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Duration 35 hours
Course Outline
Fundamentals of AI in Python
- Essential concepts and the scope of AI
- Python libraries tailored for AI development
- Structuring AI projects and establishing workflows
Preparing Data for AI
- Data cleansing, transformation, and feature engineering
- Addressing missing and imbalanced datasets
- Feature scaling and encoding techniques
Supervised Learning Approaches
- Regression and classification algorithms
- Ensemble methods: Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation strategies
Unsupervised Learning Approaches
- Clustering techniques: K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction: PCA and 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
Reinforcement Learning (Introduction)
- Core principles of agents, environments, and reward systems
- Implementing foundational reinforcement learning algorithms
- Use cases for reinforcement learning
Deploying AI Models
- Persisting and retrieving trained models
- Integrating models into applications via APIs
- Monitoring and maintaining AI systems in a production setting
Conclusion and Future Directions
Requirements
- A strong grasp of core Python programming principles
- Proficiency with data analysis tools such as NumPy and pandas
- Familiarity with fundamental machine learning concepts and algorithms
Target Audience
- Software engineers looking to broaden their AI development capabilities
- Data analysts aiming to utilize AI methodologies on complex datasets
- R&D specialists focused on 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
