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Course Outline
Introduction
- Overview of Machine Learning (ML) and Deep Learning (DL) concepts for government applications
- Future industry evolutions with ML and DL, including implications for public sector workflows
Business Strategy with Deep Learning
- Defining business problems in a government context
- Data-driven decision making to enhance governance and accountability
- Analytical thinking and mindset for effective problem-solving in the public sector
- Business strategy modeling to optimize public services
- Case studies and examples relevant to government operations
Deep Learning Software and Tools
- Fundamentals of Python and Pandas for government use cases
- Open source DL tools (TensorFlow, CNTK, Torch, Keras, etc.) adapted for government applications
- Use cases and examples demonstrating the practical application in public sector projects
Deep Learning with Neural Networks
- Neural Network Learning techniques, including Backpropagation, tailored for government data sets
- Convolutional Neural Networks (CNN) for image and pattern recognition in government contexts
- Recurrent Neural Networks (RNN) for sequence prediction and natural language processing in public sector applications
- DL modeling examples specifically designed to address government challenges
Summary and Next Steps
Requirements
- An understanding of machine learning concepts for government applications
- Experience in Python programming
Audience
- Business analysts
- Data scientists
- Developers
14 Hours
Testimonials (2)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
