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

Introduction to Applied Machine Learning

• Statistical learning compared with Machine Learning
• Iterative processes and evaluation techniques
• The Bias-Variance trade-off
• Supervised versus Unsupervised Learning
• Problems addressed through Machine Learning
• Train, Validation, and Test sets – The ML workflow designed to prevent overfitting
• The complete Machine Learning workflow
• Overview of Machine learning algorithms
• Selecting the most suitable algorithm for specific problems

Algorithm Evaluation

• Assessing numerical predictions
  • Accuracy metrics: ME, MSE, RMSE, MAPE
  • Stability of parameters and predictions
• Assessing classification algorithms
  • Accuracy and its inherent limitations
  • The confusion matrix
  • Challenges associated with unbalanced classes
• Visualizing model performance
  • Profit curve
  • ROC curve
  • Lift curve
• Strategies for Model selection
• Model tuning using grid search strategies

Data preparation for Modelling

• Data import and storage mechanisms
• Data comprehension – fundamental exploratory steps
• Data manipulation utilizing the pandas library
• Data transformations – Data wrangling techniques
• Conducting Exploratory analysis
• Handling Missing observations – detection and resolution strategies
• Identifying Outliers – detection methods 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 approaches
  • Density based methods
  • Probabilistic methods
  • Model based methods

Understanding Deep Learning

• Overview of the Basic Concepts of Deep Learning
• Distinguishing Between Machine Learning and Deep Learning
• Overview of Deep Learning Applications

Overview of Neural Networks

• Defining Neural Networks
• Neural Networks compared with Regression Models
• Grasp Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Comprehending 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 Your Data
• Defining Your Deep Learning Model
• Compiling the Model
• Fitting the Model
• Processing Classification Data
• Working with Classification Models
• Utilizing the Trained Models

Working with TensorFlow for Deep Learning

• Data Preparation
  • Acquiring the Data
  • 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 Process
• Training the Model
  • Graph Structure
  • Session Management
  • Train Loop Implementation
• Assessing the Model
  • Creating the Evaluation Graph
  • Evaluating using 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

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