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Duration 35 hours
Course Outline
Fundamentals of AI in Python
- Core concepts and the scope of Artificial Intelligence
- Essential Python libraries for AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Techniques for data cleaning, transformation, and feature engineering
- Strategies for managing missing and imbalanced data
- Methods for feature scaling and encoding
Supervised Learning Approaches
- Algorithms for regression and classification
- Ensemble techniques including Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation practices
Unsupervised Learning Approaches
- Clustering algorithms 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 optimizing neural network performance
Introduction to Reinforcement Learning
- Fundamental concepts of agents, environments, and reward mechanisms
- Implementing foundational reinforcement learning algorithms
- Real-world applications of reinforcement learning
AI Model Deployment
- Procedures for saving and retrieving trained models
- Connecting models to applications through APIs
- Ongoing monitoring and maintenance of AI systems in production
Conclusions and Future Directions
Requirements
- A strong grasp of core Python programming principles
- Practical experience with data analysis tools such as NumPy and pandas
- Familiarity with fundamental machine learning concepts and algorithms
Target Audience
- Software developers seeking to broaden their AI expertise
- Data analysts looking to apply AI techniques to complex data sets
- R&D professionals involved in creating 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
