Machine Learning Projects with Java

Machine Learning Projects with Java, available at $34.99, has an average rating of 3.05, with 24 lectures, based on 12 reviews, and has 101 subscribers.

You will learn about Perform classification using the Weka Library. Implement Pattern Recognition of non-labeled data Build Regression models for data with multiple features Save trained models for further reusability Learn how to perform cross-validation Leverage Deep Learning in ML problems Implement Natural Language Processing with Deep Learning This course is ideal for individuals who are This course is for Java developers who now want to extend their skillset in Machine Learning and would like to achieve this with ease using Java. It is particularly useful for This course is for Java developers who now want to extend their skillset in Machine Learning and would like to achieve this with ease using Java.

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Summary

Title: Machine Learning Projects with Java

Price: $34.99

Average Rating: 3.05

Number of Lectures: 24

Number of Published Lectures: 24

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 24

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Perform classification using the Weka Library.

Implement Pattern Recognition of non-labeled data

Build Regression models for data with multiple features

Save trained models for further reusability

Learn how to perform cross-validation

Leverage Deep Learning in ML problems

Implement Natural Language Processing with Deep Learning

Who Should Attend

This course is for Java developers who now want to extend their skillset in Machine Learning and would like to achieve this with ease using Java.

Target Audiences

This course is for Java developers who now want to extend their skillset in Machine Learning and would like to achieve this with ease using Java.

Developers are worried about using various algorithms to solve different problems. This course is a perfect guide to identifying the best solution to efficiently build machine learning projects for different use cases to solve real-world problems.

In this course, you will learn how to build a model that takes complex feature vector form sensor data and classifies data points into classes with similar characteristics. Then you will predict the price of a house based on historical data. Finally, you will build a Deep Learning model that can guess personality traits using labeled data.

By the end of this course, you will have mastered each machine learning domain and will be able to build your own powerful projects at work.

About The Author

Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He has worked with ML algorithms for the past 5 years, with production experience in processing petabytes of data.

He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and also at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.

Course Curriculum

Chapter 1: Feature Extraction for Unstructured Textual News Feed Data

Lecture 1: The Course Overview

Lecture 2: Performing Feature Engineering

Lecture 3: Leveraging ND4J Library Input Vectors and Matrices

Lecture 4: Extracting INDArray Features

Lecture 5: Applying Scalar Transformations to Features Vectors

Chapter 2: ML Classification for Pattern Recognition of Sensor Data Using Weka Library

Lecture 1: Project Set Up Using Weka Library

Lecture 2: Data Mining of Input Data Set

Lecture 3: Building Classifier in Weka Library

Lecture 4: Performing Cross-Validation of the Model

Lecture 5: Making Predictions Based on the Classification

Chapter 3: Building Regression Model for Housing Market

Lecture 1: Extracting Feature Vector for Housing Data

Lecture 2: Performing Normalization of Data

Lecture 3: Building Regression Model

Lecture 4: Leveraging Regression Model for Predicting Price of House

Lecture 5: Saving Model for Further Re-Usage

Chapter 4: Deep Learning for Predicting Gender Based on the Name

Lecture 1: Feeding DL4J Model with Gender Labeled Data

Lecture 2: Creating a .java File for Automatic Feature Extraction

Lecture 3: Creating Neural Network with Multiple Layers

Lecture 4: Training of Deep Learning Model

Lecture 5: Performing Validation of a Model

Chapter 5: Finding Similarity of Words in a Book Using NLP with Deep Learning

Lecture 1: Extracting Feature Vector from Text Data

Lecture 2: Loading Raw Data That will be an Input for NLP Training

Lecture 3: Leveraging NLP Construct from DL4J

Lecture 4: Finding Words Based on the Similarity

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 3 votes

4 stars: 2 votes

5 stars: 3 votes

Frequently Asked Questions

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