Developing AI and Machine Learning Solutions with Java E-Learning Training

Bestel deze geweldige E-Learning Training Developing AI and Machine Learning Solutions with Java online cursus, 1 jaar 24/ 7 toegang tot rijke interactieve video’s, spraak, praktijkopdrachten, voortgangsbewaking door rapportages en testen per onderwerp om de kennis direct te toetsen. Na de cursus ontvangt u een certificaat van deelname.

Kunstmatige intelligentie (AI) is een gebied van computerwetenschap dat de nadruk legt op het creëren van intelligente systemen die worden gebruikt om bedrijven te helpen de toenemende complexiteit van hun bedrijf te beheren. Dit leertraject behandelt het concept van AI inclusief machine learning (ML), deep learning en expertsystemen met behulp van Java-bibliotheken.

Cursusinhoud

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