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Duration 35 hours
Course Outline
Introduction to AI in Python
- Core concepts and the scope of AI
- Python libraries essential for AI development
- Structuring AI projects and defining workflows
Data Preparation for AI
- Data cleaning, transformation, and feature engineering
- Managing missing and unbalanced data
- 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 using PCA and t-SNE
- Practical use cases for unsupervised learning
Neural Networks and Deep Learning
- Getting started with TensorFlow and Keras
- Constructing and training feedforward neural networks
- Strategies for optimizing neural network performance
Reinforcement Learning (Introduction)
- Foundational concepts of agents, environments, and rewards
- Implementing basic reinforcement learning algorithms
- Real-world applications of reinforcement learning
Deployment of AI Models
- Saving and loading trained models
- Integrating models into applications through APIs
- Monitoring and maintaining AI systems in production environments
Summary and Future Directions
Requirements
- A robust understanding of Python programming fundamentals
- Familiarity with data analysis libraries such as NumPy and pandas
- Foundational knowledge of machine learning concepts and algorithms
Target Audience
- Software developers looking to broaden their AI development capabilities
- Data analysts intent on applying AI techniques to complex datasets
- R&D professionals focused on building 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
