Course Outline

Introduction

Use cases and opportunities for Telecom providers

What makes up AI?

Computer Vision, Natural Language Procession (NLP), Voice Recognition, etc.

Data as the Oil of AI

How Probability and Statistics Drive AI

The Programming Language Skills Needed for AI

Understanding Machine Learning

Applying Machine Learning Libraries to Develop Intelligent Systems

The Data Processing Engines Behind Data Analysis

Using Rules Engines and Expert Systems to Make Decisions

Advanced Approaches to Machine Learning: Deep Learning

Exercise: Predicting Network Failures with Machine Learning

How AI drives IoT and the Applications for IoT in Telecom

Handling Greater Volumes of Data with Cloud Technologies

Automation Technologies and Approaches for Telecom

Bringing it All Together

Use cases and opportunities for Telecom providers

The Low-hanging Fruit for Telecom Companies

Planning and Communicating an AI Strategy

Summary and Conclusion

Requirements

  • An understanding of the telecom industry
  • An understanding of networking
  • A general understanding of programing concepts
 14 Hours

Number of participants



Price per participant

Related Courses

H2O AutoML

14 Hours

AutoML with Auto-sklearn

14 Hours

AutoML with Auto-Keras

14 Hours

Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

21 Hours

Introduction to Stable Diffusion for Text-to-Image Generation

21 Hours

AlphaFold

7 Hours

TensorFlow Lite for Embedded Linux

21 Hours

TensorFlow Lite for Android

21 Hours

TensorFlow Lite for iOS

21 Hours

Tensorflow Lite for Microcontrollers

21 Hours

Deep Learning Neural Networks with Chainer

14 Hours

Distributed Deep Learning with Horovod

7 Hours

Accelerating Deep Learning with FPGA and OpenVINO

35 Hours

Building Deep Learning Models with Apache MXNet

21 Hours

Deep Learning with Keras

21 Hours

Related Categories

1