This course enables you to acquire practical machine learning skills. It helps you become familiar with the main techniques while emphasizing best practices. The course also includes hands-on practice in Python for each technique covered.
Anyone wishing to acquire operational skills in machine learning.
Prerequisites:
Knowledge of Python.
Elements of statistics and linear algebra.
Acquire the fundamental concepts of machine learning.
Acquire operational skills to lead a business project involving machine learning.
Become familiar with best practices in machine learning and data analysis.
Introduction to machine learning
- Fundamentals of artificial intelligence
- Principles of machine learning and learning typologies
- Classification and regression
Linear models
- Linear regression
- Diagnostic techniques
- Overfitting and regularization
- Lasso and Ridge regressions
- Logistic regression
- Practical application with Python
Non-linear machine learning models
- Decision trees
- Random Forests
- Gradient Boosting
- Support Vector Machines (SVM)
- K-nearest neighbors (KNN)
- K-means
- Principal Component Analysis (PCA)
- Practical application with Python
Model selection
- Selection techniques for linear models
- Cross-validation
- Best practices
- Practical application with Python
Introduction to deep learning
- Principles of deep learning
- Neural networks
- Training classical neural networks
- Neural network typologies (CNN, RNN, GNN, etc.)
- Modern deep learning
- Practical application with Python
Machine learning project
The training includes a theoretical part and a practical application with Python for each technique covered.
Business use cases.
Certificate of attendance provided.
Address: Color Business Center, 19 rue de l’industrie L-6089 Bertrange
For in-house training, please contact us.
