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Duration 35 hours (5 days)
Course Outline
Introduction to AI in Python
- Fundamental concepts and the scope of AI
- Essential Python libraries for AI development
- Structuring AI projects and defining workflows
Data Preparation for AI
- Techniques for data cleaning, transformation, and feature engineering
- Strategies for managing missing values and data imbalances
- Methods for feature scaling and encoding
Supervised Learning Approaches
- Algorithms for regression and classification
- Ensemble techniques including Random Forest and Gradient Boosting
- Hyperparameter optimization and cross-validation strategies
Unsupervised Learning Approaches
- Clustering techniques such as K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction via PCA and t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Getting started with TensorFlow and Keras
- Constructing and training feedforward neural networks
- Techniques for enhancing neural network performance
Reinforcement Learning (Introduction)
- Core principles involving agents, environments, and reward systems
- Implementation of basic reinforcement learning algorithms
- Real-world use cases for reinforcement learning
AI Model Deployment
- Procedures for saving and retrieving trained models
- Integrating models into applications using APIs
- Strategies for monitoring and maintaining AI systems in production environments
Conclusion and Future Steps
Requirements
- A strong command of fundamental Python programming concepts
- Practical experience with data analysis libraries like NumPy and pandas
- Familiarity with foundational machine learning concepts and algorithms
Target Audience
- Software engineers looking to broaden their expertise in AI development
- Data analysts eager to apply AI techniques to intricate datasets
- R&D professionals focused on constructing AI-powered 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
