Summary
In this chapter, we discussed various topics related to machine learning and neural networks. We explained how to read data from an Excel file using the pandas library and prepare the dataset for training a machine learning model. We explored the use of decision tree classifiers and demonstrated how to train a decision tree model using scikit-learn. We also showed how to make predictions using the trained model.
Then, we discussed how to switch from a decision tree classifier to a random forest classifier, which is an ensemble of decision trees. We explained the necessary code modifications and provided an example. Next, we shifted our focus to using a dense neural network in PyTorch. We described the process of creating the neural network architecture, training the model, and making predictions using the trained model.
Lastly, we explained the steps involved in training a dense neural network, including data preparation, model architecture, initializing the model, defining...
