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Duration 40 hours
Course Outline
Introduction to Artificial Intelligence
- Defining AI and its applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of key tools and platforms
Python for AI
- Refresher on Python fundamentals
- Utilizing Jupyter Notebook
- Managing library installation and dependencies
Data Handling
- Data preparation and cleaning techniques
- Leveraging Pandas and NumPy
- Visualization using Matplotlib and Seaborn
Machine Learning Fundamentals
- Comparing Supervised and Unsupervised Learning
- Techniques for Classification, Regression, and Clustering
- Processes for model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network architecture
- Working with TensorFlow or PyTorch
- Constructing and training models
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis
- Fundamentals of image recognition
- Utilizing pre-trained models and transfer learning
AI Deployment in Applications
- Saving and loading model artifacts
- Integrating AI models into APIs or web applications
- Best practices for testing and ongoing maintenance
Summary and Future Directions
Requirements
- Solid understanding of programming logic and structures
- Proficiency in Python or comparable high-level languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems professionals
- Software developers looking to integrate AI capabilities
- Engineers and technical leaders exploring 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
