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Course Outline
Introduction to Artificial Intelligence
- Defining AI and exploring its various applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of widely used tools and platforms
Leveraging Python for AI
- Refresher on essential Python fundamentals
- Working effectively with Jupyter Notebook
- Installing and managing necessary libraries
Handling Data
- Preparing and cleansing datasets
- Utilizing Pandas and NumPy for data manipulation
- Creating visualizations with Matplotlib and Seaborn
Fundamentals of Machine Learning
- Comparing Supervised and Unsupervised Learning
- Techniques for classification, regression, and clustering
- Processes for model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network architecture
- Implementing models with TensorFlow or PyTorch
- Constructing and training neural models
Natural Language Processing and Computer Vision
- Performing text classification and sentiment analysis
- Basics of image recognition
- Utilizing pre-trained models and transfer learning strategies
Deploying AI in Applications
- Techniques for saving and loading models
- Integrating AI models into APIs or web applications
- Best practices for ongoing testing and maintenance
Recap and Future Directions
Requirements
- A solid comprehension of programming logic and structural frameworks
- Proficiency in Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems specialists
- Software developers looking to embed AI capabilities into their work
- Engineers and technical managers investigating AI-based 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
