Supervised Machine Learning in Python
Date: 27 January 2026 | Posted by: AD-TEAM
Supervised Machine Learning in Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 79 lectures (10h 59m) | Size: 3.36 GB
A practical course about supervised machine learning using Python programming language
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
XGBoost
Voting
Stacking
Performance metrics (RMSE, MAPE, Accuracy, Precision, ROC Curve.)
Feature importance
SHAP
Recursive Feature Elimination
Hyperparameter tuning
Cross-validation
Requirements
Python porgramming language
Data pre-processing techniques
Description
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
Linear models
Linear regression
Lasso regression
Ridge regression
Elastic Net regression
Logistic regression
Decision trees
Naive Bayes
K-nearest neighbors
Support Vector Machines
Linear SVM
Non-linear SVM
Feedforward neural networks
Ensemble models
Bias-variance tradeoff
Bagging and Random Forest
Boosting and Gradient Boosting
Voting
Stacking
Performance metrics
Regression
Root Mean Squared Error
Mean Absolute Error
Mean Absolute Percentage Error
Classification
Confusion matrix
Accuracy and balanced accuracy
Precision
Recall
ROC Curve and the area under it
Multi-class metrics
Feature importance
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
Hyperparameter tuning
k-fold cross-validation
Grid search
Random search
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
Researchers
Students
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