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Course Outline
Overview of Artificial Intelligence
- Defining AI and its practical applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Key tools and industry platforms
Python for AI Development
- Refreshing core Python fundamentals
- Leveraging Jupyter Notebook for development
- Setting up and managing necessary libraries
Data Processing Techniques
- Preparing and cleaning datasets
- Utilizing Pandas and NumPy for data manipulation
- Creating visualizations with Matplotlib and Seaborn
Foundational Machine Learning
- Comparing Supervised and Unsupervised Learning
- Exploring classification, regression, and clustering
- Conducting model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network structures
- Implementing models with TensorFlow or PyTorch
- Constructing and training deep learning models
NLP and Computer Vision
- Performing text classification and sentiment analysis
- Basics of image recognition
- Utilizing pre-trained models and transfer learning
AI Deployment in Applications
- Managing model persistence (saving and loading)
- Integrating AI models into APIs and web applications
- Best practices for ongoing testing and maintenance
Conclusion and Future Directions
Requirements
- Solid grasp of programming logic and structural design
- Proficiency with Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems specialists
- Software developers looking to incorporate AI features
- Engineers and technical leaders investigating AI-based 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
