The ML Process Using scikit-learn
Learn scikit-learn to implement the ML pipeline for classifying the movie dataset.
In the previous lessons, we saw that slightly complicating the movie dataset makes our simple perceptron model unsuitable for the classification task. We also theoretically saw that introducing a hidden layer with two neurons solves the problem. However, as an ML engineer, how do you know if this actually works for our movie dataset? We need to test our new MLP model with a hidden layer by implementing it in Python. Let’s try writing the Python code to create the neural network that classifies “Good” from “Bad” movies.
We’ll use the terms multi-layer perceptron and neural network interchangeably from this point onwards.
Similar to the perceptron model, we will follow the complete ML pipeline for our MLP also. Let's revisit it once again.
The ML process
For any complex problem that requires the computer to be able to identify patterns, there is an ML process to solve it.
A Python library for ML
An MLP can classify data with non-linear decision boundaries as we have already seen. Let’s implement this model to classify the movie dataset using an MLP, but this time, we'll not code from scratch. Instead we’ll take help from a purpose-built Python