Neuronal network and Machine Learning

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) have become so deeply entwined in our daily lives in so little time that we have incorporated these technologies without considering their connotations. But what is the relationship between neural networks and machine learning?

For most people, AI, ML and DL represent the same or very similar concepts. However, while these technologies are interrelated, they present significant differences.

In this article, we’re trying to shed some light on one of these sources of confusion: neural networks and machine learning. Through a short introduction to each of the concepts, we will go on to establish the differences between them and how they relate to each other.

This certainly is an exciting field that may be attractive for you to learn more and become a specialized professional after receiving specific training in machine learning and artificial intelligence.

 

What is machine learning?

Machine learning or automatic learning is part of the much broader field of artificial intelligence. Machine learning seeks to build intelligent systems or machines that can learn through experience, without being explicitly programmed to do so or requiring any human intervention.

As such, this activity is in constant evolution. Machine learning aims to understand data structures (which are then fed into an algorithm programmed by humans) in order to fit it into models that can be used by companies and organizations for different purposes.

Machine learning presents two fundamental methods for AI systems to work: supervised learning and unsupervised learning.

The difference between the two systems is supervised learning processes involve the algorithms having a series of objective variables with specific values that are used to train the model. On the other hand, in unsupervised models, machines learn and classify variables automatically in order to make predictions.

 

What is a neural network?

Artificial neural networks are inspired by the structure of human brains. In fact, a neuronal network is essentially a model for machine learning, but it is a much more complex system that is only used in deep learning and not in machine learning processes, since, for the latter, both algorithms and systems are simpler and machines do not learn by themselves, but only do it from models implemented by humans.

In this way, it’s possible to conclude that neural networks and machine learning are not the same, although the two concepts are related to each other and can be included in the broader category of artificial intelligence.

A neural network is a network of interconnected entities known as nodes in which each node is responsible for one simple calculation.

In this way, a neural network works in a similar way to human brain neurons, since connections are established between nodes, information is sent and certain conclusions and predictions are drawn from the data that was initially entered.

 

Machine Learning vs Neural Network: the key differences

Since machine learning models are adaptive, they continually evolve by learning through new experiences and sample data. Therefore, they are able to identify the patterns in the data that is fed to them and which represent the only input layer. However, when it comes to neural networks, even a simple model presents several layers.

This is only one of the differences between these two models, now we are going to see other fundamental differences between neural networks and machine learning:

• Machine learning uses advanced algorithms to analyze data, learn from it, and use it to discover meaningful patterns of interest. Meanwhile, a neural network uses a wide variety of algorithms for data modeling and prediction making.
• While a machine learning model makes decisions based on what it has learned from the data, a neural network organizes the algorithms in such a way that it can make accurate decisions on its own.
• Neural networks do not require human intervention, as the layers nested within transport data through the nodes to draw their own conclusions. This, in the long run and over time, makes them capable of learning through their own mistakes.

As we mentioned earlier, machine learning models can be classified in two types: supervised and unsupervised learning models; on the other hand, neural networks can be classified into feedforward, recurrent, convolutional, and modular neural networks.

Not only do they differ in how they learn or transmit data, but they also are different in the way they are classified and, as we will now see, in the way they operate and present practical applications.

 

Performance comparisons between neural networks and machine learning

A machine learning model follows a simple work pattern: it learns from the data that is fed to it. On the contrary, the structure of a neural network is quite complicated. In it, data passes through several layers of interconnected nodes. Each node sorts features and information from the previous layer before the results travel to subsequent layers.

In neural networks, the first layer is the input layer, followed by a hidden layer, and finally an output layer. Each layer contains one or more neurons. The more layers and the more artificial neurons the system has, the more effective it will be in measuring and providing results.

 

Applications of neural networks and machine learning

Machine learning is applied in areas such as:

•   Medical care
•   Retail
•   E-commerce (recommendation engines)
•   Self-driving cars
•   Online video streaming
•   Internet of things
•   Transport and logistics

Neural networks, on the other hand, are used to solve numerous business challenges, including:

•   Sales forecasts
•   Data validation
•   Customer research
•   Risk management
•   Speech recognition
•   Character recognition

 

Skills for neural networks and machine learning

There are a number of things that need to be considered before delving deeper into neural networks and machine learning, as well as the skills and knowledge that are needed for each of these fields.

The skills required for machine learning include:

•   Programming
•   Probability and statistics
•   Big data
•   Knowledge of ML frameworks
•   Data structures
•   Algorithms

For neural networks, the ideal is to have skills such as:

•   Data modeling
•   Math
•   Linear algebra
•   Programming
•   Probability and statistics

 

Get training for a promising career as a neuronal network specialist!

Now you know a little better what neural networks and machine learning are. Two fields that, as you probably have already learned, are related to each other but also present some significant differences that need to be taken into account when making a decision about what interests you the most or which field is the best for you.

Whatever the decision is, if you are interested in delving into the exciting world of Machine Learning, Tokyo School’s Machine Learning Specialization Course you help you advance in this career choice and become a specialized expert.

Do not hesitate! Request information now!