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Certificate IT Centres Data Science & Artificial Intelligence

Certificate in Python and Artificial Intelligence

4 Subjects
68 Chapters
480 Lessons
400 Marks

LAPT — London Academy of Professional Training

Certificate in Python and Artificial Intelligence
Enquire Now View Brochure Download PDF
Total Marks400
Subjects4
Chapters68
Classes480

About This Certification

A professional certification programme delivered through LAPT-accredited centres worldwide.

Course Curriculum

4 subjects • 68 chapters • 480 classes
01
Python Programming
12 chapters • 100 marks
1 Getting Started with Python and Development Tools 10 classes
1.1 What Python Is Used For
1.2 Installing Python on Your Computer
1.3 Using the Interactive Shell
1.4 Choosing and Setting Up a Code Editor
1.5 Writing and Running Your First Script
1.6 Understanding Syntax and Indentation
1.7 Comments and Readable Code
1.8 Reading Error Messages
1.9 Using Virtual Environments
1.10 Organising a Small Project Folder
2 Variables, Data Types and Operators 10 classes
2.1 Naming and Assigning Variables
2.2 Numbers and Arithmetic
2.3 Text Values and Quotes
2.4 Boolean Values and Comparisons
2.5 Logical Operators
2.6 Type Checking and Conversion
2.7 Getting Input from Users
2.8 Formatting Printed Output
2.9 Operator Precedence
2.10 Practice Building a Bill Calculator
3 Strings and Text Processing 10 classes
3.1 Indexing and Slicing Strings
3.2 Common String Methods
3.3 Searching Within Text
3.4 Replacing and Splitting Text
3.5 Joining and Cleaning Text
3.6 Formatted String Literals
3.7 Escape Characters and Multiline Text
3.8 Validating Text Input
3.9 Introduction to Regular Expressions
3.10 Practice Cleaning Customer Names
4 Decision Making and Loops 10 classes
4.1 If and Else Statements
4.2 Multiple Conditions with Elif
4.3 Nested Decisions
4.4 While Loops
4.5 For Loops and Ranges
4.6 Loop Control with Break and Continue
4.7 Looping Over Text
4.8 Common Loop Patterns
4.9 Avoiding Infinite Loops
4.10 Practice Building a Number Guessing Game
5 Lists, Tuples, Sets and Dictionaries 10 classes
5.1 Creating and Changing Lists
5.2 List Methods and Slicing
5.3 Looping Through Lists
5.4 Tuples and Immutability
5.5 Sets and Removing Duplicates
5.6 Dictionaries and Key Value Pairs
5.7 Nested Data Structures
5.8 List Comprehensions
5.9 Sorting and Searching Collections
5.10 Practice Building a Contact Book
6 Functions and Modules 10 classes
6.1 Defining and Calling Functions
6.2 Parameters and Arguments
6.3 Return Values
6.4 Default and Keyword Arguments
6.5 Variable Scope
6.6 Writing Helpful Docstrings
6.7 Using Built-In Modules
6.8 Creating Your Own Modules
6.9 Importing Code Across Files
6.10 Practice Refactoring a Script into Functions
7 File Handling and Data Formats 10 classes
7.1 Opening and Closing Files
7.2 Reading Text Files
7.3 Writing and Appending to Files
7.4 Working with File Paths
7.5 Reading and Writing Delimited Files
7.6 Working with JSON Data
7.7 Handling Large Files Line by Line
7.8 Processing a Sales Data File
7.9 Saving Results to a Report
7.10 Practice Building a Log File Analyser
8 Errors, Exceptions and Debugging 10 classes
8.1 Types of Programming Errors
8.2 Reading Tracebacks
8.3 Handling Exceptions with Try and Except
8.4 Raising Your Own Exceptions
8.5 Cleaning Up with Finally
8.6 Using Print Statements to Debug
8.7 Using a Debugger with Breakpoints
8.8 Writing Simple Assertions
