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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 defining workflows
Preparing Data for AI
- Data cleaning, transformation, and feature engineering
- Managing missing data and class imbalance
- Feature scaling and encoding strategies
Supervised Learning Methods
- Regression and classification algorithms
- Ensemble techniques: Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation
Unsupervised Learning Methods
- Clustering algorithms: K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction techniques: PCA and t-SNE
- Practical use cases for unsupervised learning
Neural Networks and Deep Learning
- Getting started with TensorFlow and Keras
- Building and training feedforward neural networks
- Strategies for optimizing neural network performance
Introduction to Reinforcement Learning
- Key concepts: agents, environments, and rewards
- Implementing fundamental reinforcement learning algorithms
- Real-world applications of reinforcement learning
Deploying AI Models
- 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
- Practical experience with data analysis libraries like NumPy and pandas
- Foundational knowledge of machine learning principles and algorithms
Target Audience
- Software developers looking to enhance their AI development capabilities
- Data analysts who want to apply AI techniques to complex datasets
- R&D professionals focused on developing AI-powered 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
