Supervised Machine Learning in Python
What you'll learn
- Regression and classification models
- Linear models
- Decision trees
- Naive Bayes
- k-nearest neighbors
- Support Vector Machines
- Neural networks
- Random Forest
- Gradient Boosting
- Performance metrics (RMSE, MAPE, Accuracy, Precision, ROC Curve...)
- Feature importance
- Recursive Feature Elimination
- Hyperparameter tuning
- Python porgramming language
- Data pre-processing techniques
In this practical course, we are going to focus on supervised machine learning and how to apply it in Python programming language.
Supervised machine learning is a branch of artificial intelligence whose goal is to create predictive models starting from a dataset. With the proper optimization of the models, it is possible to create mathematical representations of our data in order to extract the information that is hidden inside our database and use it for making inferences and predictions.
A very powerful use of supervised machine learning is the calculation of feature importance, which makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common approach for calculating feature importance is the SHAP technique.
Finally, the proper optimization of a model is possible using some hyperparameter tuning techniques that make use of cross-validation.
With this course, you are going to learn:
What supervised machine learning is
What overfitting and underfitting are and how to avoid them
The difference between regression and classification models
Elastic Net regression
Support Vector Machines
Feedforward neural networks
Bagging and Random Forest
Boosting and Gradient Boosting
Root Mean Squared Error
Mean Absolute Error
Mean Absolute Percentage Error
Accuracy and balanced accuracy
ROC Curve and the area under it
How to calculate feature importance according to a model
SHAP technique for calculating feature importance according to every model
Recursive Feature Elimination for dimensionality reduction
All the lessons of this course start with a brief introduction and end with a practical example in 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 Jupyter notebooks are downloadable.
Who this course is for:
- Python developers
- Data Scientists
- Computer engineers
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.