Data pre-processing for Machine Learning in Python
What you'll learn
- How to fill the missings in numerical and categorical variables
- How to encode the categorical variables
- How to transform the numerical variables
- How to scale the numerical variables
- Principal Component Analysis and how to use it
- How to apply oversampling using SMOTE
- How to use several useful objects in scikit-learn library
- Basic knowledge of Python programming language
In this course, we are going to focus on pre-processing techniques for machine learning.
Pre-processing is the set of manipulations that transform a raw dataset to make it used by a machine learning model. It is necessary for making our data suitable for some machine learning models, to reduce the dimensionality, to better identify the relevant data, and to increase model performance. It's the most important part of a machine learning pipeline and it's strongly able to affect the success of a project. In fact, if we don't feed a machine learning model with the correctly shaped data, it won't work at all.
Sometimes, aspiring Data Scientists start studying neural networks and other complex models and forget to study how to manipulate a dataset in order to make it used by their algorithms. So, they fail in creating good models and only at the end they realize that good pre-processing would make them save a lot of time and increase the performance of their algorithms. So, handling pre-processing techniques is a very important skill. That's why I have created an entire course that focuses only on data pre-processing.
With this course, you are going to learn:
Encoding of the categorical variables
Transformation of the numerical features
Scikit-learn Pipeline and ColumnTransformer objects
Scaling of the numerical features
Principal Component Analysis
Filter-based feature selection
Oversampling using SMOTE
All the examples will be given using Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the sections of this course end with some practical exercises and the Jupyter notebooks are all downloadable.
Who this course is for:
- Python developers
- Aspiring data scientists
- People interested in machine learning and artificial intelligence
My name is Gianluca Malato, I'm Italian and have a Master's Degree cum laude in Theoretical Physics of disordered systems at "La Sapienza" University of Rome.
I'm a Data Scientist who has been working for years in the banking and insurance sector. I have extensive experience in software programming and project management and I have been dealing with data analysis and machine learning in the corporate environment for several years.
I am also skilled in data analysis (e.g. relational databases and SQL language), numerical algorithms (e.g. ODE integration, optimization algorithtms) and simulation (e.g. Monte Carlo techniques).
I've written many articles about Machine Learning, R and Python and I've been a Top Writer on Medium in Artificial Intelligence category.