Machine Learning vs Neural Network

Differences Between Machine Learning vs Neural Network

Machine Learning is an application or the subfield of artificial intelligence (AI). Machine Learning enables a system to automatically learn and progress from experience without being explicitly programmed. Machine Learning is a continuously developing practice. The goal of Machine learning is to understand the structure of data and fit that data into models, these models can be understood and used by people. In Machine Learning generally, the tasks are classified into broad categories. These categories explain how learning is received, two of the most widely used machine learning methods are supervised learning and unsupervised learning.

The neural network is inspired by the structure of the brain. The neural network contains highly interconnected entities, called units or nodes. Neural networks are deep learning technologies. It generally focuses on solving complex processes. A typical neural network is a group of algorithms, these algorithms model the data using neurons for machine learning.

Head to Head Comparisons Between Machine Learning vs Neural Network (Infographics)

Below is the Top 5 Comparison between the Machine Learning vs Neural Network

Key Differences Between Machine Learning vs Neural Network

Below are the lists of points, describe the key Differences Between Machine Learning vs Neural Network :

As discussed above machine learning is a set of algorithms that parse data and learn from the data to make informed decisions, whereas neural network is one such group of algorithms for machine learning.
Neural networks are deep learning models, deep learning models are designed to frequently analyze data with the logic structure like how we humans would draw conclusions. It is a subset of machine learning.
Machine learning models follow the function that learned from the data, but at some point, it still needs some guidance. For example, if a machine learning algorithm gives an inaccurate outcome or prediction, then an engineer will step in and will make some adjustments, whereas, in the artificial neural networks models, the algorithms are capable enough to determine on their own, whether the predictions/outcomes are accurate or not.
Neural network structures/arranges algorithms in layers of fashion, that can learn and make intelligent decisions on its own. Whereas in Machine learning the decisions are made based on what it has learned only.
Machine learning models/methods or learnings can be two types supervised and unsupervised learnings. Where in the neural network we have feedforward neural network, Radial basis, Kohonen, Recurrent, Convolutional, Modular neural networks.
Supervised learning and Unsupervised learning are machine learning tasks.
Supervised learning is simply a process of learning algorithm from the training dataset. Supervised learning is where you have input variables and an output variable, and you use an algorithm to learn the mapping function from the input to the output. The aim is to approximate the mapping function so that when we have new input data we can predict the output variables for that data.
Unsupervised learning is modeling the underlying or hidden structure or distribution of the data to learn more about the data. Unsupervised learning is where you only have input data and no corresponding output variables.
In neural network data will be passing through interconnected layers of nodes, classifying characteristics and information of a layer before passing the results on to other nodes in subsequent layers. Neural network and deep learning are differed only by the number of network layers. A typical neural network may have two to three layers, wherein deep learning network might have dozens or hundreds.
In machine learning, there is a number of algorithms that can be applied to any data problem. These techniques include regression, k-means clustering, logistic regression, decision trees, etc.
Architecturally, an artificial neural network is exhibited with layers of artificial neurons, or also called as computational units able to take input and apply an activation function along with a threshold to find out if messages are passed along.
The simple model of neural network contains: The first layer is the input layer, followed by there is one hidden layer, and lastly by an output layer. Each of these layers can contain one or more neurons. Models can become more complex, with increased problem solving and abstraction capabilities by increasing the number of hidden layers and the number of neurons in a given layer.
There are supervised and unsupervised models using neural networks, the most generally known is the feed forward neural network, which architecture is a connected and directed graph of neurons, with no cycles that are trained using the algorithm called backpropagation.
Machine learning, learning systems are adaptive and constantly evolving from new examples, so they are capable of determining the patterns in the data. For both data is the input layer. Both acquire knowledge through analysis of previous behaviors or/and experimental data, whereas in a neural network the learning is deeper than the machine learning.