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Duration 35 hours
Course Outline
Overview of AI in Python
- Core concepts and scope of AI.
- Python libraries essential for AI development.
- Structure and workflow of AI projects.
Data Preparation for AI
- Data cleaning, transformation, and feature engineering.
- Managing missing and unbalanced data.
- Feature scaling and encoding techniques.
Supervised Learning Techniques
- Regression and classification algorithms.
- Ensemble methods: Random Forest and Gradient Boosting.
- Hyperparameter tuning and cross-validation.
Unsupervised Learning Techniques
- Clustering methods: K-Means, DBSCAN, and hierarchical clustering.
- Dimensionality reduction: PCA and t-SNE.
- Practical use cases for unsupervised learning.
Neural Networks and Deep Learning
- Introduction to TensorFlow and Keras.
- Constructing and training feedforward neural networks.
- Optimizing neural network performance.
Reinforcement Learning (Intro)
- Core concepts regarding agents, environments, and rewards.
- Implementing fundamental reinforcement learning algorithms.
- Applications of reinforcement learning.
Deploying AI Models
- Saving and loading trained models.
- Integrating models into applications via APIs.
- Monitoring and maintaining AI systems in production.
Summary and Next Steps
Requirements
- Strong grasp of Python programming fundamentals.
- Practical experience with data analysis libraries such as NumPy and pandas.
- Fundamental knowledge of machine learning concepts and algorithms.
Audience
- Software developers looking to enhance their AI development capabilities.
- Data analysts who wish to apply AI techniques to complex datasets.
- R&D professionals engaged in building AI-powered 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
