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Course Outline
Foundations of Artificial Intelligence
- Defining AI and its real-world applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Key tools and industry platforms
Python in the Context of AI
- Reviewing core Python fundamentals
- Leveraging Jupyter Notebook for development
- Managing library installation and dependencies
Data Management
- Preparing and cleansing data sets
- Utilizing Pandas and NumPy for data processing
- Creating visualizations with Matplotlib and Seaborn
Fundamentals of Machine Learning
- Contrasting Supervised and Unsupervised Learning
- Exploring classification, regression, and clustering techniques
- Processes for model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network architecture
- Implementing models with TensorFlow or PyTorch
- Constructing and training effective models
Natural Language Processing and Computer Vision
- Performing text classification and sentiment analysis
- Basics of image recognition
- Utilizing pre-trained models and transfer learning strategies
Integrating AI into Applications
- Techniques for saving and retrieving models
- Incorporating AI models into APIs or web-based applications
- Best practices for ongoing testing and maintenance
Recap and Future Directions
Requirements
- A solid grasp of programming logic and structural concepts
- Prior exposure to Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT system specialists
- Software engineers aiming to incorporate 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
