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 by Machine Learning
• Train-Validation-Test split – ML workflow to prevent overfitting
• General Machine Learning workflows
• Core Machine Learning algorithms
• Selecting the appropriate algorithm for specific problems

Algorithm Evaluation

• Assessing numerical predictions
  • Accuracy measures: ME, MSE, RMSE, MAPE
  • Parameter and prediction stability
• Evaluating classification algorithms
  • Accuracy and its limitations
  • The confusion matrix
  • Handling unbalanced classes
• Visualizing model performance
  • Profit curve
  • ROC curve
  • Lift curve
• Model selection strategies
• Model tuning – grid search approaches

Data Preparation for Modeling

• Data import and storage mechanisms
• Understanding data – basic explorations
• Data manipulation using the pandas library
• Data transformations – Data wrangling
• Exploratory data analysis
• Missing observations – detection and remedies
• Outliers – detection and handling 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

Neural Networks Overview

• Definition of 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 Backpropagation

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 and Using 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
• 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 in Anomaly Detection

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

Ensemble Frameworks

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