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Duration 35 hours
Course Outline
Foundations of AI in Python
- Core AI concepts and scope
- Essential Python libraries for AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleansing, transformation, and feature engineering
- Managing missing values and data imbalance
- Techniques for feature scaling and encoding
Supervised Learning Approaches
- Algorithms for regression and classification
- Ensemble techniques: Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation strategies
Unsupervised Learning Approaches
- Clustering algorithms: K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction methods: 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
- Strategies for optimizing neural network performance
Introduction to Reinforcement Learning
- Fundamentals of agents, environments, and reward systems
- Implementing basic reinforcement learning algorithms
- Real-world use cases for reinforcement learning
AI Model Deployment
- Techniques for saving and loading trained models
- Integrating models into applications through APIs
- Monitoring and maintaining AI systems in production environments
Course Recap and Future Steps
Requirements
- A strong command of Python programming fundamentals
- Proficiency with data analysis libraries such as NumPy and pandas
- A foundational understanding of core machine learning concepts and algorithms
Target Audience
- Software engineers looking to deepen their AI development capabilities
- Data analysts interested in applying AI techniques to complex data sets
- R&D specialists focused on 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
