It is an element of artificial intelligence, which is widely mentioned as the calibre of a machine to imitate intelligent human behaviour. Artificial intelligence is used to perform tasks that are tedious and complex, and that is quite like how human solves them. The main objective of artificial intelligence is to make computer algorithms that exhibit behaviour like a human being. Machine learning can be seen as a visual scene that understands a written text in natural language. Machine learning is a method to use artificial intelligence. The AI pioneer Arthur Samuel had discovered machine learning in the 1950s.
Machine learning is no matter one of the fastest and most emerging sectors in the world right now. It is taking human behaviour and effort science to the next level that nobody has ever imagined. We have been noticing many industry trends and insights that have been taken into consideration in the industry of machine learning. Huge investments and huge career options are taking place in this sector every day. While we talk about the investments, it is so huge that they cannot be anticipated. But if you ask us about the machine learning assignment, we will say- it is the most complex subject for students who lack subject understanding Machine learning. Are you exploring different options of assignment writers, but all of them look expensive? You don’t have to panic anymore.
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Artificial neural network-
The artificial neural network is a biologically influenced programming that activates a computer to learn from observative data collection. Artificial neural network design is inspired by the human’s brain biological neural network. It leads to a process of learning that is more capable than the standard machine learning representations. It consists of output and input layers, along with some hidden layers that consist of units that transform the input that the output layers can use.
It is a machine learning algorithm that is based on supervised learning. It completes a regression task. This model is a target prediction hierarchy whose value is based on independent variables. It is used to find out the relationship between forecasting and variables. An example of it is- the cost function linear regression that is Root Mean Squared Error (RMSE) between predicted y value (pred) and actual y value (y).
It is known as a supervised machine learning algorithm that can be used for classification issues. It is a classified algorithm used to assign observation to a discrete class. Some examples of classification problems- Email spam transaction fraud online.
Types of logistic regression:
K-Nearest Neighbours (K-NN)
It is a simple, easy-to-implement machine learning algorithm that is used to solve both regression problems and classification. This algorithm anticipates that similar things evolve in more proximity. In different terms, similar things are close to each other. KNN works by searching the distance between a query and examples in the data and selecting the specified examples (K) closest to the query. Then it votes for the frequent label during the classification.
Random forest is a worldwide machine learning technique that is taken into use for both classification and regression purposes. It has a vast number of specific decision trees that operate as an ensemble. Every individual decision tree in the random forest spits out a predictive class with the most votes that become our model’s prediction. Usually, the random forest model is not supposed to overfit, and if it does, it is customary to stop it from overfitting.
Explain ‘training set’ and ‘test set’ in the model of machine learning?
Generally, there are three steps process followed to create a model
1- Train the model
2- Test the model
3- Deploy the model
The training set is an example given to the model to analyze and learn. 70% data is taken as the training dataset that is labelled data used to train the model. At the same time, the test set can be used to test the proficiency of the hypothesis generated by the model. The remaining 30% is taken as a testing dataset. We experiment and test without labelled data and then verify results with tags.
If hypothetically you have labelled data of 1000 records, one way to train models is to be exposing all 1000 records during the process. Later you can take a small set of similar data to find the model that gives excellent results in this case.
This is not the right way of testing. We set aside a part of that data known as ‘test set’ before starting the process. The remaining data is known as ‘training data’ that we use to train the model.
Explain what deep learning is?
Deep learning is a sub-part of machine learning that contains systems that learn and think like humans who use artificial neural networks. The term ‘deep’ has been taken from the fact that you can have various layers of neural networks. The key difference between machine learning and deep learning is- feature engineering is done manually when it comes to machine learning. While in deep learning, the model consisting of neural networks automatically find out which features should be taken into consideration.
Explain how you can design an Email Spam Filter?
Building a spam filter involves various steps-
1) Data sources-
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3) Punctual submission-
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4) Do you give us any tips to write a machine learning assignment?
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