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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.

Table of Contents

  1. Chapter 1 : Feature Extraction for Unstructured Textual News Feed Data
    1. The Course Overview 00:02:29
    2. Performing Feature Engineering 00:05:40
    3. Leveraging ND4J Library Input Vectors and Matrices 00:06:25
    4. Extracting INDArray Features 00:07:02
    5. Applying Scalar Transformations to Features Vectors 00:07:31
  2. Chapter 2 : ML Classification for Pattern Recognition of Sensor Data Using Weka Library
    1. Project Set Up Using Weka Library 00:07:10
    2. Data Mining of Input Data Set 00:04:15
    3. Building Classifier in Weka Library 00:05:18
    4. Performing Cross-Validation of the Model 00:03:48
    5. Making Predictions Based on the Classification 00:06:10
  3. Chapter 3 : Building Regression Model for Housing Market
    1. Extracting Feature Vector for Housing Data 00:05:22
    2. Performing Normalization of Data 00:05:22
    3. Building Regression Model 00:04:41
    4. Leveraging Regression Model for Predicting Price of House 00:05:03
    5. Saving Model for Further Re-Usage 00:04:34
  4. Chapter 4 : Deep Learning for Predicting Gender Based on the Name
    1. Feeding DL4J Model with Gender Labeled Data 00:04:35
    2. Creating a .java File for Automatic Feature Extraction 00:05:50
    3. Creating Neural Network with Multiple Layers 00:05:59
    4. Training of Deep Learning Model 00:05:56
    5. Performing Validation of a Model 00:08:03
  5. Chapter 5 : Finding Similarity of Words in a Book Using NLP with Deep Learning
    1. Extracting Feature Vector from Text Data 00:06:16
    2. Loading Raw Data That will be an Input for NLP Training 00:04:51
    3. Leveraging NLP Construct from DL4J 00:04:02
    4. Finding Words Based on the Similarity 00:05:46