Supervised machine learning in Python

Supervised Machine Learning in Python

Online course

Models, algorithms and techniques of supervised machine learning


After we have explored our dataset and pre-processed it,  we are ready to apply a machine learning model. We often start trying several models and looking for the best one, but this is wrong because if we don’t know what we are doing, our models won’t work at all.

Each model has its own properties, that we must master correctly if we want it to work for us. It’s known that simple models are often to be preferred instead of complex models, because they can generalize better.

Moreover, the interpretability and explainability of a model are very important parts of a machine learning pipeline, because they make the model behavior clear and help us to extract information that is hidden inside data. Finally, measuring the performance of a model correctly and in an unbiased way is crucial for deploying a good model that can be used in a production environment.

So, we must master supervised machine learning models and techniques properly if we want to become professional data scientists.

Advantages of supervised machine learning

  • Several models

    Several models can make us work properly with our dataset.

  • Feature selection

    Some techniques can be used for feature selection according to our models

  • Model interpretation

    Several techniques can be used to interpret the results of a model (e.g. SHAP)

  • Performance evaluation

    We must know if our model performs well, so we have to learn how to select the best performance metric for our project.

My online course

In my online course, I outline all the main topics about Supervised Machine Learning. All the lessons will be covered with practical examples in Python programming language.

These are the topics of the course:

Linear, Lasso, Ridge, Elastic Net and Logistic regressions

The most common linear models, easy to apply and explain.

Decision trees

Some of the most used and powerful supervised models.

k-nearest neighbors (KNN)

A simple but powerful machine learning model based on distances.

Naive Bayes

A statistical model that’s very used in Natural Language Processing and other applications.

Support Vector Machines (SVM)

The most complete and complex machine model among those that are not based on neural networks.

Artificial Neural Networks

The state-of-the-art of machine learning models, although difficult to train.

Ensemble models, random forest and boosting

Ensemble models are sets of models that work together in order to make better predictions. These are bagging, boosting, voting and stacking. Among bagging models, random forest is the most common one. Among boosting, gradient boosting decision trees are very famous, particularly XGBoost implementation.

Performance evaluation (ROC curve, accuracy, precision…)

How can we ensure that our model works properly? There are several techniques for regression and classification.

Cross-validation and hyperparameter tuning

How can we tune the hyperparameters of a model? There are several techniques like grid search and random search, both based on cross-validation.

Feature importance

Knowing the importance of the features is necessary because it helps us explain the model and its results. That’s why I focus on those models that give use their own interpretation of feature importance and on SHAP technique, which is a model-free technique.

Recursive Feature Elimination (RFE)

Once we have calculated feature importance, it’s useful to use this information for reducing the dimensions of our datset. RFE can be used for this purpose. 

Python libraries 

Everything will be done using scikit-learn library, imblearn and matplotlib. 

What's inside the course?

The course contains:

10 hours of video lessons

Python code for each example

Discussion area to interact with the teacher and the students

Certificate of completion at the end


The course is excellent and very useful. All major subjects are complete and clear. A "must have" if you want to become a skilled data scientist


The teacher

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 in Artificial Intelligence category.

Frequently Asked Questions

Does the course have a start and a finish date?

No. Once you enroll, you can follow the recorded video lessons when you want.

How can I pay for the course?

You can pay by Paypal or Credit card.

How can I follow the lessons?

Once you pay for your enrollment, you can access the recorded video lessons of the course when you want from your computer using this website. These videos are given in streaming, so you’ll need to connect to this website and have an Internet connection in order to watch them. After you create your account and log in, you can use the My Courses link in top of every page to see all the courses you have enrolled in.

What language will be used?

During this course, the spoken and written language is English.

I can’t afford the whole price. What can I do?

You can subscribe to the school membership. By paying a monthly or yearly fee, you’ll access this and the other courses of the school.

What if I’m not satisfied?

If you are not satisfied with the course, we apply a 30-day refund policy. Just contact us within 30 days from the date of purchase to get a full refund.


You can join the course paying a one-time payment or joining the school membership.

30-day money back guarantee

If you are not satisfied with the course, we apply a 30-day refund policy. Just contact us within 30 days from the date of purchase to get a full refund.

One-time payment

$90 (+ VAT)
  • Complete access to the course lessons (10+ hours)
  • Access to the discussion area of each lesson
  • Python code for each example
  • 30-day refund policy

School membership

You can access all the courses of my school by subscribing to my school membership.

Go to the school membership

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