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Duration 35 hours
Course Outline
Introduction to AI in Python
- Core concepts and the scope of AI
- Python libraries used for AI development
- Structuring AI projects and workflows
Data Preparation for AI
- Data cleaning, transformation, and feature engineering
- Addressing missing and unbalanced data
- Feature scaling and encoding
Supervised Learning Methods
- Regression and classification algorithms
- Ensemble techniques: Random Forest, Gradient Boosting
- Hyperparameter tuning and cross-validation
Unsupervised Learning Methods
- Clustering approaches: K-Means, DBSCAN, hierarchical clustering
- Dimensionality reduction: PCA, t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Getting started with TensorFlow and Keras
- Constructing and training feedforward neural networks
- Enhancing neural network performance
Introduction to Reinforcement Learning
- Fundamental concepts 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
Wrap-up and Future Directions
Requirements
- A strong grasp of Python programming fundamentals
- Proficiency with data analysis libraries such as NumPy and pandas
- Familiarity with basic machine learning concepts and algorithms
Audience
- Software developers looking to enhance their AI development capabilities
- Data analysts eager to apply AI techniques to complex datasets
- R&D professionals developing 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
