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 techniques
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Challenges addressed by Machine Learning
• Train, Validation, and Test splits: The ML workflow for preventing overfitting
• The Machine Learning workflow
• Overview of Machine Learning algorithms
• Selecting the appropriate algorithm for specific problems

Algorithm Evaluation

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

Data Preparation for Modelling

• Data importation and storage techniques
• Understanding the data through basic exploration
• Data manipulation using the pandas library
• Data transformations and data wrangling
• Exploratory Data Analysis
• Detecting and handling missing observations
• Identifying outliers and mitigation 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 approaches
  • Density-based methods
  • Probabilistic methods
  • Model-based methods

Understanding Deep Learning

• Overview of fundamental Deep Learning concepts
• Distinguishing between Machine Learning and Deep Learning
• Survey of Deep Learning applications

Overview of Neural Networks

• Defining 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 Backpropagation concepts

Building Simple Deep Learning Models with Keras

• Creating a Keras Model
• Analyzing and understanding your data
• Specifying the Deep Learning Model
• Compiling the Model
• Fitting the Model
• Handling Classification Data
• Implementing Classification Models
• Utilizing the trained models

Working with TensorFlow for Deep Learning

• Data Preparation
  • Data acquisition
  • Preparing Training Data
  • Preparing Test Data
  • Scaling Inputs
  • Utilizing Placeholders and Variables
• Defining the Network Architecture
• Implementing the Cost Function
• Applying the Optimizer
• Using Initializers
• Fitting the Neural Network
• Constructing the Graph
  • Inference
  • Loss calculation
  • Training steps
• Training the Model
  • Graph configuration
  • Session management
  • Training loop implementation
• Model Evaluation
  • Building the Evaluation Graph
  • Interpreting Evaluation Outputs
• 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 (GAN)
  • Generator – Discriminator architecture
  • Anomaly Detection approaches using GANs

Ensemble Frameworks

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