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Course Outline
Foundational Concepts of Artificial Intelligence
- Defining AI and its practical applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of leading tools and platforms
Python in the Context of AI
- Refreshing core Python fundamentals
- Utilizing Jupyter Notebook effectively
- Managing library installation and dependencies
Data Manipulation and Analysis
- Preparing and cleaning data sets
- Leveraging Pandas and NumPy for data processing
- Creating visualizations with Matplotlib and Seaborn
Core Machine Learning Principles
- Comparing Supervised and Unsupervised Learning approaches
- Exploring classification, regression, and clustering techniques
- Training, validating, and testing models
Deep Learning and Neural Networks
- Understanding neural network structures
- Implementing models with TensorFlow or PyTorch
- Constructing and training deep learning models
NLP and Computer Vision Applications
- Performing text classification and sentiment analysis
- Basics of image recognition
- Utilizing pre-trained models and transfer learning strategies
Integrating AI into Software Applications
- Saving and retrieving trained models
- Embedding AI models within APIs or web applications
- Best practices for ongoing testing and maintenance
Recap and Future Directions
Requirements
- A solid grasp of programming logic and structural concepts
- Proficiency with Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems specialists
- Software engineers looking to incorporate 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
