I know, there is a lot of information about Bayes theorem on the internet but in my opinion, the most effective way to learn is learning by doing. I am doing elements of AI second course building AI and I was struggling with probability part. So here is a short introduction of Bayes theorem.
Hopefully, you find it useful and clear enough to understand Bayes theorem better, at least it helped me!
Bayes theorem is used to calculate conditional probability. Conditional probability means that we calculate the probability of event B when another event A has already happened.
As you read on part 2 here, it was not clever to feed all the features inside the model and wait for the good results. Well, I was not clever and I did it but I think it was a necessary part of my learning curve. I realized that I need to see more effort with data processing if I want to train a better model.
There are many ways to processes the data and this is a post about how I did it.
As always I started with basic things as loading the packages, reading in the CSV-file, etc…
The next step was to build the model which predicts if the person survived or not. I did several models just to test out which model type is the best. Also, it was a great way to learn scikit-learn and Fast.ai packages
Model building was much harder than the initial data analysis part but during the model-building part, I learned several new things.
I created separate notebook for model building part so that’s why I started with basics…
My goal was to get a better understanding of how to work with tabular data so I challenged myself and started with the Titanic -project. I think this was an excellent way to learn the basics of data analysis with python.
You can find the competition here: https://www.kaggle.com/c/titanic
I really recommend you to try it yourself if you want to learn how to analyze the data and build machine learning models.
I started by uploading the packages:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Pandas is a great package for tabular…