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Course Outline
Introduction to the World of Artificial Intelligence
- Defining AI and identifying its practical applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of leading tools and platforms.
Leveraging Python for AI Development
- Quick refresher on Python fundamentals.
- Working effectively with Jupyter Notebook.
- Strategies for installing and managing libraries.
Data Management and Processing
- Techniques for data preparation and cleansing.
- Utilizing Pandas and NumPy for data analysis.
- Data visualisation using Matplotlib and Seaborn.
Fundamentals of Machine Learning
- Comparing Supervised and Unsupervised Learning.
- Exploring classification, regression, and clustering.
- Processes for model training, validation, and testing.
Deep Dive into Neural Networks
- Understanding neural network architecture.
- Implementing models with TensorFlow or PyTorch.
- Constructing and training deep learning models.
Natural Language Processing and Computer Vision
- Implementing text classification and sentiment analysis.
- Foundational concepts in image recognition.
- Leveraging pre-trained models and transfer learning.
Integrating AI into Live Applications
- Methods for saving and loading AI models.
- Embedding AI models within APIs or web applications.
- Best practices for ongoing testing and maintenance.
Recap and Future Directions
Requirements
- Solid comprehension of programming logic and structures.
- Practical experience 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 capabilities.
- Engineers and technical managers exploring 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
