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Course Outline
Introduction to Artificial Intelligence
- Defining AI and its real-world applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of leading tools and platforms
Python for AI
- Refreshing Python fundamentals
- Utilizing Jupyter Notebook
- Installing and managing essential libraries
Working with Data
- Data preparation and cleaning techniques
- Leveraging Pandas and NumPy
- Data visualization using Matplotlib and Seaborn
Machine Learning Basics
- Comparing Supervised and Unsupervised Learning
- Understanding classification, regression, and clustering
- Processes for model training, validation, and testing
Neural Networks and Deep Learning
- Neural network architecture concepts
- Working with TensorFlow or PyTorch
- Constructing and training deep learning models
Natural Language and Computer Vision
- Text classification and sentiment analysis
- Fundamentals of image recognition
- Utilizing pre-trained models and transfer learning
Deploying AI in Applications
- Saving and loading model artifacts
- Integrating AI models into APIs or web applications
- Best practices for testing and ongoing maintenance
Summary and Next Steps
Requirements
- A solid grasp of programming logic and structures
- Proficiency in Python or other high-level programming languages
- Fundamental knowledge of algorithms and data structures
Target Audience
- IT systems professionals
- Software developers aiming to embed AI capabilities
- 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
