Topics Covered

• Predictive AI Business and Technology Drivers
• Predictive AI Benefits
• Common Risks and Challenges of Using Predictive AI
• Business Problem Categories Addressed by AI
• Types of Predictive AI
• Common Predictive AI Learning Approaches
• Understanding Predictive AI Learning and Model Training
• Step-by-Step Training Loop Process

• Supervised Learning, Unsupervised Learning, Continuous Learning
• Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
• Common Predictive AI Functional Designs, Computer Vision, Pattern Recognition
• Robotics, Natural Language Processing (NLP)
• Speech Recognition, Natural Language Understanding (NLU)
• Understanding AI Models and Neural Networks

Topics Covered

• Generative AI Business and Technology Drivers
• Generative AI Benefits
• Common Risks and Challenges of Using Generative AI
• Business Problem Categories Addressed by Generative AI
• Understanding Models, Algorithms and Neural Networks
• Types of Generative AI

• Training Generative Models and Understanding the Training Loop
• Understanding Generative Adversarial Networks (GANs)
• Understanding Variational Encoders (VAE)
• Understanding Transformers
• Steps to Building AI Systems
• Generative AI Best Practices

Topics Covered

• Model Evaluation and Validation Techniques
• Data Preprocessing Techniques, Overfitting and Regularization
• Practical AI Ethics and Bias Mitigation
• Optimization Techniques and Advanced Learning Algorithms
• Imbalanced Datasets Handling Techniques
• Natural Language Processing (NLP) with Deep Learning
• Advanced Feature Engineering, Data Augmentation Techniques
• Fine-Tuning Strategies, Reinforcement Learning
• Frictionless Integration, Fault Tolerance Model Integration
• Model Explainability and Interpretability
• Model Deployment, Monitoring and Maintenance

• Understanding Neural Networks and Models
• Neural Network Types, Neurons, Layers, Links, Weights
• Loss, Hyperparameters, Learning Rate, Bias, Epoch
• Activation Functions (Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax, Softplus)
• Neuron Cell Types (Input, Backfed, Noisy, Hidden, Probabilistic, Spiking, Recurrent, Memory, Kernel, Convolution, Pool, Output, Match Input, etc.)
• Neural Network Architectures for Predictive AI and Generative AI
• How to Build an AI System (Step-by-Step)
• Common AI System Design Principles and AI Project Best Practices

Topics Covered

• Data Wrangling for Preparing Data for Neural Network Input
• Feature Encoding for Converting Categorical Features
• Feature Imputation for Inferring Feature Values
• Feature Scaling for Training Datasets with Broad Features
• Text Representation for Converting Data while Preserving Semantic and Syntactic Properties
• Dimensionality Reduction to Reduce Feature Space for Neural Network Input
• Supervised Learning for Training Neural Network Models
• Supervised Network Configuration for Establishing the Number of Neurons in Network Layers
• Image Identification for Using a Convolutional Neural Network
• Sequence Identification for Using a Long Short Term Memory Neural Network
• Unsupervised Learning Patterns for Training Neural Network Models

• Pattern Identification for Visually Identifying Patterns via a Self Organizing Map
• Content Filtering for Generating Recommendations
• Model Evaluation Patterns for Measuring Neural Network Performance
• Training Performance Evaluation for Assessing Neural Network Performance
• Prediction Performance Evaluation for Predicting Neural Network Performance in Production
• Baseline Modeling for Assessing and Comparing Complex Neural Networks
• Model Optimization Patterns for Refining and Adapting Neural Networks
• Overfitting Avoidance for Tuning a Neural Network
• Frequent Model Retraining for Keeping a Neural Network in Synch with Current Data
• Transfer Learning for Accelerating Neural Network Training

Topics Covered