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

Introduction to Applied Machine Learning

• Statistical learning compared with Machine learning
• Iterative processes and evaluation methods
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Challenges addressed through Machine Learning
• Train, Validation, and Test sets – an ML workflow to prevent overfitting
• The Machine Learning workflow
• Various Machine learning algorithms
• Selecting the appropriate algorithm for a specific problem

Algorithm Evaluation

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

Data preparation for Modelling

• Importing and storing data
• Understanding the data – initial explorations
• Manipulating data using the pandas library
• Data transformations – Data wrangling
• Conducting exploratory analysis
• Missing observations – identification and remediation
• Outliers – detection and handling strategies
• Standardisation, normalisation, and binarisation
• Recoding qualitative data

Machine learning algorithms for Outlier detection

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

Understanding Deep Learning

• Overview of fundamental Deep Learning concepts
• Distinguishing between Machine Learning and Deep Learning
• Overview of Deep Learning applications

Overview of Neural Networks

• What are Neural Networks?
• Exploring 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
• Learning about Feedforward and Feedback Neural Networks

Building Simple Deep Learning Models with Keras

• Creating a Keras Model
• Understanding Your Data
• Defining Your Deep Learning Model
• Compiling Your Model
• Fitting Your Model
• Handling Classification Data
• Applying 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
• Specifying the Network Architecture
• Implementing the Cost Function
• Applying Initializers
• Fitting the Neural Network
• Building the Graph
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluating the Model
  • Building the Eval Graph
  • Evaluating with Eval Output
• Training Models at Scale
• Visualizing and Evaluating Models with TensorBoard

Application of Deep Learning in Anomaly Detection

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

Ensemble Frameworks

• Combining results from different methods
• Bootstrap Aggregating
• Averaging outlier scores