Course Code: ainlpdataanalysis
Duration: 21 hours
Prerequisites:
  • Strong programming skills in C#
  • Familiarity with Microsoft Pilot and basic AI concepts

Audience

  • Developers
  • IT professionals
Overview:

The integration of AI Data Analysis, Distributed AI, and NLP is powerful in scenarios where data needs to be processed and analyzed at scale (e.g., across multiple locations or devices), and where human-computer interaction is facilitated through natural language.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to leverage AI in management and administrative functions.

By the end of this training, participants will be able to:

  • Master AI techniques for data analysis, distributed systems, and NLP using C#.
  • Integrate AI solutions into existing administrative and management workflows.
  • Gain insight into the future trends of AI and its potential impact on business operations.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction

Overview of AI in Data Analysis

  • Evolution of AI in data analysis
  • Key concepts: machine learning, deep learning, and statistical analysis

Data Handling and Preprocessing

  • Data wrangling techniques
  • Handling missing data and outliers
  • Data transformation and normalization

C# for Data Analysis

  • Utilizing C# libraries for data manipulation (e.g., ML.NET, DataFrame)
  • Integrating C# with AI tools (e.g., TensorFlow.NET)
  • Best practices for efficient data processing in C#

Machine Learning Models in C#

  • Supervised vs. unsupervised learning
  • Implementing machine learning models with ML.NET
  • Model training, validation, and evaluation

Deep Learning with C#

  • Introduction to neural networks
  • Using TensorFlow.NET for deep learning
  • Building and deploying deep learning models in a C# environment

AI-Powered Data Visualization

  • Creating interactive visualizations with C#
  • Integration with Power BI for enhanced reporting

Introduction to Distributed AI

  • Concepts of distributed computing and AI
  • Benefits and challenges of distributed AI

Implementing Distributed AI with C#

  • Distributed machine learning with ML.NET and ONNX Runtime
  • Scaling AI solutions across multiple nodes

Integrating Microsoft Pilot with Distributed AI

  • Leveraging Microsoft Pilot for AI-driven decision support
  • Automating management processes with distributed AI

Fundamentals of NLP

  • Key concepts: tokenization, sentiment analysis, and named entity recognition
  • NLP in business applications

Implementing NLP in C#

  • Utilizing NLP libraries (e.g., NLP.NET)
  • Text analysis and processing with C#

NLP for Administrative and Management Tasks

  • Automating email responses, survey analysis, and document classification
  • Enhancing customer support with AI-driven chatbots

Integrating AI with Existing Systems

  • Best practices for AI integration in C# projects
  • Challenges and considerations in deployment

AI in Management: Case Studies and Success Stories

  • Reviewing AI implementation in administrative and management scenarios
  • Lessons learned and best practices

Future of AI: Emerging Trends

  • AI in edge computing, IoT, and cloud
  • Ethical and legal considerations in AI

Summary and Next Steps