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Course Outline
Introduction to Artificial Intelligence
- Defining AI and identifying its key use cases
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of prominent tools and platforms
Python for AI
- Review of essential Python concepts
- Utilizing Jupyter Notebook
- Managing library installation and dependencies
Working with Data
- Data preparation and cleansing processes
- Leveraging Pandas and NumPy
- Data visualization using Matplotlib and Seaborn
Machine Learning Basics
- Comparing Supervised and Unsupervised Learning
- Understanding classification, regression, and clustering methods
- Processes for training, validating, and testing models
Neural Networks and Deep Learning
- Architecture of neural networks
- Implementation with TensorFlow or PyTorch
- Constructing and training deep learning models
Natural Language and Computer Vision
- Techniques for text classification and sentiment analysis
- Fundamentals of image recognition
- Utilizing pre-trained models and transfer learning
Deploying AI in Applications
- Strategies for saving and loading models
- Integrating AI models into APIs or web applications
- Best practices for ongoing testing and maintenance
Summary and Next Steps
Requirements
- A solid grasp of programming logic and structural concepts
- Practical experience with Python or comparable high-level programming languages
- Fundamental knowledge of algorithms and data structures
Target Audience
- Professionals in IT systems
- Software developers aiming to incorporate AI capabilities
- Engineers and technical managers interested in exploring 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
