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

Introduction to Applied Machine Learning

• Comparative analysis of statistical learning and machine learning
• Processes of iteration and evaluation
• The Bias-Variance trade-off
• Distinctions between Supervised and Unsupervised Learning
• Challenges addressed through Machine Learning
• Train Validation Test – ML workflow strategies to prevent overfitting
• The standard Machine Learning workflow
• Various machine learning algorithms
• Selecting the most suitable algorithm for a specific problem

Algorithm Evaluation

• Assessing numerical predictions
  • Accuracy metrics: ME, MSE, RMSE, MAPE
  • Stability of parameters and predictions
• Evaluating classification algorithms
  • Accuracy and associated limitations
  • Utilization of the confusion matrix
  • Addressing the issue of unbalanced classes
• Visualizing model performance
  • Profit curve
  • ROC curve
  • Lift curve
• Model selection techniques
• Model tuning via grid search strategies

Data Preparation for Modelling

• Data import and storage mechanisms
• Data comprehension – foundational explorations
• Data manipulation using the pandas library
• Data transformations – Data wrangling techniques
• Conducting exploratory analysis
• Handling missing observations – detection and remediation
• Managing outliers – detection and strategic approaches
• 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 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 compared to Regression Models
• Grasping Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Connections
• Handling Neurons, Layers, and Input/Output Data
• Understanding Single Layer Perceptrons
• Contrasting Supervised and Unsupervised Learning
• Exploring Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Back Propagation

Building Simple Deep Learning Models with Keras

• Creating a Keras Model
• Analyzing Your Data
• Defining Your Deep Learning Model structure
• Compiling the Model
• Fitting the Model to data
• Managing Classification Data
• Implementing Classification Models
• Deploying and Using Models

Utilizing TensorFlow for Deep Learning

• Data Preparation
  • Acquiring the Data
  • Preparing Training Data
  • Preparing Test Data
  • Input Scaling
  • Utilizing Placeholders and Variables
• Defining the Network Architecture
• Applying the Cost Function
• Using Optimizers
• Implementing Initializers
• Fitting the Neural Network
• Constructing the Graph
  • Inference
  • Loss calculation
  • Training process
• Training the Model
  • The Graph structure
  • The Session management
  • Train Loop implementation
• Model Evaluation
  • Constructing the Eval Graph
  • Evaluating via Eval Output
• Training Models at Scale
• Visualizing and Assessing Models with TensorBoard

Deep Learning Applications in Anomaly Detection

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

Ensemble Frameworks

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