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

Introduction to Applied Machine Learning

• Statistical learning vs. Machine learning.
• Iteration and evaluation processes.
• The Bias-Variance trade-off.
• Supervised vs Unsupervised Learning.
• Problems addressed by Machine Learning.
• Train, Validation, and Test splits – The ML workflow to prevent overfitting.
• The Machine Learning workflow.
• Overview of Machine learning algorithms.
• Selecting the appropriate algorithm for specific problems.

Algorithm Evaluation

• Evaluating numerical predictions.
  • Accuracy measures: ME, MSE, RMSE, MAPE.
  • Parameter and prediction stability.
• Assessing classification algorithms.
  • Accuracy and its limitations.
  • The confusion matrix.
  • Handling unbalanced class problems.
• Visualizing model performance.
  • Profit curve.
  • ROC curve.
  • Lift curve.
• Model selection strategies.
• Model tuning – grid search strategies.

Data preparation for Modelling

• Data import and storage techniques.
• Understanding the data – basic explorations.
• Data manipulations using the pandas library.
• Data transformations – Data wrangling.
• Exploratory data analysis.
• Missing observations – detection and remediation.
• Outliers – detection and handling 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 basic Deep Learning concepts.
• Differentiating between Machine Learning and Deep Learning.
• Overview of Deep Learning applications.

Overview of Neural Networks

• Definition of Neural Networks.
• Neural Networks vs 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.
• Learning about Feedforward and Feedback Neural Networks.
• Understanding Forward Propagation and Back Propagation.

Building Simple Deep Learning Models with Keras

• Creating a Keras Model.
• Understanding the dataset.
• Specifying the Deep Learning Model.
• Compiling the Model.
• Fitting the Model.
• Working with Classification Data.
• Working with Classification Models.
• Utilizing the trained 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.
• Utilizing 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.
  • Train Loop.
• Evaluating the Model.
  • Building the Eval Graph.
  • Evaluating with Eval Output.
• Training Models at Scale.
• Visualizing and Evaluating Models with TensorBoard.

Application of Deep Learning in Anomaly Detection

• Autoencoder.
  • Encoder - Decoder Architecture.
  • Reconstruction loss.
• Variational Autoencoder.
  • Variational inference.
• Generative Adversarial Network.
  • Generator – Discriminator architecture.
  • Approaches to Anomaly Detection using GAN.

Ensemble Frameworks

• Combining results from different methods.
• Bootstrap Aggregating.
• Averaging outlier scores.