8.9 Writing Basic Unit Tests
8.10 Practice Fixing a Broken Program
9 Object Oriented Programming in Python 10 classes
9.1 Thinking in Objects
9.2 Defining Classes and Creating Objects
9.3 Attributes and Methods
9.4 The Constructor Method
9.5 Class and Instance Data
9.6 Encapsulation and Private Data
9.7 Inheritance Between Classes
9.8 Method Overriding
9.9 Special Methods for Printing and Comparing
9.10 Practice Modelling a Library System
10 Working with Libraries and Packages 10 classes
10.1 Installing Packages with a Package Manager
10.2 Managing Project Requirements
10.3 Dates and Times
10.4 Random Numbers and Simulations
10.5 Working with Spreadsheets Programmatically
10.6 Making Web Requests
10.7 Reading Data from Web Services
10.8 Command Line Arguments
10.9 Reading Library Documentation
10.10 Practice Building a Weather Lookup Tool
11 Automation Scripts for Everyday Office Tasks 10 classes
11.1 Finding Tasks Worth Automating
11.2 Renaming and Organising Files
11.3 Batch Processing Documents
11.4 Generating Reports Automatically
11.5 Sending Automated Emails
11.6 Scheduling Scripts to Run
11.7 Collecting Data from Web Pages Responsibly
11.8 Logging Script Activity
11.9 Making Scripts Safe to Re-Run
11.10 Practice Automating a Weekly Report
12 Python Practical Project 10 classes
12.1 Choosing a Project Idea
12.2 Writing Requirements and a Plan
12.3 Designing Functions and Classes
12.4 Building the Core Features
12.5 Adding File or Data Storage
12.6 Adding Error Handling
12.7 Testing the Application
12.8 Writing a Readme and User Guide
12.9 Sharing Code with Version Control
12.10 Presenting and Reviewing the Project
02
Software Design, Architecture and Testing
18 chapters • 100 marks
1 Understanding Core Software Design Principles 5 classes
1.1 Introduction to Core Software Design Principles
1.2 Exploring Abstraction in Software Design
1.3 Understanding Encapsulation and Its Benefits
1.4 Implementing Modular Design for Flexibility
1.5 Applying Principles to Real-World Scenarios
2 Design Patterns and Their Applications 5 classes
2.1 Understanding Design Patterns: An Overview
2.2 Exploring Creational Patterns: Building Through Instantiation
2.3 Harnessing Structural Patterns: Organizing Objects and Classes
2.4 Implementing Behavioral Patterns: Managing Communication and Workflow
2.5 Applying Design Patterns: Real-World Examples and Best Practices
3 Principles of Object-Oriented Design 5 classes
3.1 Understanding the Core Concepts of Object-Oriented Design
3.2 Identifying and Defining Classes and Objects
3.3 Exploring Encapsulation and Information Hiding
3.4 Mastering Inheritance for Code Reusability
3.5 Implementing Polymorphism for Flexible Software Design
4 Designing for Scalability and Maintainability 5 classes
4.1 Understanding Scalability and Maintainability Concepts
4.2 Analyzing System Requirements for Scalability
4.3 Implementing Design Patterns for Maintainability
4.4 Evaluating Design Decisions for Scalability
4.5 Applying Modular Design for Future Growth
1 Understanding Core Security Concepts in Software Development 6 classes
1.1 Exploring the Fundamentals of Security in Software Development
1.2 Understanding Threat Landscapes: Identifying Risks in Development
1.3 Implementing Authentication and Authorization Mechanisms
1.4 Securing Data: Techniques for Data Protection and Encryption
1.5 Applying Secure Coding Practices to Prevent Vulnerabilities
