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Duration 35 hours
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
Preparing Data for AI
- Data cleansing, conversion, and feature extraction
- Managing missing values and imbalanced datasets
- Scaling features and applying encoding techniques
Supervised Learning Methods
- Regression and classification algorithms
- Ensemble approaches: Random Forest and Gradient Boosting
- Hyperparameter optimization and cross-validation
Unsupervised Learning Methods
- Clustering techniques: K-Means, DBSCAN, and hierarchical clustering
- Reducing dimensionality: 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
- Enhancing neural network performance
Reinforcement Learning (Introduction)
- Key concepts: agents, environments, and reward systems
- Implementing foundational reinforcement learning algorithms
- Use cases for reinforcement learning
Deployment of AI Models
- Storing and retrieving trained models
- Connecting models to applications through APIs
- Overseeing and maintaining AI systems in production
Conclusion and Future Path
Requirements
- 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 looking to enhance their AI development capabilities
- Data analysts intending to utilize AI techniques on complex datasets
- R&D specialists constructing 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
