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Duration 40 hours
Course Outline
Foundations of Artificial Intelligence
- Defining AI and its practical applications
- Distinguishing AI from Machine Learning and Deep Learning
- Overview of key tools and platforms
Python in the AI Context
- Reviewing essential Python syntax
- Utilizing Jupyter Notebook for development
- Managing and installing necessary libraries
Data Handling and Preparation
- Preparing and sanitizing datasets
- Leveraging Pandas and NumPy for analysis
- Visualizing data with Matplotlib and Seaborn
Introductory Machine Learning
- Comparing Supervised and Unsupervised Learning
- Techniques for classification, regression, and clustering
- Processes for model training, validation, and evaluation
Neural Networks and Deep Learning
- Understanding neural network architectures
- Implementing models with TensorFlow or PyTorch
- Constructing and training deep learning models
NLP and Computer Vision
- Conducting text classification and sentiment analysis
- Foundational principles of image recognition
- Utilizing pre-trained models and transfer learning
Integrating AI into Applications
- Persisting and retrieving model states
- Embedding AI models into APIs or web interfaces
- Best practices for ongoing testing and maintenance
Conclusions and Future Directions
Requirements
- Proficiency in programming logic and structural design
- Hands-on experience with Python or comparable high-level languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems experts
- Software engineers looking to incorporate AI capabilities
- Technical managers and engineers investigating AI-driven 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