1.6 Conducting Security Testing and Audits in Software Projects
2 Implementing Secure Coding Practices 6 classes
2.1 Understanding Secure Coding Principles
2.2 Identifying Common Vulnerabilities in Code
2.3 Implementing Input Validation Techniques
2.4 Applying Authentication and Authorization Protocols
2.5 Utilizing Secure Data Encryption Methods
2.6 Conducting Code Reviews for Security Flaws
3 Advanced Threat Modeling and Risk Assessment 6 classes
3.1 Understanding Advanced Threat Modeling Concepts
3.2 Identifying Potential Threats in Software Systems
3.3 Analyzing Threats with Risk Assessment Techniques
3.4 Designing Effective Mitigation Strategies for Threats
3.5 Applying Threat Modeling in Full-Stack Development
3.6 Conducting Continuous Risk Assessments in API Integration
4 Integrating Security into DevOps Processes 6 classes
4.1 Understanding the Importance of Security in DevOps
4.2 Identifying Common Security Risks in DevOps
4.3 Integrating Security Practices in Continuous Integration
4.4 Implementing Automated Security Testing in DevOps
4.5 Monitoring Security in Continuous Deployment
4.6 Building a Security-Focused DevOps Culture
5 Testing and Monitoring for Security Vulnerabilities 6 classes
5.1 Understanding Security Vulnerabilities in Software
5.2 Identifying Common Security Threats in Code
5.3 Implementing Unit Testing for Security
5.4 Conducting Penetration Testing for Web Applications
5.5 Utilizing Automated Tools for Vulnerability Scanning
5.6 Monitoring Application Logs for Security Indicators
1 Understanding Product Lifecycle Stages 6 classes
1.1 Explore the Concept of Product Lifecycle Stages
1.2 Identify Key Characteristics of Each Lifecycle Stage
1.3 Analyze the Introduction Stage Strategies
1.4 Investigate Growth Stage Challenges and Opportunities
1.5 Evaluate Strategies for the Maturity Stage
1.6 Plan for the Decline Stage and Extension Tactics
2 Strategizing Product Development and Market Entry 6 classes
2.1 Understanding the Product Development Cycle
2.2 Identifying Market Opportunities for New Products
2.3 Crafting a Strategic Product Development Plan
2.4 Analyzing Competitive Landscape for Market Entry
2.5 Developing a Go-To-Market Strategy
2.6 Evaluating Product Development Success Metrics
3 Implementing Agile Practices in Product Lifecycle Management 6 classes
3.1 Understanding Agile Principles in Product Lifecycle Management
3.2 Identifying Key Agile Roles and Responsibilities
3.3 Mapping Agile Methodologies to Product Stages
3.4 Implementing Agile Tools for Product Development
3.5 Facilitating Agile Collaboration in Cross-Functional Teams
3.6 Evaluating Agile Success Metrics in Product Lifecycles
4 Optimizing Product Performance and User Experience 6 classes
4.1 Understanding User Experience Fundamentals
4.2 Analyzing Product Performance Metrics
4.3 Identifying User Pain Points through Feedback
4.4 Implementing Performance Optimization Techniques
4.5 Enhancing User Experience with Design Improvements
4.6 Evaluating Impact of Optimizations on User Satisfaction
5 Ensuring Sustainable Product Retirement and Transition 6 classes
5.1 Understanding Sustainable Product Retirement
5.2 Analyzing Environmental Impacts of Product Disposal
5.3 Identifying Opportunities for Product Reuse and Recycling
5.4 Developing a Transition Plan for Product Lines
5.5 Implementing End-of-Life Strategies for Legacy Products
5.6 Measuring Success in Product Retirement and Transition
15 Software Requirements and User Stories 10 classes
