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Course Outline
Introduction to Artificial Intelligence
- Defining AI and exploring its various applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of popular tools and platforms.
Python for AI
- Quick refresher on Python fundamentals.
- Utilising Jupyter Notebook.
- Installing and managing libraries.
Data Manipulation
- Data preparation and cleaning techniques.
- Utilising Pandas and NumPy.
- Data visualisation using Matplotlib and Seaborn.
Fundamentals of Machine Learning
- Contrasting Supervised and Unsupervised Learning.
- Exploring classification, regression, and clustering.
- Model training, validation, and testing processes.
Neural Networks and Deep Learning
- Understanding neural network architectures.
- Working with TensorFlow or PyTorch.
- Constructing and training models.
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis.
- Basics of image recognition.
- Leveraging pre-trained models and transfer learning.
Implementing AI in Applications
- Saving and loading models.
- Integrating AI models into APIs or web applications.
- Best practices for testing and ongoing maintenance.
Summary and Future Directions
Requirements
- A solid grasp of programming logic and structures.
- Practical experience with Python or comparable high-level programming languages.
- Fundamental knowledge of algorithms and data structures.
Target Audience
- IT systems specialists.
- Software developers aiming to integrate AI capabilities.
- Engineers and technical leaders 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
