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Duration 35 hours
Course Outline
Introduction to AI in Python
- Essential concepts and the scope of AI
- Python libraries for AI development
- AI project architecture and workflow
Data Preparation for AI
- Data cleaning, transformation, and feature engineering
- Managing missing and imbalanced data
- Feature scaling and encoding techniques
Supervised Learning Techniques
- Regression and classification algorithms
- Ensemble methods: Random Forest, Gradient Boosting
- Hyperparameter tuning and cross-validation
Unsupervised Learning Techniques
- Clustering approaches: K-Means, DBSCAN, hierarchical clustering
- Dimensionality reduction: PCA, t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Overview of TensorFlow and Keras
- Constructing and training feedforward neural networks
- Enhancing neural network performance
Reinforcement Learning (Introduction)
- Key concepts: agents, environments, and rewards
- Implementing fundamental reinforcement learning algorithms
- Use cases for reinforcement learning
Deploying AI Models
- Storing and retrieving trained models
- Connecting models to applications via APIs
- Monitoring and maintaining AI systems in production
Conclusion and Future Directions
Requirements
- A strong grasp of Python programming basics
- Familiarity with data analysis tools like NumPy and pandas
- Fundamental understanding of machine learning principles and algorithms
Target Audience
- Software developers looking to enhance their AI development capabilities
- Data analysts aiming to apply AI methods to complex data sets
- R&D professionals creating AI-driven applications
Testimonials (2)
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
