Delivered either online or onsite, these instructor-led live Data Science courses utilize hands-on practice to demonstrate how to extract knowledge from data in various forms.
Data Science training is offered as "online live training" or "onsite live training". Online live training (also known as "remote live training") is conducted through an interactive remote desktop. Onsite live training can be provided locally at customer premises in Nantes or within NobleProg corporate training centers in Nantes.
NobleProg -- Your Local Training Provider
Nantes, Zenith
NobleProg Nantes, 4 rue Edith Piaf, Saint-Herblain, france, 44821
In the Parc d'Ar Mor zone, near the Zénith.
Car : from the ring road, Porte de Chézine Exit> Boulevard du Zenith > Esplanade Georges Brassens (restaurants) > Rue Edith Piaf on the right. From the N444 road (Nantes > Lorient), Exit #1 > boulevard Marcel Paul > Rue Edith Piaf at the right.
Parking Zénith P1 (free). Once parked, you can recognize the building: it's one of the tree bulding with zinc frontage.
Bicycle: free indoor parking
Public transport :
Tramway R1, Schoelcher station + 10 mn by foot through commercial center Atlantis
Tramway R1, François Mitterrand stop + bus 50, stop at Saulzaie station or bus 71, stop at the Zénith station
Tramway R3, Marcel Paul station + bus 50, Saulzaie station
Chronobus C6, Hermeland station+ bus 71, Zénith station
Bus : lignes 50 (Saulzaie station) or 71 (Zénith station)
This instructor-led, live training in Nantes (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.
By the end of this training, participants will be able to:
Understand the concept and benefits of pre-trained models.
Explore various pre-trained model architectures and their use cases.
Fine-tune a pre-trained model for specific tasks.
Implement pre-trained models in simple machine learning projects.
This instructor-led, live training in Nantes (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.
By the end of this training, participants will be able to:
Set up a data science environment in AWS Cloud9.
Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
Utilize AWS Cloud9 for machine learning model development and deployment.
Optimize cloud-based workflows for data analysis and processing.
This instructor-led live training, held in Nantes (online or onsite), is designed for intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.
By the end of this training, participants will be able to:
Set up Apache Airflow for machine learning workflow orchestration.
Automate data preprocessing, model training, and validation tasks.
Integrate Airflow with machine learning frameworks and tools.
Deploy machine learning models using automated pipelines.
Monitor and optimize machine learning workflows in production.
This instructor-led live training in Nantes (online or onsite) targets beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.
By the end of this training, participants will be able to:
This 35-hour instructor-led course in Nantes guides participants in using Python to create practical financial applications. Through a hands-on methodology that blends lectures with rigorous practical exercises, it addresses critical areas such as data analysis, asset allocation, and risk management.
This 35-hour program on Nantes focuses on practical Data Science and AI applications using Python. Participants will master CRISP-DM workflows, machine learning with TensorFlow, NLP, and Big Data processing with Spark. It is an excellent choice for beginners aiming to build career-ready analytical skills and earn Python data science certification for business environments.
This training, delivered by an instructor in Montpellier or remotely (online or onsite), is intended for beginners and/or intermediate learners who wish to acquire a solid foundation in Object-Oriented Python, software quality, and best practices.
By the end of the training, participants will be able to:
Write clean, structured Python code
Design object-oriented architecture
Apply SOLID principles and best practices
Implement robust unit tests
Establish a comprehensive software quality process
This instructor-led training in Nantes introduces KNIME Analytics Platform for data-driven innovation. Participants will learn to build data science scenarios, train and validate models, and implement end-to-end data value chains through hands-on labs and practical exercises.
This instructor-led, live training in Nantes (online or onsite) targets intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.
By the end of this course, participants will be able to:
Understand and differentiate key machine learning paradigms.
Explore data preprocessing techniques and model evaluation metrics.
Apply machine learning algorithms to solve real-world data problems.
Use Python libraries and Jupyter notebooks for hands-on development.
Build models for prediction, classification, recommendation, and clustering.
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Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
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