Fundamentals of Artificial Intelligence (AI) and Machine Learning Training Course

Introduction to Applied Machine Learning for Government

• Comparison of Statistical Learning and Machine Learning
• Iteration and Evaluation Processes in Machine Learning
• Understanding the Bias-Variance Trade-off
• Supervised versus Unsupervised Learning
• Common Problems Addressed by Machine Learning for Government
• Train, Validation, and Test Workflows to Prevent Overfitting in Machine Learning Models
• General Workflow of Machine Learning Projects
• Overview of Machine Learning Algorithms
• Selecting the Appropriate Algorithm for Specific Problems

Algorithm Evaluation for Government

• Evaluating Numerical Predictions
  • Measures of Accuracy: Mean Error (ME), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE)
  • Assessing Parameter and Prediction Stability
• Evaluating Classification Algorithms
  • Accuracy and Its Limitations
  • The Confusion Matrix and Its Components
  • Addressing Unbalanced Classes in Classification Problems
• Visualizing Model Performance
  • Profit Curves for Decision-Making
  • Receiver Operating Characteristic (ROC) Curves
  • Lift Curves to Measure Model Effectiveness
• Selecting the Best Model for Government Applications
• Tuning Models Using Grid Search Strategies

Data Preparation for Modelling in Government

• Importing and Storing Data for Analysis
• Basic Exploratory Data Analysis to Understand the Dataset
• Manipulating Data Using the Pandas Library
• Data Transformations and Wrangling Techniques
• Conducting Exploratory Data Analysis for Insight Discovery
• Detecting and Handling Missing Observations in Government Datasets
• Identifying and Addressing Outliers in Data
• Standardization, Normalization, and Binarization of Data
• Recoding Qualitative Data for Analysis

Machine Learning Algorithms for Outlier Detection in Government

• Supervised Algorithms
  • K-Nearest Neighbors (KNN)
  • Ensemble Gradient Boosting
  • Support Vector Machines (SVM)
• Unsupervised Algorithms
  • Distance-Based Methods
  • Density-Based Methods
  • Probabilistic Methods
  • Model-Based Methods

Understanding Deep Learning for Government

• Overview of Basic Concepts in Deep Learning
• Differentiating Between Machine Learning and Deep Learning for Government Applications
• Overview of Applications of Deep Learning in the Public Sector

Overview of Neural Networks for Government

• Introduction to Neural Networks
• Comparing Neural Networks with Regression Models
• Understanding the Mathematical Foundations and Learning Mechanisms of Neural Networks
• Constructing an Artificial Neural Network for Government Use
• Understanding Neural Nodes and Connections in Neural Networks
• Working with Neurons, Layers, and Input/Output Data in Neural Networks
• Understanding Single Layer Perceptrons
• Differences Between Supervised and Unsupervised Learning in the Context of Government Applications
• Learning About Feedforward and Feedback Neural Networks for Government Use
• Understanding Forward Propagation and Back Propagation in Neural Networks

Building Simple Deep Learning Models with Keras for Government

• Creating a Keras Model for Government Applications
• Understanding Your Data for Effective Modeling
• Specifying the Architecture of Your Deep Learning Model
• Compiling Your Model for Efficient Training
• Fitting Your Model to Government Datasets
• Working with Classification Data in Government Applications
• Building and Evaluating Classification Models for Government Use
• Deploying and Using Your Deep Learning Models in the Public Sector

Working with TensorFlow for Deep Learning in Government

• Preparing Data for Deep Learning Models
  • Downloading and Importing Data
  • Preparing Training Data for Model Training
  • Preparing Test Data for Model Evaluation
  • Scaling Inputs to Enhance Model Performance
  • Using Placeholders and Variables in TensorFlow
• Specifying the Network Architecture for Government Models
• Utilizing Cost Functions in Deep Learning Models
• Using Optimizers to Improve Model Performance
• Applying Initializers to Neural Networks
• Fitting the Neural Network to Government Data
• Building the Computational Graph in TensorFlow
  • Inference Processes in Deep Learning Models
  • Loss Functions for Model Evaluation
  • Training Procedures for Neural Networks
• Training the Model for Government Applications
  • Constructing the Computational Graph
  • Running Sessions to Train Models
  • Implementing the Training Loop for Efficient Learning
• Evaluating the Performance of Deep Learning Models
  • Building the Evaluation Graph in TensorFlow
  • Assessing Model Performance with Evaluation Output
• Training Models at Scale for Government Use
• Visualizing and Evaluating Models with TensorBoard

Application of Deep Learning in Anomaly Detection for Government

• Autoencoder Techniques for Anomaly Detection
  • Encoder-Decoder Architecture for Data Reconstruction
  • Using Reconstruction Loss to Identify Anomalies
• Variational Autoencoders for Advanced Anomaly Detection
  • Applying Variational Inference in Anomaly Detection Models
• Generative Adversarial Networks (GANs) for Robust Anomaly Detection
  • Generator-Discriminator Architecture for Anomaly Identification
  • Approaches to Anomaly Detection Using GANs

Ensemble Frameworks for Government

• Combining Results from Multiple Methods to Improve Accuracy
• Bootstrap Aggregating (Bagging) Techniques
• Averaging Outlier Scores for Robust Detection