15.1 Why Requirements Matter
15.2 Functional and Non-Functional Requirements
15.3 Identifying Stakeholders
15.4 Interviews and Workshops for Requirements
15.5 Writing User Stories
15.6 Acceptance Criteria
15.7 Prioritising Requirements
15.8 Requirements Traceability
15.9 Reviewing and Baselining Requirements
15.10 Managing Requirement Changes
16 System Modelling with UML Diagrams 10 classes
16.1 Purpose of Visual Modelling
16.2 Use Case Diagrams
16.3 Activity Diagrams for Workflows
16.4 Class Diagrams and Relationships
16.5 Sequence Diagrams for Interactions
16.6 State Diagrams for Behaviour
16.7 Entity Relationship Diagrams
16.8 Wireframes and Screen Flows
16.9 Choosing the Right Diagram
16.10 Modelling a Small Booking System
17 Software Testing Techniques and Test Cases 10 classes
17.1 Principles of Software Testing
17.2 Levels of Testing
17.3 Black Box Test Design
17.4 Equivalence Partitioning and Boundary Values
17.5 White Box Testing Basics
17.6 Writing Unit Tests
17.7 Integration and System Testing
17.8 Regression and Smoke Testing
17.9 Test Plans and Test Data
17.10 Executing and Recording Test Results
18 Defect Tracking, Code Review and Release Readiness 10 classes
18.1 Logging a Clear Defect Report
18.2 Defect Severity and Priority
18.3 Defect Life Cycle
18.4 Using a Defect Tracking Tool
18.5 Peer Code Review Practice
18.6 Coding Standards and Static Checks
18.7 Measuring Test Coverage
18.8 User Acceptance Testing
18.9 Release Checklists and Sign-Off
18.10 Post Release Review and Lessons Learned
03
Machine Learning Fundamentals
20 chapters • 100 marks
1 Understanding the Basics of Machine Learning 6 classes
1.1 Defining Machine Learning: Concepts and Scope
1.2 Exploring Types of Machine Learning: Supervised, Unsupervised, and Reinforcement
1.3 Understanding Data: Inputs, Outputs, and Importance
1.4 Recognizing Patterns: How Algorithms Learn
1.5 Evaluating Models: Accuracy, Precision, and Recall
1.6 Applying Ethical Considerations in Machine Learning
2 Data Preprocessing Techniques for Machine Learning 6 classes
2.1 Understanding the Importance of Data Preprocessing
2.2 Exploring Data Cleaning Methods
2.3 Handling Missing Data Effectively
2.4 Normalizing and Scaling Data for Consistency
2.5 Encoding Categorical Variables for Model Readiness
2.6 Implementing Feature Engineering Techniques
3 Exploring Supervised Learning Algorithms 6 classes
3.1 Understanding Supervised Learning Concepts
3.2 Exploring Linear Regression Techniques
3.3 Implementing Decision Trees for Classification
3.4 Analyzing Support Vector Machines
3.5 Evaluating Model Performance Metrics
3.6 Applying Ensemble Methods in Supervised Learning
4 Unsupervised Learning and Clustering Techniques 6 classes
4.1 Understanding Unsupervised Learning
4.2 Exploring Clustering Techniques
4.3 Implementing K-Means Clustering
4.4 Analyzing Hierarchical Clustering
4.5 Applying DBSCAN for Density-Based Clustering
4.6 Evaluating Clustering Performance
5 Model Evaluation and Performance Metrics 6 classes
5.1 Understanding the Importance of Model Evaluation
5.2 Exploring Common Performance Metrics in Machine Learning
5.3 Comparing Different Model Evaluation Techniques
5.4 Interpreting Confusion Matrix for Classification Models
5.5 Calculating Precision, Recall, and F1-Score
5.6 Assessing Model Performance Through ROC Curves and AUC
1 Understanding Model Evaluation Metrics 6 classes
1.1 Exploring the Importance of Model Evaluation in AI
