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Course Outline
Introduction to Artificial Intelligence
- Defining AI and exploring its real-world applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of prevalent tools and platforms.
Python for AI
- Refresher on essential Python fundamentals.
- Utilising Jupyter Notebook for development.
- Installing and managing required libraries.
Data Handling
- Preparing and cleaning datasets.
- Working with Pandas and NumPy.
- Data visualisation using Matplotlib and Seaborn.
Machine Learning Fundamentals
- Comparing Supervised and Unsupervised Learning.
- Techniques for classification, regression, and clustering.
- Processes for model training, validation, and testing.
Neural Networks and Deep Learning
- Understanding neural network architecture.
- Implementing models with TensorFlow or PyTorch.
- Building and training effective models.
Natural Language and Computer Vision
- Text classification and sentiment analysis.
- Fundamentals of image recognition.
- Leveraging pre-trained models and transfer learning.
AI Deployment in Applications
- Saving and loading trained models.
- Integrating AI models into APIs or web applications.
- Best practices for testing and ongoing maintenance.
Summary and Next Steps
Requirements
- A solid comprehension of programming logic and structural design.
- Practical experience with Python or comparable high-level programming languages.
- Foundational familiarity with algorithms and data structures.
Target Audience
- IT systems professionals.
- Software developers looking to integrate AI capabilities.
- 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
