Here you can find my online courses about Data Science and Machine Learning.
The purpose of these courses is to give a complete overview of a data science project and help the students become professional and skilled data scientists.
All the courses are practical and contain several examples in Python programming language and they are sold with a 30-day money-back guarantee.
Before applying any algorithm, we need to transform our dataset to make the information easily accessible by a model. Pre-processing is a set of techniques that help us transform a dataset in several ways to make our models work better.
All the most common models in supervised machine learning with practical examples in Python. Then, the performance metrics used to measure the performance of a model. Finally, feature importance calculation using several techniques (e.g. SHAP) and dimensionaity reduction using Recursive Feature Elimination.
How to calculate the importance of the features, how to interpret the model results and how to reduce the dimensionality of our dataset by removing the useless features.