Merci d'avoir envoyé votre demande ! Un membre de notre équipe vous contactera sous peu.
Merci d'avoir envoyé votre réservation ! Un membre de notre équipe vous contactera sous peu.
Plan du cours
Computer Vision
Data Analysis and Visualization
Deep Learning and Neural Networks
Deployment and Scaling
Ethics and Future of AI
Introduction to AI and ML
Lab Project
Machine Learning Models
Natural Language Processing (NLP)
Summary and Next Steps
- AI application deployment strategies
- Scaling AI applications
- Monitoring and maintaining AI systems
- Developing a small-scale intelligent application
- Working with real-world datasets
- Collaborating on a group project to solve an industry-relevant problem
- Ethical considerations in AI
- AI policy and regulation
- Future trends in AI and ML
- Exploratory data analysis
- Data visualization techniques
- Statistical foundations for ML
- Fundamentals of neural networks
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- Image processing fundamentals
- Object detection and image classification
- Advanced topics in computer vision
- Overview of AI and ML concepts
- Data collection and preprocessing
- Introduction to Python for AI
- Supervised learning algorithms
- Unsupervised learning algorithms
- Model evaluation and selection
- Text processing and feature extraction
- Sentiment analysis and text classification
- Language models and chatbots
Pré requis
Audience
- AI professionals
- Software developers
- Data analysts
- An understanding of basic programming concepts
- Experience with Python and fundamental data science techniques
- Familiarity with core AI and ML principles
28 Heures