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Duration 35 hours
Course Outline
Introduction to AI in Python
- Foundational concepts and the scope of AI
- Python libraries used for AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleaning, transformation, and feature engineering
- Managing missing and unbalanced data
- Feature scaling and encoding strategies
Supervised Learning Approaches
- Regression and classification algorithms
- Ensemble techniques: Random Forest, Gradient Boosting
- Hyperparameter tuning and cross-validation methods
Unsupervised Learning Approaches
- Clustering techniques: K-Means, DBSCAN, hierarchical clustering
- Dimensionality reduction methods: PCA, t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Getting started with TensorFlow and Keras
- Creating and training feedforward neural networks
- Enhancing neural network performance
Reinforcement Learning (Introductory)
- Core principles of agents, environments, and rewards
- Implementing basic reinforcement learning algorithms
- Use cases for reinforcement learning
Deployment of AI Models
- Persisting 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 fundamentals
- Familiarity with data analysis libraries such as NumPy and pandas
- Fundamental understanding of machine learning concepts and algorithms
Intended Audience
- Software developers looking to broaden their AI development capabilities
- Data analysts aiming to apply AI techniques to complex datasets
- R&D professionals developing AI-powered 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
