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

Introduction to Applied Machine Learning

• Statistical learning versus Machine Learning
• Iterative processes and evaluation
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Challenges addressed through Machine Learning
• Training, Validation, and Testing – The ML workflow to prevent overfitting
• The Machine Learning Workflow
• Overview of Machine Learning algorithms
• Selecting the appropriate algorithm for a given problem

Assessing Algorithm Performance

• Evaluating numerical predictions
  • Accuracy metrics: ME, MSE, RMSE, MAPE
  • Stability of parameters and predictions
• Evaluating classification algorithms
  • Accuracy and its limitations
  • The confusion matrix
  • Challenges with imbalanced classes
• Visualizing model performance
  • Profit curves
  • ROC curves
  • Lift curves
• Model selection strategies
• Model tuning – Grid search approaches

Data Preparation for Modeling

• Importing and storing data
• Understanding the data – Basic explorations
• Manipulating data using the pandas library
• Data transformations – Data wrangling
• Exploratory data analysis
• Handling missing observations – Detection and solutions
• Managing outliers – Detection and 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

• Defining 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
• Exploring 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
• Defining the Network Architecture
• Applying the Cost Function
• Applying the Optimizer
• Applying Initializers
• Fitting the Neural Network
• Constructing the Graph
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Training Loop
• Evaluating the Model
  • Constructing the Evaluation Graph
  • Evaluating with Evaluation Output
• Training Models at Scale
• Visualizing and Assessing 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
  • Approaches to Anomaly Detection using GANs

Ensemble Frameworks

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