Machine Learning vs Neural Networks: What is the Difference?

In my journey across Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), it’s clear these technologies, though intertwined, have distinct differences. As a professional who has navigated these differences, I’ve seen how AI encompasses ML, with DL focusing specifically on neural networks. This distinction, Machine Learning vs Neural Networks, is crucial for applying AI effectively in real-world scenarios. 

From my experience, understanding these nuances is foundational. Whether implementing ML algorithms for analytics or utilizing neural networks for image recognition, each has unique strengths. To those aspiring to deepen their AI knowledge, I recommend comprehensive courses offered by top universities. These programs blend theoretical understanding with practical experience, a combination I’ve found essential in my career. 

Join the ML and AI Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.

Today, we’ll shed light on one such source of mass confusion – Machine Learning vs Neural Network.

Machine Learning vs Neural Networks: Table of Comparison

In the rapidly evolving world of artificial intelligence (AI), understanding the nuances between machine learning and neural networks is crucial for professionals looking to make their mark. Here’s a closer look at how machine learning vs neural networks, highlighting examples and case studies from my career. 

Parameters	Machine Learning	Neural Networks
Definition	Machine learning is a subset of artificial intelligence that enables a system to learn from data rather than through explicit programming.	Neural networks, a subset of machine learning, are algorithms modeled after the human brain that help computers recognize patterns and solve common problems in AI, such as classification and prediction.
Approach	Uses a variety of algorithms, such as regression and decision trees, to solve problems by learning from data.	Employs layers of interconnected nodes or neurons to process data in a complex, nonlinear manner.
Application	Widely used in predictive analytics, spam detection, recommendation systems, etc. For example, Netflix uses machine learning to recommend movies to its users based on their viewing history.	Primarily used in deep learning applications like image and speech recognition. Google Photos uses neural networks for its facial recognition feature.
Flexibility	It can be more easily applied to a wider range of problems but might require more manual tuning for specific tasks.	Highly effective for tasks involving large amounts of data and complex patterns but requires substantial computing power and data.
Example	In finance, machine learning models are used for credit scoring and algorithmic trading.	AlphaGo, developed by DeepMind, uses neural networks to outperform human players in the game of Go.

What is Machine Learning?

Machine Learning falls under the larger canvas of Artificial Intelligence. Machine Learning seeks to build intelligent systems or machines that can automatically learn and train themselves through experience, without being explicitly programmed or requiring any human intervention.

In this sense, Machine Learning is a continuously evolving activity. Machine learning aims to understand the data structure of the dataset at hand and accommodate the data into ML models that can be used by companies and organizations. 

The two core ML methods are supervised learning and unsupervised learning. Learn more about the types of machine learning.

The following section discusses the usefulness of Machine learning:

Benefits of Machine Learning:

1.Enhanced decision-making:

Machine learning uses advanced algorithms to improve the decision-making process capacity. It facilitates innovative models and business services simultaneously. It provides a deep understanding of the variations and types of data patterns. You can determine which step to take next based on the variations and data patterns.

2. Increases business productivity:

It improves the business process and productivity, contributing to business growth. It helps you to adapt to the changing situations at workplaces quickly. The data continue to be updated daily. So, the work environment, too, keeps on changing quickly. Machine learning reduces the chances of error occurrence by half. Hence, it boosts business productivity. This aspect is important to consider when carrying out deep learning vs neural network.

3. Removes manual data entry:

One of the most common concerns in many organizations is the usage of duplicate records. Machine Learning algorithms use predictive models that significantly avoid any errors caused by manual data entry. The corresponding programs use the discovered data to enhance these processes. Hence, the employees can save time to focus on other important business tasks.

4. Guarantees customer satisfaction:

The machine learning algorithms are uniquely designed to continue attaining experience with time. They are accurate and efficient. These algorithms improve the machines’ decision-making skills. Machine learning can anyhow find a way to make accurate decisions or predictions, although the data is overwhelming and ever-increasing.

It benefits businesses with the latest market opportunities related to revenue.  As a result, it can satisfy the customers’ expectations and boost your business’ sales in less time. Moreover, it can quickly recognize threats in the market. You can compare deep learning vs neural networks based on this aspect to have a clear judgment.

5. Provides product recommendation:

Unsupervised research assists in the development of suggestion systems depending on goods. Currently, most e-commerce platforms use machine learning to provide product recommendations. ML algorithms use the consumers’ purchasing experience to balance it with the assets’ huge inventory. This helps in detecting secret trends and connects identical products. Finally, these goods are recommended to the consumers.

6. Detects spam:

Machine learning is widely used for spam detection. It uses spam filters to identify spam and phishing communications.

7. Improves network security:

Machine learning improves an organization’s security. It helps organizations to develop new systems capable of quickly and efficiently recognizing unknown threats. It can track abnormalities present in network activity and automatically execute relevant actions. When the ML algorithm is used for self-training, it removes manual research and analysis. So, it enhances the organization’s network security. Many deep learning neural networks are also used for this purpose.

8. Simplifies business analysis:

ML is used in business analysis that involves huge volumes of precise and quantitative historical data. It is widely used for algorithmic trading, portfolio management, fraud detection, and lending in finance. The future ML applications for finance will entail Chatbots and a few other interfaces for improving customer service, security, and sentiment analysis. Many neural networks and deep learning algorithms are also used to streamline finance analysis.

