Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Artificial Intelligence
- Defining AI and its real-world applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of popular tools and platforms.
Python for AI
- Refresher on Python fundamentals.
- Utilizing Jupyter Notebook.
- Installing and managing necessary libraries.
Working with Data
- Data preparation and cleaning techniques.
- Leveraging Pandas and NumPy.
- Data visualization using Matplotlib and Seaborn.
Machine Learning Basics
- Supervised versus Unsupervised Learning.
- Exploring classification, regression, and clustering.
- Processes for model training, validation, and testing.
Neural Networks and Deep Learning
- Understanding neural network architecture.
- Implementing with TensorFlow or PyTorch.
- Constructing and training models.
Natural Language and Computer Vision
- Text classification and sentiment analysis.
- Fundamentals of image recognition.
- Utilizing 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 maintenance.
Summary and Next Steps
Requirements
- A solid grasp of programming logic and structural concepts.
- Prior experience with Python or comparable high-level programming languages.
- Basic familiarity with algorithms and data structures.
Target Audience
- IT systems professionals.
- Software developers aiming to integrate AI capabilities.
- Engineers and technical managers investigating 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
