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 diverse applications
- Distinguishing AI from Machine Learning and Deep Learning
- Overview of leading tools and platforms
Python for AI
- Refresher on essential Python concepts
- Utilizing Jupyter Notebook for development
- Managing the installation and maintenance of libraries
Data Handling
- Preparing and cleaning datasets
- Leveraging Pandas and NumPy for data manipulation
- Creating visualizations with Matplotlib and Seaborn
Foundations of Machine Learning
- Comparing Supervised and Unsupervised Learning
- Exploring classification, regression, and clustering techniques
- Processes for model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network structures
- Implementing models with TensorFlow or PyTorch
- Constructing and training deep learning models
Natural Language Processing and Computer Vision
- Performing text classification and sentiment analysis
- Basics of image recognition
- Utilizing pre-trained models and transfer learning
AI Deployment in Applications
- Techniques for saving and loading models
- Integrating AI models into APIs or web applications
- Best practices for ongoing testing and maintenance
Conclusion and Future Steps
Requirements
- A solid grasp of programming logic and structural fundamentals
- Proficiency in Python or comparable high-level languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems specialists
- Software developers looking to embed AI capabilities
- Engineers and technical leaders 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
