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Course Outline
Introduction to Artificial Intelligence
- Defining AI and exploring its various applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of leading tools and platforms.
Python for AI
- Review of essential Python concepts.
- Utilizing Jupyter Notebook effectively.
- Managing library installation and dependencies.
Data Handling
- Data preparation and cleaning techniques.
- Working with Pandas and NumPy.
- Data visualization using Matplotlib and Seaborn.
Fundamentals of Machine Learning
- Comparing Supervised and Unsupervised Learning.
- Understanding classification, regression, and clustering.
- Processes for model training, validation, and testing.
Neural Networks and Deep Learning
- Understanding neural network architectures.
- Utilizing TensorFlow or PyTorch.
- Constructing and training complex models.
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis.
- Introduction to 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 testing and ongoing maintenance.
Conclusion and Path Forward
Requirements
- A solid understanding of programming logic and structural design
- 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 incorporate AI functionalities
- Engineers and technical managers 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
