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Duration 40 hours
Course Outline
Fundamentals of Artificial Intelligence
- Defining AI and identifying its application areas
- Differentiating AI, Machine Learning, and Deep Learning
- Overview of key tools and platforms
Python for AI Development
- Refreshing core Python skills
- Leveraging Jupyter Notebook
- Managing library installation and dependencies
Data Management
- Preparing and sanitizing data
- Utilizing Pandas and NumPy
- Data visualization using Matplotlib and Seaborn
Machine Learning Essentials
- Comparing Supervised and Unsupervised Learning
- Exploring Classification, regression, and clustering
- Training, validating, and testing models
Neural Networks and Deep Learning
- Understanding Neural network architecture
- Implementing with TensorFlow or PyTorch
- Constructing and training models
NLP and Computer Vision
- Performing Text classification and sentiment analysis
- Basics of Image recognition
- Applying Pre-trained models and transfer learning
AI Deployment in Applications
- Persistence of models (saving and loading)
- Integrating AI models into APIs or web applications
- Best practices for testing and ongoing maintenance
Conclusion and Future Pathways
Requirements
- A solid comprehension of programming logic and structural design
- Practical experience with Python or comparable high-level languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems specialists
- Software engineers aiming to incorporate AI capabilities
- Engineers and technical leaders investigating AI-centric 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
