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Duration 35 hours
Course Outline
Introduction to AI in Python
- Core concepts and the scope of AI
- Essential Python libraries for AI development
- Structuring AI projects and establishing workflows
Preparing Data for AI
- Data cleansing, transformation, and feature engineering
- Strategies for handling missing and unbalanced data
- Techniques for feature scaling and encoding
Supervised Learning Approaches
- Algorithms for regression and classification
- Ensemble methods: Random Forest, Gradient Boosting
- Hyperparameter tuning and cross-validation techniques
Unsupervised Learning Approaches
- Clustering methods: K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction techniques: PCA, t-SNE
- Practical use cases for 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: agents, environments, and rewards
- Implementing basic reinforcement learning algorithms
- Real-world applications of reinforcement learning
Deploying AI Models
- Techniques for saving and loading trained models
- Integrating models into applications via APIs
- Monitoring and maintaining AI systems in production environments
Conclusion and Future Directions
Requirements
- A solid grasp of Python programming fundamentals
- Practical experience with data analysis libraries such as NumPy and pandas
- Foundational knowledge of machine learning concepts and algorithms
Target 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-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
