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

Introduction to Applied Machine Learning

• Statistical learning versus Machine learning
• Iteration and evaluation processes
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Problems addressed via Machine Learning
• Train, Validation, and Test – the ML workflow to prevent overfitting
• The Machine Learning workflow
• Machine learning algorithms
• Selecting the appropriate algorithm for the problem

Algorithm Evaluation

• Assessing numerical predictions
  • Accuracy metrics: ME, MSE, RMSE, MAPE
  • Stability of parameters and predictions
• Evaluating classification algorithms
  • Accuracy and associated limitations
  • The confusion matrix
  • Handling unbalanced classes
• Visualising model performance
  • Profit curve
  • ROC curve
  • Lift curve
• Model selection strategies
• Model tuning – grid search techniques

Data preparation for Modelling

• Data import and storage
• Understanding the data – initial explorations
• Data manipulation using the pandas library
• Data transformations – Data wrangling
• Exploratory analysis
• Missing observations – identification and resolution
• Outliers – detection and management strategies
• Standardisation, normalisation, and binarisation
• Recoding of qualitative data

Machine learning algorithms for Outlier detection

• Supervised algorithms
  • KNN
  • Ensemble Gradient Boosting
  • SVM
• Unsupervised algorithms
  • Distance-based methods
  • Density-based methods
  • Probabilistic methods
  • Model-based methods

Understanding Deep Learning

• Overview of Basic Deep Learning Concepts
• Distinguishing Between Machine Learning and Deep Learning
• Overview of Deep Learning Applications

Overview of Neural Networks

• Definition of Neural Networks
• Neural Networks versus Regression Models
• Comprehending Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Connections
• Working with Neurons, Layers, and Input/Output Data
• Understanding Single Layer Perceptrons
• Differences Between Supervised and Unsupervised Learning
• Studying Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Back Propagation

Building Simple Deep Learning Models with Keras

• Creating a Keras Model
• Understanding Your Data
• Specifying Your Deep Learning Model
• Compiling Your Model
• Fitting Your Model
• Working with Classification Data
• Working with Classification Models
• Deploying Your Models

Working with TensorFlow for Deep Learning

• Preparing the Data
  • Downloading the Data
  • Preparing Training Data
  • Preparing Test Data
  • Scaling Inputs
  • Using Placeholders and Variables
• Defining the Network Architecture
• Implementing the Cost Function
• Utilising the Optimizer
• Using Initializers
• Fitting the Neural Network
• Building the Graph
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • The Training Loop
• Evaluating the Model
  • Constructing the Evaluation Graph
  • Evaluating using Evaluation Output
• Training Models at Scale
• Visualising and Evaluating Models with TensorBoard

Application of Deep Learning in Anomaly Detection

• Autoencoder
  • Encoder - Decoder Architecture
  • Reconstruction loss
• Variational Autoencoder
  • Variational inference
• Generative Adversarial Network
  • Generator – Discriminator architecture
  • Approaches to Anomaly Detection using GAN

Ensemble Frameworks

• Aggregating results from various methods
• Bootstrap Aggregating
• Averaging outlier scores