Learn how to leverage well-proven ML algorithms to solve day-to-day ML problems
About This Video
Build machine learning projects using Java's extensive library support such as Weka, deeplearning4j, ND4J, and many more
A practical guide, with a strict focus on case implementations, to creating projects for each machine learning domain
Solve real-world problems with the help of machine learning with Java ML libraries
In Detail
Developers are …
Machine Learning Projects with Java
Video description
Learn how to leverage well-proven ML algorithms to solve day-to-day ML problems
About This Video
Build machine learning projects using Java's extensive library support such as Weka, deeplearning4j, ND4J, and many more
A practical guide, with a strict focus on case implementations, to creating projects for each machine learning domain
Solve real-world problems with the help of machine learning with Java ML libraries
In Detail
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.
Audience
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.
This course will also appeal to someone who has a basic understanding of ML concepts but now wants to learn how to implement it with Java.
Chapter 1 : Feature Extraction for Unstructured Textual News Feed Data
The Course Overview
Performing Feature Engineering
Leveraging ND4J Library Input Vectors and Matrices
Extracting INDArray Features
Applying Scalar Transformations to Features Vectors
Chapter 2 : ML Classification for Pattern Recognition of Sensor Data Using Weka Library
Project Set Up Using Weka Library
Data Mining of Input Data Set
Building Classifier in Weka Library
Performing Cross-Validation of the Model
Making Predictions Based on the Classification
Chapter 3 : Building Regression Model for Housing Market
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 Re-Usage
Chapter 4 : Deep Learning for Predicting Gender Based on the Name
Feeding DL4J 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
Chapter 5 : Finding Similarity of Words in a Book Using NLP with Deep Learning
Extracting Feature Vector from Text Data
Loading Raw Data That will be an Input for NLP Training
Leveraging NLP Construct from DL4J
Finding Words Based on the Similarity
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