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Duration 35 hours
Course Outline
Introduction to AI in Python
- Essential concepts and the breadth of AI
- Python libraries used in AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleansing, transformation, and feature engineering
- Managing missing and imbalanced data
- Applying feature scaling and encoding
Supervised Learning Methods
- Regression and classification algorithms
- Ensemble techniques: Random Forest, Gradient Boosting
- Tuning hyperparameters and cross-validation
Unsupervised Learning Methods
- Clustering approaches: K-Means, DBSCAN, hierarchical clustering
- Reducing dimensionality: 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
- Improving neural network performance
Reinforcement Learning (Introduction)
- Fundamental concepts of agents, environments, and rewards
- Building basic reinforcement learning algorithms
- Real-world uses of reinforcement learning
Deploying AI Models
- Storing and retrieving trained models
- Embedding models into applications through APIs
- Overseeing and maintaining AI systems in production environments
Wrap-up and Future Directions
Requirements
- A strong grasp of Python programming basics
- Familiarity with data analysis tools like NumPy and pandas
- Foundational understanding of machine learning principles and algorithms
Target Audience
- Software engineers looking to enhance their AI development capabilities
- Data analysts intending to apply AI methods to intricate datasets
- R&D specialists 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
