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Course Outline
Introduction to Artificial Intelligence
- Defining AI and its practical applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of prevalent tools and platforms.
Python for AI
- A quick refresher on Python fundamentals.
- Navigating Jupyter Notebook.
- Installing and managing essential libraries.
Working with Data
- Preparing and cleansing datasets.
- Leveraging Pandas and NumPy.
- Visualising data using Matplotlib and Seaborn.
Machine Learning Basics
- Contrasting Supervised and Unsupervised Learning.
- Exploring classification, regression, and clustering.
- Model training, validation, and testing procedures.
Neural Networks and Deep Learning
- Understanding neural network architecture.
- Utilising TensorFlow or PyTorch.
- Constructing and training models.
Natural Language and Computer Vision
- Text classification and sentiment analysis techniques.
- Fundamentals of image recognition.
- Utilising pre-trained models and transfer learning.
Deploying AI in Applications
- Saving and loading models for reuse.
- Integrating AI models into APIs or web applications.
- Best practices for testing and ongoing maintenance.
Summary and Future Directions
Requirements
- A solid understanding of programming logic and structural concepts.
- Proficiency in 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