1.2 Understanding Accuracy, Precision, and Recall Metrics
1.3 Interpreting F1 Score and Its Application
1.4 Analyzing ROC Curves and AUC in Model Assessment
1.5 Evaluating Confusion Matrix Insights for Model Improvement
1.6 Implementing Cross-Validation for Robust Model Evaluation
2 Advanced Techniques for Model Validation 6 classes
2.1 Understanding Cross-Validation Techniques
2.2 Implementing k-Fold Cross-Validation
2.3 Applying Leave-One-Out Cross-Validation
2.4 Utilizing Stratified Sampling in Validation
2.5 Analyzing Model Validation Metrics
2.6 Comparing Validation Approaches for Robust Models
3 Hyperparameter Tuning and Optimization 6 classes
3.1 Understanding Hyperparameters in Machine Learning Models
3.2 Analyzing the Impact of Hyperparameter Values
3.3 Exploring Methods for Hyperparameter Tuning
3.4 Implementing Grid Search for Hyperparameter Optimization
3.5 Applying Random Search for Enhanced Model Performance
3.6 Comparing Hyperparameter Optimization Techniques
4 Model Deployment Strategies 6 classes
4.1 Understanding Model Deployment Environments
4.2 Preparing Models for Deployment
4.3 Containerizing Machine Learning Models
4.4 Implementing Deployment Pipelines
4.5 Monitoring and Managing Deployed Models
4.6 Scaling Model Deployments Across Platforms
5 Monitoring and Maintaining Deployed Models 6 classes
5.1 Understanding the Importance of Model Monitoring
5.2 Setting Up Monitoring Tools and Pipelines
5.3 Analyzing Model Performance Metrics
5.4 Identifying and Responding to Model Drift
5.5 Implementing Automated Alerts and Notifications
5.6 Ensuring Model Robustness and Reliability Over Time
1 Deep Learning Architectures 6 classes
1.1 Explaining Neural Network Fundamentals
1.2 Constructing Convolutional Neural Networks
1.3 Designing Recurrent Neural Networks
1.4 Implementing LSTM and GRU Networks
1.5 Exploring Transformer Architectures
1.6 Applying Deep Learning Architectures to Real-world Problems
2 Natural Language Processing Techniques 6 classes
2.1 Understanding the Basics of Natural Language Processing
2.2 Exploring Tokenization and Text Preprocessing
2.3 Analyzing Syntax with Part-of-Speech Tagging
2.4 Utilizing Named Entity Recognition for Data Extraction
2.5 Applying Sentiment Analysis Techniques
2.6 Implementing Language Models for Text Generation
3 Generative Adversarial Networks (GANs) 6 classes
3.1 Understanding the Basics of GANs
3.2 Exploring the Architecture of Generator and Discriminator
3.3 Training GANs: Techniques and Challenges
3.4 Implementing GANs Using Popular Frameworks
3.5 Evaluating GAN Performance Metrics
3.6 Applying GANs to Real-World Scenarios
4 AI in Reinforcement Learning Systems 6 classes
4.1 Understanding Reinforcement Learning: Fundamentals and Concepts
4.2 Exploring Key Algorithms in Reinforcement Learning
4.3 Analyzing Policy and Value Functions in RL
4.4 Implementing Deep Reinforcement Learning Techniques
4.5 Evaluating and Optimizing Reward Structures
4.6 Applying Reinforcement Learning to Real-World Scenarios
5 Ethical and Responsible AI 6 classes
5.1 Understanding the Principles of Ethical AI
5.2 Exploring Bias and Fairness in AI Systems
5.3 Analyzing AI Transparency and Explainability
5.4 Ensuring Privacy and Data Protection in AI
5.5 Evaluating the Societal Impact of AI Technologies
5.6 Implementing Ethical Guidelines in AI Development
1 Introduction to Machine Learning 6 classes
1.1 Fundamentals of Machine Learning
