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

Introduction to Applied Machine Learning

• Statistical learning versus Machine learning
• Iterative processes and evaluation
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Problems addressed by Machine Learning
• Train, Validation, Test – ML workflows to prevent overfitting
• Machine Learning workflows
• Machine learning algorithms
• Selecting the appropriate algorithm for specific problems

Algorithm Evaluation

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

Data Preparation for Modelling

• Data import and storage
• Understanding the data – initial exploration
• Data manipulation using the pandas library
• Data transformations – Data wrangling
• Exploratory data analysis
• Missing observations – identification and resolution
• Outliers – detection and management strategies
• Standardization, normalization, and binarization
• Recoding 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 Fundamental Deep Learning Concepts
• Distinguishing Between Machine Learning and Deep Learning
• Overview of Deep Learning Applications

Overview of Neural Networks

• Defining Neural Networks
• Neural Networks compared to Regression Models
• Understanding 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
• Understanding Forward Propagation and Back Propagation

Building Simple Deep Learning Models with Keras

• Creating a Keras Model
• Understanding Your Dataset
• Defining Your Deep Learning Model
• Compiling Your Model
• Fitting Your Model
• Working with Classification Data
• Working with Classification Models
• Utilizing Your Models

Working with TensorFlow for Deep Learning

• Data Preparation
  • Data Downloading
  • Preparing Training Data
  • Preparing Test Data
  • Input Scaling
  • Using Placeholders and Variables
• Defining the Network Architecture
• Using the Cost Function
• Using the Optimizer
• Using 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

Deep Learning Applications in Anomaly Detection

• Autoencoders
  • Encoder-Decoder Architecture
  • Reconstruction Loss
• Variational Autoencoders
  • Variational Inference
• Generative Adversarial Networks
  • Generator – Discriminator Architecture
  • Approaches to AN using GANs

Ensemble Frameworks

• Combining results from diverse methods
• Bootstrap Aggregating (Bagging)
• Averaging outlier scores