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Duration 35 hours
Course Outline
Introduction to AI within Python
- Fundamental concepts and the reach of AI
- Essential Python libraries for AI creation
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleansing, manipulation, and feature creation
- Managing missing values and data imbalance
- Scaling features and applying encoding
Supervised Learning Methods
- Algorithms for regression and classification
- Ensemble strategies: Random Forest and Gradient Boosting
- Adjusting hyperparameters and using cross-validation
Unsupervised Learning Methods
- Clustering techniques: K-Means, DBSCAN, and hierarchical approaches
- Reducing dimensions: PCA and t-SNE
- Practical applications of unsupervised learning
Deep Learning and Neural Networks
- Getting started with TensorFlow and Keras
- Constructing and training feedforward neural networks
- Enhancing neural network performance
Reinforcement Learning (Fundamentals)
- Key concepts of agents, environments, and reward systems
- Applying basic reinforcement learning algorithms
- Use cases for reinforcement learning
Deploying AI Models
- Persisting and retrieving trained models
- Connecting models to applications through APIs
- Overseeing and sustaining AI systems in live environments
Conclusion and Future Directions
Requirements
- A strong grasp of Python programming basics
- Proficiency with data analysis tools like NumPy and pandas
- Familiarity with fundamental machine learning concepts and algorithms
Target Audience
- Software engineers seeking to broaden their AI development capabilities
- Data analysts aiming to leverage AI methods on complex data sets
- R&D specialists developing AI-enhanced 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
