WA3316
Introduction to Machine Learning with Neural Networks Training
This Machine Learning (ML) training course teaches attendees the concepts, terminology, and practice of ML using neural networks. Students learn the architecture of artificial neural networks, activation functions, and more.
Course Details
Duration
1 day
Prerequisites
- Familiarity with Python and
- Foundational understanding of linear algebra and calculus
Target Audience
- Data engineers
- Business analysts
- Data scientists
- Software developers
- IT architects
- Technical managers
Skills Gained
- Understand the concepts and terminology of machine learning with neural networks
- Configure, train, and use neural networks
- Gain practical experience using Keras
Course Outline
- Machine Learning with Neural Networks
- Arthur C. Clarke’s 3rd Law
- What is Machine Learning?
- Terminology: Features and Targets
- Terminology: Observations (Examples)
- Supervised and Unsupervised ML
- “Classical” ML and ML with Neural Networks
- The Shared Concepts and Principles
- AI and Data Science
- What is a Neural Network?
- Network vs Model
- Positional Types of Layers
- Deep Learning
- How Does My Network Know Which Problem I Want It to Solve?
- The Desired Model Properties
- The Artificial Neuron
- Perceptron
- The Perceptron Symbol
- A Breakthrough in Neural Networks Design
- Perceptrons and MLPs
- A Basic Neural Network Example
- Popular Activation Functions
- Navigating Neural Networks Layers
- A Sample Neural Network Diagram
- Model Training
- Measuring the Error with the Loss (Cost) Function
- Loss Function Properties
- Mini-batches and Epochs
- Neural Networks Training Steps
- The Chain Rule in Calculus
- The Chain Rule in Neural Networks
- The Gradient Descent Formulation
- Applying Efficiencies with Autodiff ...
- Types of Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- RNN Common and Unrolled Visual Representation
- Autoencoders
- Neural Network Libraries and Frameworks
- Ethical AI
- Then a Miracle Occurs ...
- Machine Learning Concepts and Terminology
- Supervised and Unsupervised ML
- Self-Supervised Learning
- Terminology: Features and Targets
- Terminology: Observations (Examples)
- Notation for Observations
- Data Structures: Tensors
- Continuous and Categorical Features
- Continuous Features
- Categorical Features
- Feature Types Visually
- Common Distance Metrics
- The Euclidean Distance
- What is a Model?
- Model Parameters and Hyperparameters
- Model Accuracy
- Loss Functions
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- (Categorical) Cross Entropy Loss
- The Cross Entropy Loss Visually
- The softmax Function
- Confusion Matrix
- The Binary Classification Confusion Matrix
- Multi-class Classification Confusion Matrix Example
- Feature Engineering
- Data Scaling and Normalization
- Bias-Variance (Underfitting vs Overfitting) Trade-off
- Bias and Variance Visually
- Underfitting vs Overfitting Visually
- Ways to Balance Off the Bias-Variance Ratio
- Regularization
- Dimensionality Reduction
- Model Validation and Avoiding Test Data Leakage
- Training Error vs Validation Error Diagram
- Training/Validation/Test Data Split Ratios
- Online Glossaries
- Introduction to Keras
- What is Keras?
- Core Keras Data Structures
- Layers in Keras
- The Dense Layer
- Defining the Layer Activation Function
- Models in Keras
- Components of a Keras Model
- Creating Neural Networks in Keras
- The Sequential Model
- A Sequential Model Code Example
- The Strengths and Weaknesses of Sequential Models
- The Functional API
- A Functional API Example
- The Strengths and Weaknesses of the Functional API
- Making New Layers and Models via Subclassing
- The Strengths and Weaknesses of Subclassing
