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Duration 40 hours
Course Outline
Introduction to Artificial Intelligence
- Defining AI and identifying its real-world applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of leading tools and platforms.
Python for AI
- Refresher on Python fundamentals.
- Navigating Jupyter Notebook.
- Managing and installing essential libraries.
Working with Data
- Data cleansing and preparation techniques.
- Leveraging Pandas and NumPy.
- Data visualization using Matplotlib and Seaborn.
Machine Learning Basics
- Comparing Supervised and Unsupervised Learning.
- Concepts of 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 models.
Natural Language and Computer Vision
- Text classification and sentiment analysis.
- Fundamentals of image recognition.
- Application of pre-trained models and transfer learning.
Deploying AI in Applications
- Saving and retrieving models.
- Integrating AI models into APIs or web applications.
- Best practices for ongoing testing and maintenance.
Summary and Next Steps
Requirements
- A solid grasp of programming logic and code structure.
- Practical experience with Python or comparable high-level languages.
- Foundational knowledge of algorithms and data structures.
Target Audience
- IT infrastructure and systems specialists.
- Software engineers aiming to incorporate AI capabilities.
- Engineers and technical leads 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
