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Duration 35 hours (5 days)
Course Outline
Introduction to AI in Python
- Foundational concepts and the scope of AI
- Essential Python libraries for AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleansing, transformation, and feature engineering
- Strategies for handling missing values and imbalanced datasets
- Techniques for feature scaling and encoding
Supervised Learning Approaches
- Algorithms for regression and classification tasks
- Ensemble techniques including Random Forest and Gradient Boosting
- Hyperparameter optimization and cross-validation strategies
Unsupervised Learning Approaches
- Clustering algorithms 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 frameworks
- Constructing and training feedforward neural networks
- Methods for enhancing neural network performance
Reinforcement Learning Fundamentals
- Core principles of agents, environments, and reward systems
- Implementing basic reinforcement learning algorithms
- Real-world applications of reinforcement learning
Deploying AI Models
- Processes for saving and loading trained models
- Integrating models into applications through APIs
- Monitoring and maintaining AI systems in production environments
Conclusion and Future Directions
Requirements
- Strong command of fundamental Python programming concepts
- Proficiency with data analysis tools such as NumPy and pandas
- Familiarity with basic machine learning concepts and algorithms
Target Audience
- Software developers seeking to enhance their AI engineering capabilities
- Data analysts looking to apply AI methods to complex data sets
- 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
