Online ExpertTrack in IT & Computer Science

Ethics, Laws and Implementing an AI Solution on Microsoft Azure

Build your data science for AI skills with Python and Microsoft Azure, and explore its ethical and legal frameworks.

Created by

CloudSwyft Global Systems, Inc.CloudSwyft Global Systems, Inc.

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Accredited by

MicrosoftMicrosoft

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Build your confidence in data science for AI and machine learning

Data science is increasingly vital to the world of work. Workplace skills in this area are among those most in demand from employers, and the ethical and legal support for this vital sector must develop in line with the technology.

This Expert Track will help you develop your career and expertise in data science for AI. You’ll also cover the laws and ethics of AI, data research methods with Python, and the design of an AI solution on the Microsoft Azure cloud infrastructure.

Build your knowledge of the laws and ethical standards relevant to AI

You’ll get to grips with the moral principles that guide the development and use of AI technologies.

You’ll learn how to apply data research methods for ethical and legal work in analytics and AI, and gain an in-depth insight into the foundation of ethical and legal frameworks.

Process, analyse, and extract meaning from natural language with software

Alongside a study of AI ethics, you’ll learn how to develop machine learning models with Azure machine learning.

You’ll also process images and videos to gain an understanding of how we see the world in the way that we do, and will learn how bots are created and enhanced with QnA Maker and LUIS.

Get in-demand practical Python programming skills

This course will also help you implement data science research methods using Python.

[Content for this ExpertTrack has been adapted from the Advanced AI on Microsoft Azure microcredential, which offers smaller cohorts, with rich tutor interactions, feedback on assessments, and academic credit.]

Key skills you will learn

  • Ethics of AI
  • Coding
  • Microsoft Azure
  • Cloud Computing
  • data science

Experience required

You’ll need a basic knowledge of maths, statistics, programming (Python would be an advantage), and C# or Visual Studio.

You should also have some experience working with data from Excel, databases or text files, and be willing to develop your skills with hands-on practice.

Getting started

This interactive Expert Track is designed for students and professionals beginning or developing their careers in data science for AI, analytics, and machine learning.

ExpertTrack course overview

  • Course 1

    Understand the ethics and laws surrounding AI and Analytical tools including data sharing and privacy.

    3 weeks

    5 hours per week

    • Week 1

      Course Introduction
      • About this Course
      • Data's Ethical Foundations
      • Data's Legal Foundations
      • Ethical Data Practice
      • CloudSwyft Hands-On Learning: Lab Check 1
      • Wrapping Up the Week
    • Week 2

      Data - Individuals, Society and Business Ethics
      • CloudSwyft Hands-On Learning: Lab Check 2
      • Data Bias and Identity
      • Data Privacy and Power
      • Business and Ethical Data Use
      • Wrapping Up the Week
    • Week 3

      Data - Business Law, A.I. and Future Opportunities
      • Business and Data Privacy
      • CloudSwyft Hands-On Learning: Lab Check 3
      • AI and Design
      • XAI
      • CloudSwyft Hands-On Learning: Lab Check 4
      • Course Wrap-Up
  • Course 2

    Discover data collection methods to support your data science research and analysis.

    3 weeks

    5 hours per week

    • Week 1

      Course Introduction
      • About this Course
      • The Research Process
      • The Psychology of Providing Data
      • CloudSwyft Hands-On Lab 1
      • Planning for Analysis
      • Wrapping up the Week
    • Week 2

      Research Claims, Measurement and Correlation and Experimental Design
      • Power and Sample Size Planning
      • Research Practices
      • CloudSwyft Hands-On Lab 2
      • Frequency Claims
      • Association Claims
      • Causal Claims
      • CloudSwyft Hands-On Lab 3
      • Wrapping Up the Week
    • Week 3

      Measurement, Correlational and Experimental Design
      • Survey Design and Measurement
      • Reliability and Validity
      • CloudSwyft Hands-On Lab 4
      • Bivariate and Multivariate Designs
      • Between and Within Groups Experimental Designs
      • Factorial Designs
      • CloudSwyft Hands-On Lab 5
      • Wrapping Up the Course
  • Course 3

    Understand the theory of machine learning before gaining practical experience using Python programming.

    5 weeks

    5 hours per week

    • Week 1

      Introduction to Course and Machine Learning
      • Course Introduction
      • Introduction to Machine Learning
      • Exploratory Data Analysis for Regression
      • Visualisation for High Dimensions
      • Wrapping Up the Week
    • Week 2

      Data Exploration & Preparation
      • Exploratory Data Analysis for Classification
      • Data Cleaning
      • Data Preparation
      • Data Preparation and Cleaning using Python
      • Feature Engineering
      • Weekly Wrap-Up
    • Week 3

      Regression & Classification
      • Regression
      • Putting Regression Concepts Into Practice
      • Classification
      • RoC Curves
      • Putting Classification Concepts Into Practice
      • Weekly Wrap-Up
    • Week 4

      Principles & Techniques of Model Improvement
      • Principles of Model Improvement
      • Techniques for Improving Models
      • Cross Validation
      • Dimensionality Reduction
      • Introduction to Decision Trees
      • Ensemble Methods: Boosting
      • Weekly Wrap-Up
    • Week 5

      Machine Learning Algorithms & Unsupervised Learning
      • Ensemble Methods: Descent & Decision Forests
      • Advanced Machine Learning Algorithm: Neural Networks
      • Advanced Machine Learning Algorithm: SVMs
      • Advanced Machine Learning Algorithm: Naive Bayes Models
      • Unsupervised Machine Learning
      • Unsupervised Machine Learning Labs
      • Wrapping up the Course
  • Course 4

    Gain the skills and confidence in Microsoft Azure to help you understand how to design and implement a data science solution.

    3 weeks

    6 hours per week

    • Week 1

      Course Introduction
      • About this Course
      • Overview of Azure Cognitive Services
      • Azure Cognitive Service Accounts
      • Introduction to Bots
      • Wrapping up the week
    • Week 2

      Introducing Language Understanding
      • Bot Framework and the Bot Emulator
      • CloudSwyft Labs
      • Introducing Language Understanding
      • Building Intents, Utterances and Entities
      • Language Understanding and AI Applications
      • CloudSwyft Labs and Wrapping up the week
    • Week 3

      QnA Maker, Knowledge Bases and Cognitive Services for Bot Interactions
      • Introducing the QnA Maker
      • Implementing a Knowledge Base with QnA Server
      • Understanding Cognitive Services for Bot Interactions
      • CloudSwyft Lab and Wrapping up the Course

Prove you're job ready

Highlight the new, job-relevant skills you’ve gained and supplement existing qualifications with a hard-earned, industry-specific digital certificate – plus one for every course within your ExpertTrack.

  • Learn the latest in your chosen industry or subject.
  • Complete each course and pass assessments.
  • Receive certificates validated by the educating organisation.
  • Create a shareable certificate link for your CV and LinkedIn.
  • Impress employers with learning outcomes you can add to your CV.
  • Make your career dreams a reality.

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Become an expert

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World-class learning with CloudSwyft Global Systems, Inc.

CloudSwyft has partnered with the top global technology companies to deliver cutting edge digital skills learning across the modern workplace.

This ExpertTrack is accredited by Microsoft

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