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Duration 40 hours
Course Outline
Foundations of Artificial Intelligence
- Defining AI and its practical applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of prevalent tools and platforms
Python for AI Development
- Refresher on essential Python fundamentals
- Utilizing Jupyter Notebook for interactive coding
- Installation and management of relevant libraries
Data Manipulation and Analysis
- Preparing and cleansing datasets
- Leveraging Pandas and NumPy for data operations
- Visualizing data using Matplotlib and Seaborn
Introductory Machine Learning
- Contrasting 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 Architectures
- Working with TensorFlow or PyTorch frameworks
- Constructing and training advanced models
Natural Language Processing and Computer Vision
- Text Classification and Sentiment Analysis
- Fundamentals of Image Recognition
- Implementing Pre-trained Models and Transfer Learning
AI Integration and Deployment
- Saving and loading trained models
- Embedding AI models into APIs or web applications
- Best practices for ongoing testing and maintenance
Recap and Future Directions
Requirements
- Solid grasp of programming logic and structural concepts
- Practical experience with Python or comparable high-level languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems specialists
- Software developers aiming to incorporate AI capabilities
- Engineers and technical leaders 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
