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Data Science And Machine Learning With Java




Java Online Institute


All Job Seekers


Regular Offline and Online Live Training


Week Days and Week Ends

Duration :

1.5  hrs in weekdays and 3hrs during Weekend

Java Objectives

•Troubleshoot advanced models in Java.
•Work with standard programming skills in Java.
•Learn Java best practices and become a blackbelt.
•Learn End to End Java complete ground up
•You will understand how to implement a Java job.
•Learn Java For Beginners. The Complete Course With Practical Examples
•You will learn how to draw different Java entities through code.
•Students will have a solid understanding on how to create Java App.
•with this time saving course you will Learn Java and ready to use it

data science and machine learning with java Course Features

•You Get Real Time Project to practice
•Free technical support for students
•We assist on Internship on Real-Time Project 
•The courses range from basic to advanced level
•Facility of Lab on cloud available (based on booking)
•We also provide Cost Effective and Flexible Payment Schemes
•We also provide Normal Track, Fast Track and Weekend Batches also for Working Professionals
•Very in depth course material with Real Time Scenarios for each topic with its Solutions for Online Trainings.

Who are eligible for Java

•Artificial Intelligence, Data Science, Block Chain, Iot, Cloud Computing, Ux Design, Mobile Application Development, Natural Language Processing, Business
•Java Developer, Manual Testing, Automation Testing, Oracle Developer, Sybase Developer, SQL Server Developer with SSIS and SSRS, Windows/Weblogic Application
•Java, Net, C#, Manual Testing, Automation Testing, Manual Testing With Healthcare, Android And Ios Developer
•Qa Testers / Developers, Full Stack Developers – Backend / Frontend, Power Bi, Market Intelligence
•ux, ui, Python Developers, Qa Automation, sales, Ui Development, Ux Design, Software Development, Python, Qa Testing, Automation Testing


•HandsOn Data Science with Java
•The Course Overview
•Environment Configuration Step
•Loading Data from Different Sources
•Accessing Different Objects from the Datasets
•Filtering Unwanted Data
•Handling the NAN and the Null
•Formatting Various Data Types
•Efficient Distribution of Data
•Correlation in the Data
•Trend Analysis for Features
•Visualizing Different Data Forms
•Using Unsupervised Learning
•Executing Supervised Learning Regression
•Executing Supervised Learning Classification
•Formatting the Data for Your Model
•Performing Cross Validation
•Fitting the Model
•Predicting and Determining the Accuracy of the Model
•Importing Deeplearningj into Your Environment
•Choosing and Preparing Data for Deep Learning Model
•Building and Training a Model with a Framework
•Building and Training a Model Without a Framework
•Test Your Knowledge
•Machine Learning Projects with Java
•Performing Feature Engineering
•Leveraging NDJ Library Input Vectors and Matrices
•Extracting INDArray Features
•Applying Scalar Transformations to Features Vectors
•Project Set Up Using Weka Library
•Data Mining of Input Data Set
•Building Classifier in Weka Library
•Performing CrossValidation of the Model
•Making Predictions Based on the Classification
•Extracting Feature Vector for Housing Data
•Performing Normalization of Data
•Building Regression Model
•Leveraging Regression Model for Predicting Price of House
•Saving Model for Further ReUsage
•Feeding DLJ Model with Gender Labeled Data
•Creating a java File for Automatic Feature Extraction
•Creating Neural Network with Multiple Layers
•Training of Deep Learning Model
•Performing Validation of a Model
•Extracting Feature Vector from Text Data
•Loading Raw Data That will be an Input for NLP Training
•Leveraging NLP Construct from DLJ
•Finding Words Based on the Similarity