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Course Outline
Introduction
Overview of TensorFlow
- Understanding TensorFlow.
- Key features of TensorFlow.
Foundations of AI
- Computational Psychology
- Computational Philosophy
Machine Learning
- Theoretical aspects of computational learning
- Algorithms for computational experience
Deep Learning
- Artificial neural networks
- Distinguishing deep learning from machine learning
Setting Up the Development Environment
- Installing and configuring TensorFlow
TensorFundamentals
- Managing nodes
- Leveraging the Keras API
Implementing Fraud Detection
- Data ingestion and storage
- Feature engineering
- Data labeling
- Data normalization
- Partitioning data for training and testing
- Input data formatting
Forecasting and Regression
- Model loading
- Prediction visualization
- Building regression models
Classification
- Constructing and compiling classifier models
- Training and evaluating models
Summary and Wrap-up
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
