Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have become so deeply entwined in our day-to-day lives and so fast that we’ve become accustomed to them without even knowing their connotations. For most people, AI, ML, and DL are all the same. However, though these technologies are inter-related, they have innate differences.
Today, we’ll shed light on one such source of mass confusion – Machine Learning vs Neural Network.
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.
What is a Neural Network?
The structure of the human brain inspires a Neural Network. It is essentially a Machine Learning model (more precisely, Deep Learning) that is 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.
Machine Learning vs Neural Network: Key Differences
Let’s look at the core differences between Machine Learning and 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.
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.
These are some of the major differences between Machine Learning and 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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