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

Introduction to Applied Machine Learning

• Statistical learning compared to Machine Learning
• Iterative processes and evaluation
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Problems addressed by Machine Learning
• Train, Validation, and Test – The ML workflow for preventing 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
• Evaluating classification algorithms
  • Accuracy and its associated challenges
  • The confusion matrix
  • The issue of unbalanced classes
• Visualizing model performance
  • Profit curves
  • ROC curves
  • Lift curves
• Model selection strategies
• Model tuning – Grid search approaches

Preparing Data for Modeling

• Data importation and storage
• Data comprehension – Basic explorations
• Data manipulation using the pandas library
• Data transformations – Data wrangling techniques
• Exploratory data analysis
• Handling missing observations – Detection and resolution
• Outliers – Identification 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

• Definition and nature of Neural Networks
• Neural Networks compared to Regression Models
• 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
• Forward Propagation and Back Propagation

Building Basic Deep Learning Models with Keras

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

Using TensorFlow for Deep Learning

• Data Preparation
  • 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
  • The Training Loop
• Evaluating the Model
  • Building the Evaluation Graph
  • Evaluating using Output Metrics
• Training Models at Scale
• Visualizing and Evaluating Models with TensorBoard

Applying Deep Learning to Anomaly Detection

• Autoencoders
  • Encoder - Decoder Architecture
  • Reconstruction loss
• Variational Autoencoders
  • Variational inference
• Generative Adversarial Networks (GANs)
  • Generator – Discriminator architecture
  • Approaches to Anomaly Detection using GANs

Ensemble Frameworks

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