1.2 Types of Machine Learning
1.3 Supervised vs Unsupervised Learning
1.4 Applications in Hospitality
1.5 Data Preparation Techniques
1.6 Ethics in Machine Learning
2 Predictive Analytics Techniques 6 classes
2.1 Understanding Predictive Analytics
2.2 Regression Analysis Basics
2.3 Time Series Forecasting
2.4 Classification Methods Overview
2.5 Model Evaluation Metrics
2.6 Real-World Applications
3 Customer Preference Modeling 6 classes
3.1 Understanding Customer Preferences
3.2 Data Collection Techniques
3.3 Segmentation Strategies
3.4 Behavioral Analysis Methods
3.5 Personalization Techniques
3.6 Feedback Loop Integration
4 Marketing Strategy Development 6 classes
4.1 Defining Marketing Objectives
4.2 Target Audience Identification
4.3 Digital Marketing Strategies
4.4 Content Marketing Approaches
4.5 Campaign Planning Essentials
4.6 Budgeting for Marketing
5 Evaluating Marketing Campaigns 6 classes
5.1 Key Performance Indicators
5.2 Analyzing Campaign Results
5.3 Customer Feedback Analysis
5.4 A/B Testing Techniques
5.5 Adjusting Strategies Based on Data
5.6 Reporting and Presentation Skills
04
AI Solution Design, Deployment and Ethics
18 chapters • 100 marks
1 Understanding the Principles of AI Solution Design 6 classes
1.1 Exploring the Fundamentals of AI Solution Design
1.2 Identifying Core Components of AI Systems
1.3 Analyzing Data Requirements for AI Solutions
1.4 Prioritizing Objectives for Effective AI Design
1.5 Evaluating Ethical Considerations in AI Solutions
1.6 Applying AI Design Principles to Real-World Scenarios
2 Data Acquisition and Preprocessing Strategies 6 classes
2.1 Understanding Data Acquisition Methods
2.2 Identifying Relevant Data Sources
2.3 Implementing Data Collection Techniques
2.4 Cleaning and Preparing Raw Data
2.5 Applying Data Transformation Techniques
2.6 Evaluating Data Quality for AI Solutions
3 Model Selection and Evaluation Techniques 6 classes
3.1 Distinguishing Models: An Introduction to Supervised and Unsupervised Learning
3.2 Exploring Model Selection: Criteria and Considerations
3.3 Applying Cross-Validation Techniques for Model Assessment
3.4 Evaluating Model Performance: Metrics and Interpretations
3.5 Selecting Optimal Models: Understanding Bias-Variance Trade-off
3.6 Implementing Model Evaluation: Techniques and Tools
4 Deployment and Integration of AI Models 6 classes
4.1 Exploring AI Model Deployment Environments
4.2 Preparing AI Models for Deployment
4.3 Implementing Continuous Integration and Continuous Deployment (CI/CD) in AI
4.4 Integrating AI Models with Existing Systems
4.5 Monitoring and Maintaining Deployed AI Models
4.6 Evaluating the Performance of Integrated AI Solutions
5 Monitoring and Optimizing AI Solutions 6 classes
5.1 Understanding Key Metrics for AI Monitoring
5.2 Implementing Continuous Performance Tracking
5.3 Analyzing AI Solution Data for Insights
5.4 Utilizing Feedback Loops to Optimize AI Models
5.5 Identifying and Mitigating AI Drift
5.6 Deploying Tools for Automated AI System Alerts
1 Overview of AI in Hospitality 6 classes
1.1 Introduction to AI Technologies
1.2 Historical Context of AI in Hospitality
1.3 Current Trends in AI Applications
1.4 Key AI Tools for Hospitality
1.5 AI Ethics in Hospitality
1.6 Future of AI in Hospitality
2 Optimizing Service Delivery 6 classes
2.1 AI-Driven Customer Insights
2.2 Personalization Techniques Using AI
2.3 Automation in Service Processes
2.4 Enhancing Guest Experience with AI
