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  • Course Length:
  • 6 weeks

Machine Learning (ML) aspects of Artificial Intelligence (AI) is revolutionizing all aspects of the computer industry. ML use cases like speech and image recognition have already had an impact on many industries. The telecom industry is different. The Machine Learning Workshop provides a detailed introduction to AI from a telecom perspective. AI is explored from a definition and underlying technology perspective. It starts with an introduction to AI models. The course then moves to an exploration of data selection, then to the details of building Machine Learning models and Deep Learning models based on telecom-specific data. The key concepts are presented using hands-on activities that include analyzing data and using a Machine Learning model in Python and Pandas. Hands-on Exercises are based on Pandas and Splunk.

This workshop is intended for anyone with Python skills and the desire to build knowledge and skills related to leveraging data tools to start their journey into Data Analytics. It follows the Part 1 of the Workshop series.

After completing this course, the student will be able to:
■ Define AI terms: Neural Network, ML, Deep Learning
■ List key examples of neural networks
■ Describe model training, testing and deployment
■ Describe types of input data
■ List key Machine Learning tools
■ Build a basic Machine Learning model using Python
■ Build a basic Machine Learning model using a cloud-based Service

1. AI Defined
1.1 What is AI?
1.2 What is ML?
1.3 What is Deep Learning?
1.4 AI – An End User View
Exercise: Use Case: Inference and AI Model
Exercise: Use Case: Cloud-based Service Use Case

2. ML Models Defined
2.1 What is an ANN?
2.2 Basics of ML Model Design
2.3 Types of Learning
2.4 ML Models
Exercise: Use Case: Types of Supervised Learning and Neural Networks
Exercise: Use Case: Cloud-based Service Use Case

3. ML Lifecycle
3.1 Building and Framing the ML Model
3.2 Data Gathering and Preparation
3.3 Model Creation and Training
3.4 Model Testing and Deployment
Exercise: Use Case: Build the ML Model
Exercise: Use Case: Cloud-based Service Use Case

4. ML Data Preparation
4.1 Types of Data
4.2 Data Selection and Process Flow
Exercise: Use Case: Prepare Data for ML Model Creation
Exercise: Use Case: Cloud-based Service Use Case

5. ML Model Creation and Training
5.1 ML Model Layer Details
5.2 Neurons and Activation Functions
5.3 Defining Hyperparameters
5.4 Analyzing Results
5.5 ML Development Libraries
Exercise: Use Case: Build and Train the ML Model
Exercise: Use Case: Cloud-based Service Use Case