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Duration 14 hours
Course Outline
Introduction
TensorFundamentals
- Definition of TensorFlow
- Core capabilities of TensorFlow
Artificial Intelligence Overview
- Computational approaches to psychology
- Computational perspectives in philosophy
Machine Learning Principles
- Theoretical frameworks for computational learning
- Algorithmic methods for processing computational experience
Deep Learning Concepts
- Artificial neural network architecture
- Differentiation between deep learning and machine learning
Development Environment Setup
- Installation and configuration of TensorFlow
TensorFoundamentals
- Implementation of graph nodes
- Utilization of the Keras API
Fraud Detection Applications
- Data ingestion and persistence operations
- Feature engineering processes
- Data labeling protocols
- Data normalization techniques
- Dataset partitioning for testing and training
- Input image formatting standards
Predictive Analysis and Regression
- Model loading procedures
- Visualization of predictive outcomes
- Construction of regression models
Classification Systems
- Development and compilation of classifier models
- Model validation through training and testing
Summary and Conclusions
Requirements
- Proficiency in Python programming
Intended Audience
- Data Scientists
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