2.5 Real-Time Service Optimization
2.6 Feedback Mechanisms and AI
3 AI Workflow Management 6 classes
3.1 Designing AI Workflows
3.2 Integrating AI with Existing Systems
3.3 Monitoring AI Performance
3.4 Collaboration Tools for AI Teams
3.5 Managing AI Project Lifecycles
3.6 Troubleshooting AI Workflows
4 Risk Management in AI Solutions 6 classes
4.1 Identifying AI Risks
4.2 Mitigation Strategies for AI Risks
4.3 Compliance and Regulatory Considerations
4.4 Data Privacy and Security in AI
4.5 Crisis Management in AI Implementation
4.6 Evaluating Risk Management Frameworks
5 Evaluating AI Impact on Operations 6 classes
5.1 Measuring AI Success Metrics
5.2 Impact Assessment Techniques
5.3 ROI Analysis for AI Investments
5.4 Feedback Loop for Continuous Improvement
5.5 Case Studies on AI Impact
5.6 Strategic Planning Based on AI Insights
1 Vision and Strategic Planning 6 classes
1.1 Crafting a Compelling Vision
1.2 Setting Strategic Objectives
1.3 Analyzing Market Trends
1.4 Developing Actionable Strategies
1.5 Measuring Success Metrics
1.6 Adapting to Change
2 Inspiring Teams for Success 6 classes
2.1 Building Trust and Rapport
2.2 Empowering Team Members
2.3 Effective Communication Techniques
2.4 Recognizing and Rewarding Achievements
2.5 Fostering a Collaborative Environment
2.6 Navigating Conflict Resolution
3 Aligning Values and Mission 6 classes
3.1 Defining Core Values
3.2 Integrating Values into Culture
3.3 Communicating the Mission Effectively
3.4 Creating a Value-Driven Organization
3.5 Evaluating Mission Alignment
3.6 Incorporating Stakeholder Perspectives
4 Driving Growth and Sustainability 6 classes
4.1 Identifying Growth Opportunities
4.2 Sustainable Business Practices
4.3 Leveraging Technology for Growth
4.4 Measuring Long-Term Impact
4.5 Strategic Partnerships for Growth
4.6 Balancing Profit and Purpose
5 Cultivating Innovation and Improvement 6 classes
5.1 Encouraging Creative Thinking
5.2 Implementing Continuous Improvement
5.3 Utilizing Feedback for Growth
5.4 Exploring Emerging Technologies
5.5 Creating an Innovation Culture
5.6 Measuring Innovation Outcomes
16 Responsible and Ethical AI Governance in Practice 10 classes
16.1 Principles of Responsible AI
16.2 Bias and Fairness in Everyday AI Tools
16.3 Privacy and Consent When Using AI
16.4 Transparency and Explainability
16.5 Accountability and Human Oversight
16.6 Writing a Simple AI Use Policy
16.7 AI Impact Assessment Steps
16.8 Logging and Auditing AI Decisions
16.9 Handling AI Incidents and Complaints
16.10 Reviewing an AI Policy for Compliance
17 Generative AI Tools for Workplace Productivity 10 classes
17.1 What Generative AI Can and Cannot Do
17.2 Choosing the Right Tool for a Task
17.3 Writing Clear Prompts
17.4 Drafting Documents and Emails with AI
17.5 Summarising Reports and Meetings
17.6 Generating Ideas and Outlines
17.7 Checking AI Output for Accuracy
17.8 Avoiding Confidential Data in Prompts
17.9 Citing and Disclosing AI Assistance
17.10 Building a Personal AI Workflow
18 Planning and Delivering an AI Pilot Project 10 classes
18.1 Selecting a Suitable AI Use Case
18.2 Defining Success Measures
18.3 Assessing Data Readiness
18.4 Estimating Cost and Effort
18.5 Choosing Between Build and Buy
18.6 Assembling the Pilot Team
18.7 Running the Pilot in Stages
18.8 Gathering User Feedback
18.9 Evaluating Pilot Results
18.10 Deciding to Scale, Adjust or Stop
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