Introduction to Neural Networks for Customer Experience (CX) Professionals
Course Overview:
This course equips Customer Experience (CX) and Customer Service Management (CSM) professionals with the foundational knowledge of Neural Networks, a core technology driving Artificial Intelligence (AI). You'll explore the fundamentals of how neural networks learn and process information, uncovering their potential to revolutionize customer interactions and experiences within your organization.
Learning Objectives:
Explain the core concepts of Neural Networks and their role in AI applications.
Understand the basic structure and function of artificial neurons within a neural network.
Identify different types of neural networks relevant to CX applications (e.g., Perceptrons, Convolutional Neural Networks).
Explore how neural networks learn and adapt through training with data, uncovering the concept of backpropagation.
Evaluate the potential applications of neural networks in tasks like image recognition, sentiment analysis, and personalized recommendations for improved CX.
Course Highlights:
1. Demystifying the Neural Network Hype:
Introduction to Artificial Intelligence (AI): Understanding the broader context of neural networks within the AI landscape.
Unveiling the Neural Network: Exploring the basic structure and function of artificial neurons, the building blocks of neural networks.
Learning from Examples: Understanding how neural networks learn through supervised learning and the concept of training data.
Case Study: Utilizing neural networks for image recognition in a mobile app to enhance product search experiences for customers.
Hands-on Session: Working with a user-friendly platform to visualize and interact with a simple neural network model.
2. Unveiling Different Neural Network Architectures:
Perceptrons: Exploring the foundational building block of neural networks and their limitations.
Activation Functions: Demystifying the role of activation functions in introducing non-linearity to neural networks.
Beyond Perceptrons: Introducing different neural network architectures like Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs) for complex tasks.
Guest Speaker Session: Inviting a data scientist or AI engineer to discuss real-world applications of neural networks in the CX field.
Group Discussion: Brainstorming potential applications of different neural network architectures for specific CX challenges within your department.
3. The Power of Learning: Backpropagation and Training Neural Networks:
The Learning Process: Understanding the concept of backpropagation, a crucial algorithm for training neural networks.
Optimizing Performance: Exploring different optimization algorithms used to fine-tune neural network performance during training.
Data Considerations for Neural Networks: Highlighting the importance of high-quality and relevant data for effective training of neural networks in CX applications.
Interactive Workshop: Experimenting with training a simple neural network model on a sample customer dataset using a user-friendly platform.
Project Planning: Developing a project plan outlining how you can leverage neural networks for a specific CX challenge in your role.
4. The Future of Neural Networks and Responsible AI in CX:
Emerging Trends in Neural Networks: Exploring advancements in neural network architectures and their potential future applications in customer experience.
Limitations and Challenges: Discussing the limitations of neural networks and potential challenges in their implementation for CX tasks.
Responsible AI for CX with Neural Networks: Developing strategies for responsible use of neural networks, considering fairness, bias, and explainability in CX initiatives.
Course Wrap-up & Project Presentations: Teams present their project plans, outlining the chosen neural network application for CX, responsible implementation strategies, and potential impact.
Resource Sharing: Discussing best practices and ongoing learning opportunities for staying up-to-date with neural network advancements in the CX field.
Prerequisites:
Strong understanding of linear algebra, calculus, and probability theory
Proficiency in programming with Python and libraries such as NumPy and Matplotlib
Familiarity with basic machine learning concepts and techniques