based on the creation of algorithms that can process and learn data independently.
The whole approach is based on the fact that it is more efficient to teach a computer how to learn, rather than programming it to perform all the required tasks.
An example of machine learning are the virtual assistants Alexa (Amazon) and Siri (Apple).
These systems use learning algorithms and, the more habits the user learns, the better it can handle requests.
Facial recognition is also an example of machine learning.
In this case, a still image will be the input for the system that will identify the people depicted inside it in a dynamic scenario.
ML libraries like Scikit-Learn are so advanced today that even an app developer with no background in mathematics or statistics, or with only basic AI training, can start using them to create, test, and deploy ML models.
Deep Learning algorithms are a branch of machine learning, which take over all those situations in which ML is weak.
DL uses neural networks to solve problems, which is a framework that combines various machine learning algorithms to solve certain types of tasks.
A deep learning system is essentially a very large neural network that is trained using a large amount of data.
There are different types of deep learning architectures, and it's not uncommon to hear about the use of a recurrent neural network (RNN) or a convolutional neural network (CNN).
The term "deep" refers to the number of layers or transformation points contained within the framework.
When the input passes through these layers it is made more abstract, ending in the output layer.
It is at this stage that a prediction based on the original input is made.
Deep Learning Examples
Deep learning is currently used in many complex tasks.
An example of deep learning is Google Translate,a software that can translate written texts in more than 100 languages.
Netflix also uses dl to improve the personalized offer of its customers: by analyzing the engagement data, in fact, it is able to advise more and more accurately which programs are in line with our preferences.
Looking to the future, deep learning will be applied to technologies such as: finance, autonomous vehicles and healthcare.