Formation Professionnelle et Certifiante
Aperçu du programme :
You will master the principles of machine learning in this approachable program, as well as how to apply these methods to create practical AI applications.
The program gives a comprehensive overview of contemporary machine learning, covering supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), as well as some of the best techniques for Artificial Intelligence and machine learning innovation used in Silicon Valley.
After the completion of this specialization, you will have mastered fundamental ideas and acquired the practical skills necessary to apply machine learning swiftly and effectively to difficult real-world issues. The new Machine Learning Specialization is the ideal place to start if you want to get into AI or develop a career in machine learning.
Détails du programme:
| Dates | Starts:TBA. 6-month program. 4 hours of study a week ( 2 independent online, 2 live sessions ) |
| Langue | Français et anglais |
| Format | – Access to quality online content from MUST & Coursera. – Practical labs offering hands-on sessions with a MUST Professor Continue independent FREE access to Coursera for up to 3 months and earn the Coursera Machine Learning Certificate from DeepLearning.AI / Stanford. |
| Dernier délai d’inscription | TBA. |
| Emplacement | MUST University, Lac3, Tunis. |
| Coût | 2800 TND |
Thèmes du programme :
1. Supervised Machine Learning: Regression and Classification
• Build machine learning models in Python using popular machine learning libraries.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
2. Advanced Learning Algorithms
• Build and train a neural network with TensorFlow to perform multi-class classification.
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
3. Unsupervised Learning, Recommenders, Reinforcement Learning
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.
Formateur(s) :
- TBD
Inscription :
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