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Duration 35 hours
Course Outline
Introduction to AI in Python
- Fundamental concepts and the scope of AI
- Python libraries used in AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleaning, transformation, and feature engineering
- Managing missing values and imbalanced datasets
- Techniques for feature scaling and encoding
Supervised Learning Methods
- Regression and classification algorithms
- Ensemble techniques including Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation strategies
Unsupervised Learning Methods
- Clustering approaches such as K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction techniques like PCA and t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Overview of TensorFlow and Keras
- Construction and training of feedforward neural networks
- Strategies for enhancing neural network performance
Introduction to Reinforcement Learning
- Core principles involving agents, environments, and rewards
- Implementation of foundational 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
Wrap-up and Future Directions
Requirements
- A strong grasp of fundamental Python programming concepts
- Proficiency with data analysis libraries such as NumPy and pandas
- A foundational understanding of machine learning concepts and algorithms
Target Audience
- Software developers looking to enhance their AI development capabilities
- Data analysts aiming to apply AI techniques to complex datasets
- R&D professionals constructing 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
