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

Introduction to Applied Machine Learning

• Statistical learning compared with Machine Learning
• Iteration and evaluation processes
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Challenges addressed through Machine Learning
• Train, Validation, and Test splits – The ML workflow for preventing overfitting
• The standard Machine Learning workflow
• Overview of Machine Learning algorithms
• Selecting the optimal 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
  • Interpretation of the confusion matrix
  • Handling the unbalanced classes problem
• Visualizing model performance
  • Profit curve
  • ROC curve
  • Lift curve
• Model selection strategies
• Model tuning via grid search strategies

Data Preparation for Modeling

• Data import and storage mechanisms
• Understanding the data through basic explorations
• Data manipulation using the pandas library
• Data transformations and data wrangling
• Conducting exploratory analysis
• Detecting and resolving missing observations
• Identifying outliers and applying appropriate strategies
• Standardization, normalization, and binarization techniques
• 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
• Comprehending mathematical foundations and learning mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Connections
• Managing 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 Back Propagation

Building Simple Deep Learning Models with Keras

• Initializing a Keras Model
• Analyzing and Understanding Your Data
• Defining the Deep Learning Model structure
• Compiling the Model
• Fitting the Model to data
• Processing Classification Data
• Developing Classification Models
• Deploying and Using Your Models

Working with TensorFlow for Deep Learning

• Data Preparation
  • Data acquisition
  • Preparing training datasets
  • Preparing test datasets
  • Input scaling
  • 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 operations
  • Loss calculation
  • Training operations
• Model Training Process
  • Graph configuration
  • Session management
  • Training Loop implementation
• Model Evaluation
  • Building the Evaluation Graph
  • Assessing performance with Evaluation Output
• Scaling Model Training
• Visualizing and Evaluating Models using TensorBoard

Application of Deep Learning in Anomaly Detection

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

Ensemble Frameworks

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