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Duration 40 hours
Course Outline
Introduction to Artificial Intelligence
- Defining AI and its practical applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of prevalent tools and platforms
Python for AI
- Review of essential Python features
- Working effectively with Jupyter Notebook
- Library installation and dependency management
Data Processing
- Data preparation and cleansing techniques
- Utilizing Pandas and NumPy for analysis
- Creating visualizations with Matplotlib and Seaborn
Fundamentals of Machine Learning
- Comparing Supervised and Unsupervised Learning
- Techniques for classification, regression, and clustering
- Model training, validation, and evaluation strategies
Neural Networks and Deep Learning
- Understanding neural network architecture
- Implementation using TensorFlow or PyTorch
- Constructing and training deep learning models
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis
- Basics of image recognition
- Leveraging pre-trained models and transfer learning
AI Deployment in Applications
- Persisting and retrieving models
- Integrating AI models into APIs or web applications
- Best practices for testing and ongoing maintenance
Conclusion and Future Directions
Requirements
- Proficiency in programming logic and fundamental structures
- Practical experience with Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems professionals
- Software developers looking to incorporate AI capabilities
- Engineers and technical managers investigating AI-driven solutions
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
