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Duration 35 hours
Course Outline
AI Foundations in Python
- Core principles and the scope of artificial intelligence
- Python libraries essential for AI development
- Structuring AI projects and establishing workflows
Data Readiness for AI
- Data cleaning, transformation, and feature engineering
- Strategies for handling missing values and class imbalance
- Applying feature scaling and 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
- 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
- Enhancing neural network performance
Introduction to Reinforcement Learning
- Key concepts: agents, environments, and reward systems
- Implementing fundamental reinforcement learning algorithms
- Use cases for reinforcement learning
AI Model Deployment
- Persistence of trained models: saving and loading
- Embedding models into applications through APIs
- Monitoring and maintenance of AI systems in production
Recap and Future Directions
Requirements
- A strong grasp of core Python programming principles
- Proficiency with data analysis tools like NumPy and pandas
- Familiarity with foundational machine learning theories and algorithms
Target Audience
- Software engineers looking to enhance their AI development capabilities
- Data analysts interested in applying AI methods to complex data sets
- R&D specialists creating applications powered by AI
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
