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

Introduction to Applied Machine Learning

• Statistical learning versus Machine Learning
• Iteration and model evaluation
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Challenges addressed through Machine Learning
• Train, Validation, and Test splits – ML workflows to prevent overfitting
• The Machine Learning workflow
• Overview of Machine learning algorithms
• Selecting the appropriate algorithm for a specific problem

Algorithm Assessment

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

Data Preparation for Modeling

• Data ingestion and storage
• Understanding the data – initial explorations
• Data manipulation using the pandas library
• Data transformations – Data wrangling techniques
• Exploratory data analysis
• Handling missing observations – detection and solutions
• Outliers – identification and strategic handling
• 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
• Summary of Deep Learning Applications

Neural Networks Overview

• Definition and scope of Neural Networks
• Neural Networks versus 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
• Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Backpropagation

Building Basic Deep Learning Models with Keras

• Creating a Keras Model
• Analyzing Your Data
• Defining Your Deep Learning Model
• Compiling the Model
• Fitting the Model
• Handling Classification Data
• Utilizing Classification Models
• Deploying Your Models

Using TensorFlow for Deep Learning

• Data Preparation
  • Downloading Datasets
  • Preparing Training Data
  • Preparing Test Data
  • Scaling Inputs
  • Utilizing Placeholders and Variables
• Defining the Network Architecture
• Implementing the Cost Function
• Selecting the Optimizer
• Applying Initializers
• Fitting the Neural Network
• Constructing the Graph
  • Inference
  • Loss Calculation
  • Training Process
• Model Training
  • The Graph Structure
  • The Session
  • Training Loop
• Model Evaluation
  • Building the Evaluation Graph
  • Assessing with Eval Output
• Scaling Model Training
• 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 (GANs)
  • Generator–Discriminator Architecture
  • Anomaly Detection approaches using GANs

Ensemble Frameworks

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