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Course Outline
Foundations of Artificial Intelligence
- Defining AI and its real-world applications
- Differentiating AI, Machine Learning, and Deep Learning
- Overview of leading tools and platforms
Python for AI Development
- Review of essential Python syntax
- Leveraging Jupyter Notebook for experimentation
- Managing and installing necessary libraries
Data Handling and Preparation
- Preparing and cleaning datasets
- Utilizing Pandas and NumPy for data analysis
- Visualizing data with Matplotlib and Seaborn
Core Machine Learning Concepts
- Contrasting supervised and unsupervised learning methods
- Exploring classification, regression, and clustering techniques
- Processes for training, validating, and testing models
Neural Networks and Deep Learning
- Understanding neural network architectures
- Implementing models with TensorFlow or PyTorch
- Constructing and training deep learning models
Natural Language Processing and Computer Vision
- Performing text classification and sentiment analysis
- Fundamentals of image recognition
- Utilizing pre-trained models and transfer learning strategies
Integrating AI into Applications
- Techniques for saving and loading models
- Embedding AI models into APIs or web interfaces
- Best practices for ongoing testing and maintenance
Conclusions and Future Directions
Requirements
- A solid grasp of programming logic and structural concepts
- Proficiency with Python or comparable high-level languages
- Fundamental knowledge of algorithms and data structures
Target Audience
- IT infrastructure specialists
- Software engineers looking to incorporate AI capabilities
- Technical leaders and engineers investigating AI-centric 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
