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

Introduction to Applied Machine Learning

• Distinguishing statistical learning from machine learning.
• ​Concepts of iteration and model evaluation.
• ​The Bias-Variance trade-off.
• ​Comparing Supervised and Unsupervised Learning.
• ​Types of problems effectively solved by Machine Learning.
• ​Train, Validation, and Test splits – the ML workflow for preventing overfitting.
• ​Understanding the end-to-end Machine Learning workflow.
• ​An overview of common Machine learning algorithms.
• ​Strategies for selecting the most appropriate algorithm for a given problem.

Algorithm Evaluation

• Assessing numerical predictions
  • Accuracy metrics: ME, MSE, RMSE, MAPE.
  • ​Evaluating parameter and prediction stability.
• ​Evaluating classification algorithms
  • Accuracy and its associated limitations.
  • ​Utilizing the confusion matrix.
  • ​Addressing the unbalanced classes problem.
• ​Visualizing model performance
  • Profit curves.
  • ​ROC curves.
  • ​Lift curves.
• ​Model selection techniques.
• ​Model tuning – grid search strategies.

Data preparation for Modelling

• Importing and storing data.
• ​Understanding the data – initial explorations.
• ​Performing data manipulations with the pandas library.
• ​Data transformations – Data wrangling.
• ​Conducting Exploratory analysis.
• ​Handling missing observations – detection and remediation.
• ​Outliers – identification 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

• An overview of Deep Learning's basic concepts.
• ​Differentiating between Machine Learning and Deep Learning.
• ​A summary of Deep Learning applications.

Overview of Neural Networks

• Defining Neural Networks.
• ​Comparing Neural Networks with Regression Models.
• ​Understanding the Mathematical Foundations and Learning Mechanisms.
• ​Constructing an Artificial Neural Network.
• ​Explaining 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 Your Data.
• ​Specifying Your Deep Learning Model.
• ​Compiling Your Model.
• ​Fitting Your Model.
• ​Working with Your Classification Data.
• ​Working with Classification Models.
• ​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.
• ​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.
  • ​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 Autencoder
  • Variational inference.
• ​Generative Adversarial Network
  • Generator – Discriminator architecture.
  • ​Approaches to AN using GAN.

Ensemble Frameworks

• Combining results from different methods.
• ​Bootstrap Aggregating.
• ​Averaging outlier score.