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Course Outline
Introduction to Artificial Intelligence
- Understanding AI and its applications.
- Differentiating between AI, Machine Learning, and Deep Learning.
- Overview of popular tools and platforms.
Python for AI
- Refresher on Python fundamentals.
- Utilizing Jupyter Notebook.
- Installing and managing libraries.
Working with Data
- Data preparation and cleaning techniques.
- Leveraging Pandas and NumPy.
- Data visualization using Matplotlib and Seaborn.
Machine Learning Basics
- Distinguishing between Supervised and Unsupervised Learning.
- Concepts of classification, regression, and clustering.
- Model training, validation, and testing processes.
Neural Networks and Deep Learning
- Understanding neural network architecture.
- Utilizing TensorFlow or PyTorch.
- Constructing and training models.
Natural Language and Computer Vision
- Text classification and sentiment analysis.
- Fundamentals of image recognition.
- Leveraging 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
- 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 professionals
- Software developers looking to integrate AI capabilities
- Engineers and technical managers exploring 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
