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Course Outline
Introduction to Artificial Intelligence
- Defining AI and identifying its application domains.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of leading tools and platforms.
Python for AI Development
- Review of essential Python fundamentals.
- Utilizing Jupyter Notebook for development.
- Installation and management of necessary libraries.
Data Handling and Processing
- Techniques for data preparation and cleansing.
- Leveraging Pandas and NumPy for analysis.
- Data visualization using Matplotlib and Seaborn.
Fundamentals of Machine Learning
- Comparison of Supervised and Unsupervised Learning.
- Exploring classification, regression, and clustering.
- Processes for model training, validation, and testing.
Neural Networks and Deep Learning
- Understanding neural network architectures.
- Implementation using TensorFlow or PyTorch.
- Construction and training of deep learning models.
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis techniques.
- Basics of image recognition.
- Utilization of pre-trained models and transfer learning.
Integrating AI into Applications
- Procedures for saving and loading models.
- Incorporating AI models into APIs or web applications.
- Best practices for ongoing testing and maintenance.
Recap and Future Directions
Requirements
- Foundational knowledge of programming logic and structural design.
- Proficiency with Python or comparable high-level programming languages.
- Elementary understanding of algorithms and data structures.
Target Audience
- IT systems specialists.
- Software developers aiming to embed AI capabilities.
- Engineers and technical leaders investigating AI-driven 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
