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Duration 35 hours
Course Outline
Overview of AI in Python
- Core concepts and the scope of AI
- Python libraries essential for AI development
- Structuring AI projects and defining workflows
Data Preparation for AI
- Data cleaning, transformation, and feature engineering
- Managing missing values and unbalanced data
- Feature scaling and encoding strategies
Supervised Learning Techniques
- Regression and classification algorithms
- Ensemble methods including Random Forest and Gradient Boosting
- Hyperparameter tuning and cross-validation
Unsupervised Learning Techniques
- Clustering methods such as K-Means, DBSCAN, and hierarchical clustering
- Dimensionality reduction using PCA and t-SNE
- Practical applications of unsupervised learning
Neural Networks and Deep Learning
- Introduction to TensorFlow and Keras
- Constructing and training feedforward neural networks
- Enhancing neural network performance
Reinforcement Learning (Intro)
- Fundamental concepts of agents, environments, and rewards
- Implementing basic reinforcement learning algorithms
- Real-world applications of reinforcement learning
Deploying AI Models
- Techniques for saving and loading trained models
- Integrating models into applications via APIs
- Monitoring and maintaining AI systems in production environments
Summary and Future Steps
Requirements
- A robust grasp of Python programming fundamentals
- Hands-on experience with data analysis libraries like NumPy and pandas
- Familiarity with foundational machine learning concepts and algorithms
Target Audience
- Software developers looking to enhance their AI development capabilities
- Data analysts aiming to apply AI techniques to complex datasets
- R&D professionals 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
