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Duration 35 hours
Course Outline
Introduction to AI in Python
- Fundamental concepts and the scope of AI
- Essential Python libraries for AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleaning, transformation, and feature engineering
- Managing missing values and unbalanced datasets
- Techniques for feature scaling and encoding
Supervised Learning Approaches
- Algorithms for regression and classification
- Ensemble methods including Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation strategies
Unsupervised Learning Approaches
- Clustering techniques: K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction methods: PCA and 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
- Strategies for optimizing neural network performance
Reinforcement Learning Basics
- Core principles: agents, environments, and reward mechanisms
- Implementing foundational reinforcement learning algorithms
- Real-world applications of reinforcement learning
AI Model Deployment
- Processes for saving and loading trained models
- Integrating models into applications through APIs
- Monitoring and maintaining AI systems in production environments
Recap and Future Directions
Requirements
- A strong grasp of fundamental Python programming concepts
- Practical experience with data analysis libraries like NumPy and pandas
- Familiarity with basic machine learning theories and algorithms
Target Audience
- Software developers looking to enhance their AI development capabilities
- Data analysts eager to apply AI techniques to complex datasets
- R&D professionals 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
