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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
- Reviewing essential Python fundamentals
- Utilizing Jupyter Notebook for development
- Managing the installation of required libraries
Data Management
- Preparing and refining datasets
- Leveraging Pandas and NumPy for data processing
- Creating visualizations with Matplotlib and Seaborn
Machine Learning Fundamentals
- Comparing supervised and unsupervised learning approaches
- Exploring classification, regression, and clustering techniques
- Conducting model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network structures
- Working with frameworks like TensorFlow or PyTorch
- Constructing and training complex models
Natural Language Processing and Computer Vision
- Performing text classification and sentiment analysis
- Grasping the basics of image recognition
- Utilizing pre-trained models and transfer learning strategies
Integrating AI into Applications
- Persisting and retrieving model states
- Embedding AI models into APIs or web applications
- Adhering to best practices for ongoing testing and maintenance
Conclusion and Future Directions
Requirements
- A solid grasp of programming logic and structural principles
- Proficiency with Python or comparable high-level programming languages
- Fundamental knowledge of algorithms and data structures
Target Audience
- IT systems specialists
- Software developers looking to incorporate AI capabilities
- Engineers and technical managers 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
