Developing AI and ML Solutions with Java: AI Fundamentals

Course: 1 Hour, 4 Minutes

• Development Environment for Artificial Intelligence
• Machine Learning with Java
• Artificial Intelligence Implementation Scenarios
• Deeplearning4J Introduction
• Configure Neural Network Using DL4J
• Domain Specific Implementation of AI
• Predictive Modeling
• Exercise: Working with ML Libraries 

Developing AI and ML Solutions with Java: Machine Learning Implementation

Course: 1 Hour, 27 Minutes

• Machine Learning and Artificial Intelligence
• Machine Learning Algorithm Types
• Supervised and Unsupervised Learning
• K-Means Cluster
• KNN Algorithms
• Decision Tree and Random Forest
• Linear Regression Analysis
• Gradient Boosting Algorithms
• Logistics Regression
• Probabilistic Classifier
• Naïve Bayes Classifier
• Exercise: Implementing Machine Learning Algorithms
• Course Test 

Developing AI and ML Solutions with Java: Neural Network and Neuroph Framework

Course: 1 Hour, 49 Minutes

• Neural Network and its Essential Components
• Implement a Simple Neural Network
• Neural Network Types
• Implementing Hopfield Neural Networks
• Implementing Back Propagation Neural Networks
• Role of Activation Function
• Loss Functions and their Benefits
• Implementing Activation Functions and Loss Functions
• Hyperparameter
• Neuroph Java Neural Framework Capabilities
• Hyperparameter Implementation using DL4J
• Deep Learning
• Comparing Deep Learning and Graph Models
• Combining Deep Learning and Graph Model1
• Deep Learning and Graph Model Use Cases
• Exercise: Working with Neuroph and Neural Networks 

Developing AI and ML Solutions with Java: Neural Network and NLP Implementation

Course: 57 Minutes

• Multilayer Networks and Computation Graphs
• Implementing Multilayer Networks
• NLP Introduction
• Components of NLP
• Language and Sentence
• Tokenizer and Name Finder
• Detecting Parts of Speech
• Classifying Text and Documents
• Using Parser to Extract Relationships
• Speech Implementation
• Exercise: Working with NLP Components  

Developing AI and ML Solutions with Java: Expert Systems and Reinforcement Learning

Course: 48 Minutes

• Expert Systems Tools
• Working with Jess
• Defining Rules
• Supervised Learning and Notations
• Datasets and Training Models
• Outlier Types
• Feature Search and Feature Evaluation Techniques
• Principal Component Analysis Data Transformation
• Clustering Concept
• Hierarchical Clustering
• Graph Modeling
• Exercise: Working with Datasets and Clustering