Machine Learning with Python Training Course
The training guides participants through a comprehensive process of building machine learning models in Python using the scikit-learn library. The program covers both regression and classification algorithms, with an emphasis on practical application.
Participants will learn key aspects of working with data—from feature engineering, normalization, and standardization to handling missing values and encoding categorical variables. They will explore methods for detecting and eliminating outliers as well as techniques for selecting the most important attributes. Special attention is given to the problem of overfitting and methods to prevent it, including cross-validation and regularization.
In the algorithms section, participants will implement a wide range of models—from simple linear regression through decision trees, SVMs, and KNN to advanced ensemble methods such as Random Forest and Gradient Boosting. Each algorithm is discussed from a practical application perspective, along with appropriate evaluation metrics.
The program also includes model optimization through hyperparameter tuning using grid search and building pipelines that automate the data processing workflow. Upon completing the training, participants will be able to independently prepare data, choose an appropriate algorithm, train a model, and evaluate its quality using relevant metrics.
Course Outline
Module I: Regression Models
1. Basics of regression using the linear model example
2. Optimization using the least squares method
3. Practical implementation using scikit-learn
4. Measures of regression model quality
5. Overview of other regression methods
Module II: Data Preparation for Modeling
1. Feature engineering
2. Scaling and standardizing variables
3. Identifying and eliminating outliers
4. Strategies for imputing missing values
5. Dimensionality reduction and feature selection methods
6. Encoding categorical variables (one-hot encoding, label encoding)
Module III: The Problem of Overfitting
1. The phenomenon of overfitting and its consequences
2. Techniques to prevent overfitting
3. Cross-validation as a tool for model evaluation
4. Regularization in machine learning models
Module IV: Optimization of the Learning Process
1. Hyperparameter tuning using grid search
2. Building data processing pipelines
Module V: Classification Algorithms
1. Introduction to classification using logistic regression
2. Comparison of linear and non-linear models
3. Evaluation metrics for classifiers
4. Decision tree algorithm
5. Naive Bayes classifier
6. Support Vector Machine (SVM)
7. k-Nearest Neighbors (KNN) method
8. Multiclass classification issues
9. Ensemble methods—Random Forest and Gradient Boosting
Open Training Courses require 5+ participants.
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Testimonials (1)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
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