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Course Outline
Introduction to Artificial Intelligence
- Defining AI and its applications
- Differentiating 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 processes
- Leveraging Pandas and NumPy
- Data visualization with Matplotlib and Seaborn
Machine Learning Basics
- Contrasting Supervised and Unsupervised Learning
- Classification, regression, and clustering techniques
- Model training, validation, and testing procedures
Neural Networks and Deep Learning
- Understanding neural network architecture
- Implementing 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
- Techniques for saving and loading models
- Integrating AI models into APIs or web applications
- Best practices for testing and maintenance
Summary and Next Steps
Requirements
- A foundational understanding of programming logic and structures
- Prior experience with Python or comparable high-level programming languages
- Basic familiarity with algorithms and data structures
Target Audience
- IT systems professionals
- Software developers looking to integrate AI capabilities
- Engineers and technical managers exploring 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
