Developing AI and ML Solutions with Java E-Learning Ausbildung

Bestellen Sie dieses großartige E-Learning-Ausbildung zur Entwicklung von KI- und ML-Lösungen mit Java-Online-Kurs, 1 Jahr rund um die Uhr Zugriff auf umfangreiche interaktive Videos, Sprache, praktische Aufgaben, Fortswchrittsüber direwachung Tests Berk Test . Nach dem Kurs erhalten Sie eine Teilnahmebescheinigung.

Kursinhalt

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