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Duration 35 hours
Course Outline
Overview of AI in Python
- Core concepts and the scope of AI
- Python libraries dedicated to AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleaning, transformation, and feature engineering
- Managing missing and imbalanced data
- Scaling and encoding features
Supervised Learning Approaches
- Regression and classification algorithms
- Ensemble techniques: Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation
Unsupervised Learning Approaches
- Clustering methods: K-Means, DBSCAN, and hierarchical clustering
- Reducing dimensionality: PCA and t-SNE
- Practical use cases for unsupervised learning
Neural Networks and Deep Learning
- Fundamentals of TensorFlow and Keras
- Constructing and training feedforward neural networks
- Enhancing neural network performance
Introduction to Reinforcement Learning
- Key concepts: agents, environments, and rewards
- Implementing basic reinforcement learning algorithms
- Applications of reinforcement learning
Deploying AI Models
- Persisting and retrieving trained models
- Integrating models into applications through APIs
- Monitoring and maintaining AI systems in production environments
Summary and Future Directions
Requirements
- A strong grasp of Python programming basics
- Proficiency with data analysis tools like NumPy and pandas
- Foundational understanding of machine learning concepts and algorithms
Target Audience
- Software developers looking to enhance their AI development capabilities
- Data analysts interested in applying 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
