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 through Machine Learning
• Train, Validation, Test – The ML workflow to prevent overfitting
• Machine Learning workflow
• Various Machine Learning algorithms
• Selecting the appropriate algorithm for specific problems

Evaluating Algorithms

• Assessing numerical predictions
  • Accuracy measures: ME, MSE, RMSE, MAPE
  • Stability of parameters and predictions
• Assessing classification algorithms
  • Accuracy and associated limitations
  • Utilization of the confusion matrix
  • Handling imbalanced classes
• Visualizing model performance
  • Profit curves
  • ROC curves
  • Lift curves
• Model selection strategies
• Model tuning – Grid search techniques

Data Preparation for Modelling

• Data import and storage mechanisms
• Understanding data – Fundamental explorations
• Manipulating data using the pandas library
• Data transformations – Data wrangling
• Exploratory analysis
• Missing observations – Identification and remediation
• 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
• Survey of Deep Learning applications

Neural Networks Overview

• Defining Neural Networks
• Neural Networks versus Regression Models
• Grasping mathematical foundations and learning mechanisms
• Constructing Artificial Neural Networks
• Understanding Neural Nodes and Connections
• Working with Neurons, Layers, and Input/Output Data
• Understanding Single Layer Perceptrons
• Differences between Supervised and Unsupervised Learning
• Feedforward and Feedback Neural Networks
• Forward Propagation and Back Propagation mechanisms

Developing Simple Deep Learning Models with Keras

• Creating a Keras Model
• Analyzing and understanding your data
• Defining your Deep Learning Model
• Compiling the Model
• Fitting the Model
• Processing Classification Data
• Working with Classification Models
• Deploying and Using Models

Utilizing TensorFlow for Deep Learning

• Data Preparation
  • Downloading data sets
  • Preparing training data
  • Preparing test data
  • Scaling inputs
  • Using Placeholders and Variables
• Defining the Network Architecture
• Implementing the Cost Function
• Applying the Optimizer
• Using Initializers
• Fitting the Neural Network
• Constructing the Graph
  • Inference
  • Loss calculation
  • Training process
• Training the Model
  • The Graph structure
  • The Session
  • Training Loop
• Evaluating the Model
  • Building the Evaluation Graph
  • Evaluating with 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 Anomaly Detection using GANs

Ensemble Frameworks

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