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Duration 40 hours
Course Outline
Introduction to Artificial Intelligence
- Defining AI and its practical applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of popular tools and platforms.
Python for AI
- Refresher on Python fundamentals.
- Working with Jupyter Notebook.
- Installation and management of libraries.
Working with Data
- Data preparation and cleansing techniques.
- Leveraging Pandas and NumPy.
- Data visualization using Matplotlib and Seaborn.
Machine Learning Basics
- Contrasting Supervised and Unsupervised Learning.
- Exploration of classification, regression, and clustering.
- Processes for model training, validation, and testing.
Neural Networks and Deep Learning
- Understanding neural network architecture.
- Utilizing TensorFlow or PyTorch.
- Construction and training of models.
Natural Language and Computer Vision
- Text classification and sentiment analysis.
- Fundamentals of image recognition.
- Application of pre-trained models and transfer learning.
Deploying AI in Applications
- Techniques for saving and loading models.
- Integration of AI models into APIs or web apps.
- Best practices for testing and ongoing maintenance.
Summary and Next Steps
Requirements
- Solid comprehension of programming logic and structures.
- Proficiency with Python or comparable high-level languages.
- Foundational knowledge of algorithms and data structures.
Target Audience
- IT systems professionals.
- Software developers aiming to embed AI capabilities.
- Engineers and technical leaders investigating AI-based solutions.
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
