Introduction to Neural Networks for Customer Experience (CX) Professionals 

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. 

• 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.

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.

• 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