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Duration 35 hours (5 days)
Course Outline
Introduction to AI in Python
- Core AI principles and scope
- Essential Python libraries for AI
- Structuring AI projects and workflows
Preparing Data for AI
- Data cleansing, transformation, and feature creation
- Managing missing and imbalanced data
- Feature scaling and encoding techniques
Supervised Learning Approaches
- Regression and classification models
- Ensemble techniques: Random Forest, Gradient Boosting
- Hyperparameter adjustment and cross-validation
Unsupervised Learning Approaches
- Clustering algorithms: K-Means, DBSCAN, hierarchical clustering
- Dimensionality reduction: PCA, t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Getting started with TensorFlow and Keras
- Constructing and training feedforward neural networks
- Enhancing neural network performance
Reinforcement Learning (Fundamentals)
- Key concepts: agents, environments, and rewards
- Building basic reinforcement learning algorithms
- Real-world uses of reinforcement learning
Deploying AI Models
- Persisting and retrieving trained models
- Connecting models to applications via APIs
- Overseeing and maintaining AI systems in production
Wrap-up and Future Steps
Requirements
- A strong grasp of Python programming basics
- Proficiency with data analysis tools like NumPy and pandas
- Familiarity with foundational machine learning concepts and algorithms
Target Audience
- Software developers looking to broaden their AI development capabilities
- Data analysts aiming to leverage AI techniques on complex datasets
- R&D specialists 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
