Developing AI and ML Solutions with Java: AI Fundamentals

Developing AI and ML Solutions with Java: AI Fundamentals

• identify the primary goals of machine learning, artificial Intelligence, deep learning, and reinforcement learning
• recognize the essential features of artificial intelligence and differentiate it with non-AI applications
• set up the Java development environment for artificial intelligence
• demonstrate the use of machine learning algorithms in Java
• specify the various implementations scenarios of AI
• identify the essential features and capabilities afforded by Deeplearning4j
• demonstrate how to configure neural networks using DL4J
• identify the primary domains where artificial intelligence is predominantly implemented
• describe the concept of predictive modeling along with various relatable algorithms

Discover the fundamental concepts of the technologies driving artificial Intelligence (AI).

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

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

• list the tools, shells, and programming languages that are being used for Expert Systems
• work with Jess to create rule based expert systems
• describe how to define rules and work with expert system shell using Java
• recognize data notations from the perspective of quality, descriptive, and visualization notations
• list the different types of datasets and their utility over the various phases of supervised learning
• identify the various types of Outliers and their impact on the accuracy of the models
• describe the various approaches of feature relevance search and the evaluation techniques
• implement principal component analysis data transformation using Java pca-tranform
• recognize the clustering implementation algorithms and illustrate the validation and evaluation techniques
• implement hierarchical clustering using the top down approach with Java
• describe the concept of graph modelling and the various approaches of implementing graphs in machine learning

Explore the concepts of expert system along with its Implementation using Java based frameworks, and examine the implementation and usages of ND4J and Arbiter to facilitate optimization.

Developing AI and ML Solutions with Java: Machine Learning Implementation

Developing AI and ML Solutions with Java: Machine Learning Implementation

• identify the critical relation between machine learning and artificial intelligence
• specify the various classifications of machine learning algorithms
• describe the differences between supervised and unsupervised learning
• state how to implement K-Means clusters
• describe how to implement KNN algorithms
• implement decision tree and random forest
• recall how to use and work with linear regression analysis
• implement gradient boosting algorithms using Java
• illustrate the implementation of logistic regression using Java
• recognize the usage and objective of probabilistic classifiers for statistical classification
• implement Naïve Bayes classifier using Java

Explore the various machine learning techniques and implementations using Java libraries, and learn to identify certain scenarios where you can implement algorithms.

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

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

• recognize the concept of neural network, neurons and the different layers of neuron
• describe the practical implementation of a simple neural network using Java
• list the various types of neural networks that are prominently used today
• Implementing Hopfield Neural Networks
• describe how to implement back propagation neural networks using Java
• identify the relevance of activation functions and list the various types of activation functions in neural networks
• recognize the benefits of loss functions and list the various types of loss functions in practice today
• implement activation functions and loss functions using DL4J
• demonstrate how to work with hyperparameters in neural networks
• recall the capabilities and practical implementation of Neuroph framework
• work with the Arbiter hyperparameter optimization library designed to automate hyperparameter
• describe the concept of the deep learning and list its various components
• recognize the similarities and differences between deep learning and graph model
• work with the collaboration of deep learning and graph model
• identify the relevant use cases for implementing deep learning and graph model

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

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

• describe the essential features of multilayer networks and computation graphs
• describe how to use multilayer networks and computation graphs
• specify the essential features and important components of NLP
• list the important components of NLP along with their roles and usages
• implement language and sentence detector
• describe the utilization of Tokenizer and Name Finder in NLP
• describe how to detect parts of speech to assign tags to the words and sentences
• classify text and documents using the NLP model
• Illustrate the relationships, extraction and dependencies using parser API
• implement recognizer, synthesizer and translator using Java

Discover how to implement advanced neural network using DL4j and explore the concept of NLP and its implementation using OpenNLP Java library.