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Which task is not suitable for machine learning?
finding shortest path between a pair of nodes in a graph. predicting if stock price will rise or fall predicting the price of petroleum. group in mails as spam or non spam.
In which field machine learning is applicable?
Machine Learning is built on the field of Mathematics and Computer Science. Specifically, machine learning methods are best described using linear and matrix algebra and their behaviours are best understood using the tools of probability and statistics.
Which of the following is not a machine learning algorithm?
Which of the following is not a machine learning algorithm? Explanation: SVM stands for scalable vector machine. 5.
What fields use deep learning?
Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug …
How difficult is learning machine learning?
Learning how to use machine learning isn’t any harder than learning any other set of libraries for a programmer. The key is to focus on USING it, not designing the algorithm. Look at it this way: if you need to sort data, you don’t invent a sort algorithm, you pick an appropriate algorithm and use it right.
Does your machine learning algorithm not want to learn?
If you work with data in general, and machine learning algorithms in particular, you might be familiar with that feeling of frustration when a model really does not want to learn the task at hand. You have tried it all, but the accuracy metric just won’t rise.
Why are my machine learning models failing?
You had the data but the quality of the data was not up to scratch. In the same way that having a lack of good features can cause your algorithm to perform poorly, having a lack of good ground truth data can also limit the capabilities of your model.
What is machine learning (ML)?
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across.
What are the limitations of neural networks in machine learning?
There are multiple researchers looking at adding physical constraints to neural networks and other algorithms so that they can be used for purposes such as this. This is the most obvious limitation. If you feed a model poorly, then it will only give you poor results. This can manifest itself in two ways: lack of data, and lack of good data.