Before getting deep into deep learning vs neural network, let’s go through the details of neural networks.

What is a Neural Network?

The structure of the human brain inspires a Neural Network, which is essentially a machine learning vs neural networks model (more precisely, Deep Learning) used in unsupervised learning. A Neural Network is a web of interconnected entities known as nodes, wherein each node is responsible for a simple computation. In this way, a Neural Network functions similarly to the neurons in the human brain.

Read: Deep Learning vs Neural Network

Also known as an artificial neural network, it is a technical machine learning model commonly used in supervised learning.

Neural networks are categorized into three main types. They are sequence learning, classification, and function approximation.  Examples of different types of neural networks are Perceptron, Hopfield, convolutional neural networks, autoencoders, Boltzmann machines, self-organizing maps, restricted Boltzmann machines, deep belief networks, recurrent neural networks, etc. The neural network’s key application is classification.

Technically, a neural network is a kind of machine learning model that is used in supervised learning. These deep learning neural networks estimate the way how neurons work in the human brain. They connect various nodes, and each node is tasked with a direct computation.

In other words, neural networks represent a collection of algorithms developed to identify patterns. They classify or group raw input to comprehend sensory data through machine perception. The real-world data, like texts, images, sounds, etc., should be transformed into vectors to allow neural networks to identify the patterns.

Neural networks can’t learn the fundamental rules if your dataset is small.  Its internal structure is difficult to comprehend.

The following benefits help you to compare neural networks and deep learning.

1. Stores information on the entire network:

In conventional programming, data is stored on the network instead of a database. The neural networks make sure the entire network’s operation is not stopped when a few pieces of data disappear from a location.

They provide good fault tolerance. They make sure the corruption of one or multiple artificial network cells doesn’t impact the output production. Hence, networks can better tolerate errors.

2. Distributed memory:

Two aspects are important to allow an artificial neural network to learn. They outline the examples and train the network as per the anticipated output by offering related examples. These examples are directly correlated with the network’s development.

3. Can work with incomplete knowledge:

The output produced by the data may be incomplete. The neural networks can work on this data to identify the missing aspect and work accordingly.

4. Avoids network corruption:

A network can slow down or degrade over time. The neural networks protect the data from this corruption.

5. Trains a machine:

An artificial neural network can comment on comparable situations. Consequently, they can learn from these experiences and make decisions.

6. Supports parallel processing:

Their ability to parallel process helps them to accomplish multiple tasks simultaneously. It is one of the prominent aspects that differentiates deep learning neural networks.

Machine Learning vs Neural Network: Key Differences

Let’s look at the core differences between Machine Learning vs Neural Networks.

1. Machine Learning uses advanced algorithms that parse data, learns from it, and use those learnings to discover meaningful patterns of interest. Whereas a Neural Network consists of an assortment of algorithms used in Machine Learning for data modelling using graphs of neurons.

2. While a Machine Learning model makes decisions according to what it has learned from the data, a Neural Network arranges algorithms in a fashion that it can make accurate decisions by itself. Thus, although Machine Learning models can learn from data, in the initial stages, they may require some human intervention.

Neural networks do not require human intervention as the nested layers within pass the data through hierarchies of various concepts, which eventually makes them capable of learning through their own errors.

3. As we mentioned earlier, Machine learning models can be categorized under two types – supervised and unsupervised learning models. However, Neural Networks can be classified into feed-forward, recurrent, convolutional, and modular Neural Networks.

4. An ML model works in a simple fashion – it is fed with data and learns from it. With time, the ML model becomes more mature and trained as it continually learns from the data. On the contrary, the structure of a Neural Network is quite complicated. In it, the data passes through several layers of interconnected nodes, wherein each node classifies the characteristics and information of the previous layer before passing the results on to other nodes in subsequent layers. 

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5. Since Machine Learning models are adaptive, they are continually evolving by learning through new sample data and experiences. Thus, the models can identify the patterns in the data. Here, data is the only input layer. However, even in a simple Neural Network model, there are multiple layers.

The first layer is the input layer, followed by a hidden layer, and then finally an output layer. Each layer contains one or more neurons. By increasing the number of hidden layers within a Neural Network model, you can increase its computational and problem-solving abilities.

6. Skills required for Machine Learning include programming, probability and statistics, Big Data and Hadoop, knowledge of ML frameworks, data structures, and algorithms. Neural networks demand skills like data modelling, Mathematics, Linear Algebra and Graph Theory, programming, and probability and statistics.

7. Machine Learning is applied in areas like healthcare, retail, e-commerce (recommendation engines), BFSI, self-driving cars, online video streaming, IoT, and transportation and logistics, to name a few. Neural Networks, on the other hand, are used to solve numerous business challenges, including sales forecasting, data validation, customer research, risk management, speech recognition, and character recognition, among other things.

Conclusion

These are some of the major differences between machine learning vs neural networks. Neural Networks are essentially a part of Deep Learning, which in turn is a subset of Machine Learning. So, Neural Networks are nothing but a highly advanced application of Machine Learning that is now finding applications in many fields of interest.

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