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Course Outline
Introduction to Artificial Intelligence
- Defining AI and exploring its applications
- Distinguishing AI from Machine Learning and Deep Learning
- Overview of prevalent tools and platforms
Python for AI
- A refresher on Python fundamentals
- Utilising Jupyter Notebook
- Managing library installation and dependencies
Data Handling
- Data preparation and cleansing techniques
- Working with Pandas and NumPy
- Visualisation using Matplotlib and Seaborn
Machine Learning Fundamentals
- Supervised versus Unsupervised Learning
- Classification, regression, and clustering techniques
- Model training, validation, and testing processes
Neural Networks and Deep Learning
- Understanding neural network architecture
- Leveraging TensorFlow or PyTorch
- Constructing and training models
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis
- Fundamentals of image recognition
- Utilising pre-trained models and transfer learning
Integrating AI into Applications
- Saving and loading models
- Implementing AI models in APIs or web applications
- Best practices for testing and ongoing maintenance
Summary and Next Steps
Requirements
- A solid grasp of programming logic and structures
- Practical experience with Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems professionals
- Software developers looking to incorporate AI capabilities
- Engineers and technical managers investigating 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
