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

Introduction to Applied Machine Learning

• Distinguishing Statistical Learning from Machine Learning
• The Process of Iteration and Evaluation
• The Bias-Variance Trade-off
• Supervised vs. Unsupervised Learning
• Problem Solving via Machine Learning
• Train, Validation, and Test Splits – ML Workflows to Prevent Overfitting
• The Machine Learning Workflow
• Overview of Machine Learning Algorithms
• Selecting the Right Algorithm for Specific Problems

Evaluating Algorithms

• Assessing Numerical Predictions
  • Accuracy Metrics: ME, MSE, RMSE, MAPE
  • Stability of Parameters and Predictions
• Assessing Classification Algorithms
  • Accuracy and Associated Challenges
  • Utilizing the Confusion Matrix
  • Handling Unbalanced Classes
• Visualizing Model Performance
  • Profit Curves
  • ROC Curves
  • Lift Curves
• Model Selection Strategies
• Model Tuning – Grid Search Techniques

Data Preparation for Modeling

• Data Import and Storage Mechanisms
• Understanding Data – Initial Explorations
• Manipulating Data with the pandas Library
• Data Transformations – Data Wrangling
• Exploratory Data Analysis
• Missing Observations – Detection and Resolution
• Outliers – Identification and Strategic Handling
• Standardization, Normalization, and Binarization
• Recoding Qualitative Data

Machine Learning Algorithms for Outlier Detection

• Supervised Algorithms
  • K-Nearest Neighbors (KNN)
  • Ensemble Gradient Boosting
  • Support Vector Machines (SVM)
• Unsupervised Algorithms
  • Distance-Based Methods
  • Density-Based Methods
  • Probabilistic Methods
  • Model-Based Methods

Understanding Deep Learning

• Overview of Basic Deep Learning Concepts
• Differentiating Between Machine Learning and Deep Learning
• Surveying Applications of Deep Learning

Overview of Neural Networks

• Defining Neural Networks
• Neural Networks vs. Regression Models
• Comprehending Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Their Connections
• Working with Neurons, Layers, and Input/Output Data
• Understanding Single Layer Perceptrons
• Distinctions Between Supervised and Unsupervised Learning
• Learning Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Back Propagation

Building Simple 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
• Working with Classification Models
• Deploying Your Models

Working with TensorFlow for Deep Learning

• Data Preparation
  • Acquiring the Data
  • Preparing Training Data
  • Preparing Test Data
  • Scaling Input Values
  • Using Placeholders and Variables
• Specifying the Network Architecture
• Implementing the Cost Function
• Selecting the Optimizer
• Applying Initializers
• Fitting the Neural Network
• Building the Graph
  • Inference
  • Loss Calculation
  • Training Process
• Training the Model
  • The Graph Structure
  • The Session
  • Training Loop
• Evaluating the Model
  • Constructing the Evaluation Graph
  • Assessing Results via Evaluation Output
• Training Models at Scale
• Visualizing and Evaluating Models using TensorBoard

Application of Deep Learning in Anomaly Detection

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

Ensemble Frameworks

• Aggregating Results from Diverse Methods
• Bootstrap Aggregating
• Averaging Outlier Scores