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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 essential tools and platforms.
Python for AI
- Refresher on Python fundamentals.
- Utilizing Jupyter Notebook for development.
- Installing and managing necessary libraries.
Working with Data
- Data preparation and cleaning techniques.
- Utilizing Pandas and NumPy for data manipulation.
- Visualizing data using Matplotlib and Seaborn.
Machine Learning Basics
- Differentiating between Supervised and Unsupervised Learning.
- Exploring classification, regression, and clustering methods.
- Processes for model training, validation, and testing.
Neural Networks and Deep Learning
- Understanding neural network architectures.
- Implementing models using TensorFlow or PyTorch.
- Constructing and training deep learning models.
Natural Language and Computer Vision
- Performing text classification and sentiment analysis.
- Basics of image recognition.
- Leveraging pre-trained models and transfer learning.
Deploying AI in Applications
- Techniques for saving and loading models.
- Integrating AI models into APIs or web applications.
- Best practices for ongoing testing and maintenance.
Summary and Future Directions
Requirements
- Solid comprehension of programming logic and structural frameworks
- Practical experience with Python or equivalent high-level languages
- Foundational knowledge of algorithms and data structures
Intended Audience
- IT system specialists
- Software engineers aiming to embed AI capabilities
- Technical managers and engineers investigating AI-driven 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
