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Duration 35 hours
Course Outline
Introduction to AI in Python
- Essential concepts and the scope of AI
- Python libraries used for AI development
- Structuring AI projects and establishing workflows
Data Preparation for AI
- Cleaning data, transforming inputs, and engineering features
- Addressing missing values and imbalanced datasets
- Techniques for feature scaling and encoding
Supervised Learning Approaches
- Regression and classification algorithms
- Ensemble techniques: Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation strategies
Unsupervised Learning Approaches
- Clustering algorithms: K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction techniques: PCA and t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Overview of TensorFlow and Keras
- Constructing and training feedforward neural networks
- Enhancing neural network performance
Reinforcement Learning (Foundations)
- Key concepts: agents, environments, and reward signals
- Implementing foundational reinforcement learning algorithms
- Use cases for reinforcement learning
Deploying AI Models
- Persisting and retrieving trained models
- Connecting models to applications through APIs
- Monitoring and maintaining AI systems in production environments
Recap and Future Directions
Requirements
- A strong grasp of Python programming fundamentals
- Proficiency with data analysis libraries such as NumPy and pandas
- Familiarity with core machine learning concepts and algorithms
Target Audience
- Software developers looking to broaden their AI development expertise
- Data analysts who want 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
