Course : Machine learning, from POC to production in PythonPython for data science
Practical course - 3d
- 21h00 - Ref. PYD
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 | Setting up the various preprocessing steps with Python |
| Choosing the right model for a given problem |
| Apply and evaluate models on real data |
| Make a model available in the cloud and enable it to be queried via the API |
Intended audience
Anyone interested in learning Python and its application to data science and machine learning.
Prerequisites
Knowledge of the Python language. Theoretical knowledge of machine learning.
Practical details
Hands-on work
The training emphasizes practical application, to ensure participants' autonomy.
Teaching methods
Designed by experts on the basis of their feedback, this course reviews the different stages of a Machine Learning project, from conception to production.
Course schedule
1 Data import and preprocessing
- Python / Anaconda / Jupyter Notebook development environment.
- Pandas: analysis of tabular data (CSV, Excel...), statistics, pivots, joins, filters.
- Handling missing values: imputation by mean, median, interpolation, knn...
- Outlier processing: graphical analysis, IQR method, Z-score.
- Standardization.
- Standardization: Skewness and Kurtosis.
- Unbalanced data: Undersampling, Oversampling, SMOTE.
Hands-on work
Handling Python in a Jupyter notebook. Practical exercise with pandas.
Implementation of all pre-processing using specific Python libraries.
2 Model training and evaluation
- The most common supervised and unsupervised learning models.
- Model training with Scikit-learn.
- Evaluation methods: choosing the right metrics for each problem.
Hands-on work
Train several supervised and unsupervised models, compare performance and select the best model.
3 Model optimization and performance logging
- Presentation of the Optuna and Hyperopt libraries.
- Presentation of the Grid Search approach for identifying the best hyper-parameters in a model.
- Log hyper-parameters and performance in Mlflow.
Hands-on work
Optimization of the models developed in the previous section and logging of metrics/hyperparameters in Mlflow.
4 Model and Data Drift
- Interest in checking the Drift model and Data Drift.
- Introducing the Evidently and Streamlit libraries.
Hands-on work
Implementation of an Evidently Dashboard to monitor data drift.
5 Industrialization: deployment in the cloud
- Introduction to the AWS EC2 service.
- Introducing Flask for the for making a machine learning model available via an API.
- Presentation of various tools for connecting to the virtual environment, such as Putty, Visual Studio Code...
- Code deployment via GitHub.
Hands-on work
Deploying a model on a cloud environment with the Flask library.
Customer reviews
3,9 / 5
Customer reviews are based on end-of-course evaluations. The score is calculated from all evaluations within the past year. Only reviews with a textual comment are displayed.
CHRISTOPHE S.
18/05/26
3 / 5
pour les exercices, trop de typos/énoncés incomplets ou pas clairs (plusieurs fois on ne sait pas sur quel fichier de données il faut travailler). Ensuite la formation est sur le machine learning, pas apprendre à manipuler des dataframes ou faire des regex sur des strings, ça c’est un pré requis. Du coup il y a trop de python/pandas qui devrait être un acquis (le premier jour y est consacré à 100%) et pas assez sur la partie ML/sklearn qu’on a survolé avant de passer à Flask/Streamlit.
IBRAHIM A.
18/05/26
4 / 5
Le contenu de cette formation était plutôt intéressant et expliquait bien comment un projet passe d’un POC à la production. Le formateur est pleinement qualifié. Une meilleure organisation entre la théorie et la pratique serait bénéfique pour cette formation.
ALEXIS P.
18/05/26
3 / 5
Trop de sujets vus trop rapidement. Pédagogie à revoir pour l’encadrent. Malgré des sujets difficiles la présentation ne permet pas de comprendre même en surface les impacts et la pertinence de chaque module. A savoir que j’ai toutes les connaissances techniques nécessaire mais pas la pratique industrielle dans la mise en place et pour autant je me suis confronté à des complexité de compréhension des choses à retenir et parfois même de la pertinence de chaque partie.
Publication date : 02/23/2024
PARTICIPANTS
Anyone interested in learning Python and its application to data science and machine learning.
PREREQUISITES
Knowledge of the Python language. Theoretical knowledge of machine learning.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Anyone interested in learning Python and its application to data science and machine learning.
PREREQUISITES
Knowledge of the Python language. Theoretical knowledge of machine learning.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Dates and locations
Last places available
Guaranteed date, in person or remotely
Guaranteed session