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Course Outline
Introduction
Overview of TensorFlow
- Understanding TensorFlow
- Key features of TensorFlow
Introduction to Artificial Intelligence
- Computational Psychology
- Computational Philosophy
Machine Learning
- Computational learning theory
- Algorithms for computational experience
Deep Learning
- Artificial neural networks
- Differences between deep learning and machine learning
Setting Up the Development Environment
- Installation and configuration of TensorFlow
TensorFlow Quick Start
- Working with nodes
- Utilizing the Keras API
Fraud Detection
- Data input and output operations
- Feature preparation
- Data labeling
- Data normalization
- Dividing data into test and training sets
- Formatting input images
Predictions and Regressions
- Loading models
- Visualizing predictions
- Creating regression models
Classifications
- Building and compiling a classifier model
- Training and testing the model
Summary and Conclusion
Requirements
- Experience 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
