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What should I be learning first: machine learning or neural networks? Could someone give a proper explanation for this?

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Neural Network consists of a variety of algorithms that aim to identify the hidden relationship between the data in a given set using systems that can process human-like tasks. In other terms, the neural network is a collection of organic or artificial neurons. It is adaptable to changes and the network creates the best result without having to make any changes in the output criteria. It is a set of models in the field of Machine Learning.

If you wish to learn Neural Networks then you can sign up for Machine Learning Training that has neural networks as part of its curriculum.

Now, if your main question is that is it better to get into neural networks without having a basic understanding of Machine Learning, then the answer is no. In order to understand neural networks and their techniques in-details, it is important that you have a generic, if not advanced, knowledge of Machine Learning concepts, benefits, limitations, techniques, and more. This knowledge will be beneficial while you learn Machine Learning as it will improve your understanding of neural networks and their working. So, to get in-depth knowledge of deep learning and neural networks, you need to have a clear understanding of Machine Learning which is why it is suggested that you learn the concepts side by side.

To start by getting a brief understanding of Machine Learning and Neural Networks, you can also take a look at this comprehensive video tutorial:

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