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Duration 35 hours
Course Outline
Introduction to AI in Python
- Core concepts and the scope of AI
- Python libraries utilized in AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleaning, transformation, and feature engineering
- Managing missing values and unbalanced datasets
- Scaling and encoding features
Supervised Learning Approaches
- Algorithms for regression and classification
- Ensemble techniques such as Random Forest and Gradient Boosting
- Tuning hyperparameters and performing cross-validation
Unsupervised Learning Approaches
- Clustering techniques including K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction methods like 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
Introduction to Reinforcement Learning
- Fundamentals of agents, environments, and rewards
- Implementing basic reinforcement learning algorithms
- Use cases for reinforcement learning
Deploying AI Models
- Persisting and loading trained models
- Incorporating models into applications using APIs
- Monitoring and maintaining AI systems in production
Wrap-up and Further Directions
Requirements
- A strong grasp of fundamental Python programming concepts
- Familiarity with data analysis tools like NumPy and pandas
- A basic understanding of machine learning principles and algorithms
Audience
- Software developers looking to broaden their AI development capabilities
- Data analysts who wish to apply AI methods to complex datasets
- R&D professionals 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
