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Duration 40 hours
Course Outline
Introduction to Artificial Intelligence
- Defining AI and its practical applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of popular tools and platforms.
Python for AI
- Refreshing core Python fundamentals.
- Utilizing Jupyter Notebook effectively.
- Managing library installation and dependencies.
Data Management
- Data preparation and cleaning techniques.
- Leveraging Pandas and NumPy for data manipulation.
- Visualizing data using Matplotlib and Seaborn.
Machine Learning Fundamentals
- Comparing Supervised and Unsupervised Learning.
- Exploring 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 deep learning models.
Natural Language and Computer Vision
- Text classification and sentiment analysis techniques.
- Foundations of image recognition.
- Utilizing pre-trained models and transfer learning.
AI Deployment in Applications
- Strategies for saving and loading models.
- Integrating AI models into APIs or web applications.
- Best practices for testing and ongoing maintenance.
Summary and Future Directions
Requirements
- A solid grasp of programming logic and structural concepts.
- Practical experience with Python or comparable high-level languages.
- Foundational knowledge of algorithms and data structures.
Target Audience
- IT systems specialists.
- Software developers looking to embed AI capabilities.
- Engineers and technical managers evaluating AI-based 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
