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Course Outline
Introduction
Overview of TensorFlow
- Understanding what TensorFlow is
- Key features of TensorFlow
The Concept of AI
- Computational Psychology
- Computational Philosophy
Machine Learning Foundations
- Computational learning theory
- Algorithms for computational experience
Deep Learning Insights
- Artificial neural networks
- Comparing Deep Learning with Machine Learning
Setting Up the Development Environment
- Installation and configuration of TensorFlow
TensorFlow Quick Start
- Managing nodes
- Leveraging the Keras API
Fraud Detection Workflows
- Data reading and writing processes
- Feature preparation
- Data labeling techniques
- Data normalization
- Partitioning data into training and test sets
- Formatting input data structures
Prediction and Regression Analysis
- Loading existing models
- Visualizing prediction results
- Generating regression models
Classification Models
- Construction and compilation of classifier models
- Model training and evaluation
Summary and Key Takeaways
Requirements
- Familiarity with Python programming
Target Audience
- Data Scientists
14 Hours
Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
