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Course Outline
Introduction to Artificial Intelligence
- What is AI and where is it applied?
- AI versus Machine Learning versus Deep Learning.
- Popular tools and platforms.
Python for AI
- Refreshing Python fundamentals.
- Using Jupyter Notebook.
- Installing and managing libraries.
Working with Data
- Data preparation and cleaning.
- Using Pandas and NumPy.
- Visualisation with Matplotlib and Seaborn.
Machine Learning Basics
- Supervised versus Unsupervised Learning.
- Classification, regression, and clustering.
- Model training, validation, and testing.
Neural Networks and Deep Learning
- Neural network architecture.
- Using TensorFlow or PyTorch.
- Building and training models.
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis.
- Fundamentals of image recognition.
- Pre-trained models and transfer learning.
Deploying AI in Applications
- Saving and loading models.
- Integrating AI models into APIs or web applications.
- Best practices for testing and maintenance.
Summary and Next Steps
Requirements
- A solid understanding of programming logic and structures.
- Experience with Python or similar high-level programming languages.
- Basic familiarity with algorithms and data structures.
Audience
- IT systems professionals.
- Software developers seeking to integrate AI.
- Engineers and technical managers exploring AI-based solutions.
40 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
