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Course Outline
Foundations of Artificial Intelligence
- Defining AI and identifying its key applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of prevalent tools and platforms
Python for AI Development
- Refreshing essential Python fundamentals
- Utilizing Jupyter Notebook for development
- Managing the installation of necessary libraries
Data Management and Processing
- Techniques for data preparation and cleaning
- Leveraging Pandas and NumPy for data handling
- Creating visualizations with Matplotlib and Seaborn
Introduction to Machine Learning
- Differentiating Supervised and Unsupervised Learning
- Exploring classification, regression, and clustering methods
- Processes for model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network architectures
- Working with TensorFlow or PyTorch
- Constructing and training deep learning models
Natural Language Processing and Computer Vision
- Performing text classification and sentiment analysis
- Fundamentals of image recognition
- Utilizing pre-trained models and transfer learning techniques
Integrating AI into Applications
- Saving and loading trained models
- Implementing AI models within APIs or web applications
- Best practices for ongoing testing and maintenance
Conclusion and Future Directions
Requirements
- A solid grasp of programming logic and structural design
- Proficiency with Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Intended Audience
- IT systems professionals
- Software developers looking to incorporate AI capabilities
- Engineers and technical managers 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
