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Course Outline
Foundations of Artificial Intelligence
- Defining AI and identifying its application areas
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of leading tools and platforms
Python for AI Development
- Review of essential Python concepts
- Utilizing Jupyter Notebook
- Managing library installation and dependencies
Data Management
- Preparing and cleansing datasets
- Leveraging Pandas and NumPy
- Visualizing data with Matplotlib and Seaborn
Fundamentals of Machine Learning
- Differentiating between Supervised and Unsupervised Learning
- Exploring classification, regression, and clustering techniques
- Processes for training, validating, and testing models
Deep Learning and Neural Networks
- Understanding neural network architectures
- Implementing models with TensorFlow or PyTorch
- Constructing and training deep learning models
Natural Language Processing and Computer Vision
- Performing text classification and sentiment analysis
- Basics of image recognition
- Utilizing pre-trained models and transfer learning
AI Deployment in Applications
- Techniques for saving and loading models
- Integrating AI models into APIs or web applications
- Best practices for ongoing testing and maintenance
Conclusion and Future Directions
Requirements
- A solid grasp of programming logic and structural concepts
- 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 embed 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
