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Course Outline
Introduction to Artificial Intelligence
- Defining AI and its applications
- Distinctions between AI, Machine Learning, and Deep Learning
- Overview of popular tools and platforms
Python for AI
- Refresher on Python fundamentals
- Utilizing Jupyter Notebook
- Installation and management of libraries
Working with Data
- Data preparation and cleaning techniques
- Leveraging Pandas and NumPy
- Data visualization using Matplotlib and Seaborn
Machine Learning Basics
- Supervised versus Unsupervised Learning
- Classification, regression, and clustering methods
- Model training, validation, and testing processes
Neural Networks and Deep Learning
- Neural network architecture principles
- Employing TensorFlow or PyTorch
- Constructing and training models
Natural Language and Computer Vision
- Text classification and sentiment analysis
- Fundamentals of image recognition
- Utilizing pre-trained models and transfer learning
Deploying AI in Applications
- Saving and loading models
- Integrating AI models into APIs or web applications
- Best practices for testing and maintenance
Summary and Next Steps
Requirements
- Proficiency in programming logic and structures
- Prior experience with Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems professionals
- Software developers aiming to integrate 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
