Artificial Intelligence with Python (Intermediate Level) Training Course
Artificial Intelligence with Python involves developing intelligent systems by leveraging Python’s rich ecosystem of AI and machine learning libraries.
This instructor-led, live training (available online or on-site) is designed for intermediate-level Python programmers who want to design, implement, and deploy AI solutions using Python.
By the end of this training, participants will be able to:
- Implement AI algorithms utilizing Python’s core AI libraries.
- Work with supervised, unsupervised, and reinforcement learning models.
- Integrate AI solutions into existing applications and workflows.
- Evaluate model performance and optimize for accuracy and efficiency.
Format of the Course
- Interactive lectures and discussions.
- Plenty of exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Overview of AI in Python
- Key concepts and scope of AI
- Python libraries for AI development
- AI project structure and workflow
Data Preparation for AI
- Data cleaning, transformation, and feature engineering
- Handling missing and unbalanced data
- Feature scaling and encoding
Supervised Learning Techniques
- Regression and classification algorithms
- Ensemble methods: Random Forest, Gradient Boosting
- Hyperparameter tuning and cross-validation
Unsupervised Learning Techniques
- Clustering methods: K-Means, DBSCAN, hierarchical clustering
- Dimensionality reduction: PCA, t-SNE
- Use cases for unsupervised learning
Neural Networks and Deep Learning
- Introduction to TensorFlow and Keras
- Building and training feedforward neural networks
- Optimizing neural network performance
Reinforcement Learning (Intro)
- Core concepts of agents, environments, and rewards
- Implementing basic 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
- Solid understanding of Python programming fundamentals
- Experience with data analysis libraries such as NumPy and pandas
- Basic knowledge of machine learning concepts and algorithms
Audience
- Software developers aiming to expand their AI development skills
- Data analysts seeking to apply AI techniques to complex datasets
- R&D professionals building AI-powered applications
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Testimonials (2)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
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