Artificial Intelligence, Machine Learning, and Deep Learning have all been interlinked to each other in our day-to-day lives and we have all become accustomed to them without even knowing what they actually are, how they work and what their connotations are. Most of them think that AL, ML, and DL are all one and the same. However, these technologies are inter-related, but they have their own internal and technical differences.
Today, we will shed light on some such sourced of mass confusion that these terms such as Machine Learning and Neural Networks are creating.
So now what is Machine Learning?
As we already know that Machine learning is a part which falls under the larger canvas of Artificial Intelligence. Machine Learning is that part which seeks assistance to build artificial systems or machine that can automatically learn and train themselves through their experience, without actually being programmed or requiring any human assistance.
In that sense, we can make a strong note that machine learning is a continuously evolving activity. This so-called machine learning aims to understand the data structure of the dataset from various other source and accommodate the data into ML models using various algorithms that are used by companies and organisations.
There are two core ML methods, they are:
Supervised Machine Learning
Unsupervised Machine Learning
We will not get in detail about these two types of Machine Learning in this blog hence we will move ahead and know other important things.
What are Neural Networks?
Neural networks and human nervous system are both related. It is said that the structure of the human brain inspires a Neural Network. It is essentially a part of machine learning’s unsupervised learning and to be more precise, it is a part of deep learning.
Neural Networks has something called as nodes, which is a web of interconnected entities wherein each node is important for a simple computation and hence it is said that neural network functions is similar to the neutrons in the human brain.
Key differences between Machine learning and Neural Network
We will now look into some of the core differences between Machine Learning and Neural Networks.
Machine learning always uses an advanced algorithm that collects data, learns from it and use those learnings to discover meaningful patterns of interest. On the other hand, Neural Networks consists of a mixture of algorithms that are used in the machine learning for data modelling using the graphs of neurons.
A Machine learning model always makes decisions based on the learnings from the data, and a neural network arranges various algorithms in a manner that can take accurate decision decisions on its own. Hence, even though machine learning models can learn from data, during the initial stages, they still might require some human assistance. There’s no obligations with regards to the Neural Networks that it should compulsorily have some human assistance because it has the nested layers within itself which passes the data through set hierarchies of various concepts which will eventually learn from its mistake and make decision on its own with the actual human intervention.
Like we already saw, Machine learning has two models I.e., is the supervised and the unsupervised learning models. However Neural Networks has something called as the feed-forward, recurrent, convolutional and modular Neural Networks.
Machine learning models work in a simple fashion - it is fed with data and learns from it. With the passage of time, the ML model becomes more mature and trained as it continually learns from the data. On the other hand, the structure of a Neural Network is quite complicated. It is said so because the data passes through several layers of interconnected nodes and each of these nodes classifies the characteristics and information of the previous layer before actually giving out the results on to the other nodes in a subsequent layer.
Machine Learning models are adaptive in nature and this is a known fact, hence they are continually evolving by learning through new sample data and experiences and with this, the models can identify the patterns in the data. Here, data is referred to the input layer. However, a simple Neural Network model has multiple layers in it. The first layer is called as the input layer, followed by a hidden layer and then finally an output layer. Each of these layers contains one or more neurons. With the increase in the number of hidden layers within a Neural Network model, you can increase the computational and problem-solving abilities.
Any field, and any work, have specific required skills and knowledge that are to be known, in the same way, skill required for Machine Learning are programming knowledge, probability and statistics, Big Data and Hadoop, knowledge of ML frameworks, data structures and algorithms. Similarly, Neural Networks too has separate set of required skills, they are: data modelling, Mathematics, Linear Algebra and Graph Theory, programming and probability and statistic are required too.
Machine Learning is applied in the areas like healthcare, retail, e-commerce, BFSI, self-driving cars, online video streaming, IoT, and Transportation and logistics too, these are some of them. Neural Networks on the other hands, are used in various different fields like: 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 is again a subset of Machine learning and this Machine Learning is again a part of Artificial Intelligence. So, Neural Networks are nothing but a highly advanced application of Machine Learning that is now finding applications in many fields of interest.
With the above-mentioned differences, it is clear that Machine Learning and Neural Networks are both important in its own way and it is also clear that both these along with other models and related concepts are the future. There’s a high demand in this field. Like I already mentioned, these Machine Learning and Neural Networks are the subset of a broader term called the Artificial Intelligence and hence it is always said the AI is the future!
Why Infimind for AI:
If you’re looking for a training programme in Artificial Intelligence, we at Infimind Institute provide training which slated for 6months. We have the top trainers who are industry experts in this field. Ask why Infimind?
Infimind ensures a good student faculty ratio for one-on-one attention.
We create a positive learning environment.
Lifetime access of course content.
Curriculum designed to suit the demand of the industry.
Highly-skilled faculty with excellent industry experience.
We provide you industry-based projects.
250+ hours of practical assignments and projects.
Hand holding, as and when required, both by the Faculty and Infimind Institute.
We teach you both R and Python language.
Advantages of AI & ML:
Replace humans in mundane, repetitive, and tedious jobs.
Reduction in human errors if coded properly.
Conversion of large data into knowledge.
Prediction made easier with AI.
Solve new problems with ease.