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Duration 40 hours
Course Outline
Foundations of Artificial Intelligence
- Defining AI and its common applications.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- Overview of widely used tools and platforms.
Utilizing Python for AI
- Refresher on core Python concepts.
- Working effectively with Jupyter Notebook.
- Installing and managing essential libraries.
Data Management
- Preparing and cleansing data.
- Leveraging Pandas and NumPy for data manipulation.
- Creating visualizations with Matplotlib and Seaborn.
Fundamentals of Machine Learning
- Comparing Supervised and Unsupervised Learning.
- Exploring classification, regression, and clustering techniques.
- Processes for training, validating, and testing models.
Neural Networks and Deep Learning
- Understanding neural network architecture.
- Implementing models with TensorFlow or PyTorch.
- Constructing and training deep learning models.
NLP and Computer Vision
- Performing text classification and sentiment analysis.
- Basics of image recognition.
- Utilizing pre-trained models and transfer learning.
Integrating AI into Applications
- Saving and loading trained models.
- Incorporating AI models into APIs or web applications.
- Best practices for ongoing testing and maintenance.
Recap and Future Directions
Requirements
- A solid grasp 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 professionals.
- Software developers aiming to incorporate AI capabilities.
- Engineers and technical managers investigating 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
