English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2.5 Hours | 700 MB
Explore the power of MLlib, DL4j, Weka, and more
If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This course will help you to learn how you can retrieve data from data sources with different level of complexities. You will learn how you could handle big data to extract meaningful insights from data. Later we will dive to visualizing data to uncover trends and hidden relationships. Finally, we will work through unique videos that solve your problems while taking data science to production, writing distributed data science applications, and much more—things that will come in handy at work.
What You Will Learn
- Use machine learning techniques to learn patterns from data
- Perform clustering, and feature selection exercises using the Weka machine learning Workbench
- Learn data import and export, classification, and feature selection using Java Machine Learning (Java-ML) library
- Learn application of core Java and popular libraries, such as OpenNLP, Stanford CoreNLP, Mallet, and Weka
- Learn application of big data platforms for machine learning, such as Apache Mahout and Spark-MLib
- Familiarize yourself with the very basics of deep learning using the Deep Learning for Java (DL4j) library
- Learn to use GRAL package to generate an appealing and informative display based on data
Table of Contents
01 The Course Overview
02 Creating and Saving an ARFF File
03 Cross-Validating a Machine Learning Model
04 Classifying Unseen Test Data
05 Classifying Unseen Test data with a Filtered Classifier
06 Generating Linear Regression Models
07 Generating Logistic Regression Models
08 Clustering Data Points Using the K-means Algorithm
09 Clustering Data from Classes
10 Learning Association Rules from Data
11 Selecting Features and Attributes
12 Applying Machine Learning on Data Using the Java-ML Library
13 Classifying Data Points Using the Stanford Classifier
14 Classifying Data Points Using Massive Online Analysis (MOA)
15 Classifying Multilabeled Data Points Using Mulan
16 Detecting Tokens Using Java
17 Detecting Sentences Using Java
18 Detecting Tokens (words) and Sentences Using OpenNLP
19 Retrieving Lemma and Part of Speech, and Recognizing Named Entities from Tokens Using Stanford CoreNLP
20 Measuring Text Similarity with Cosine Similarity Measure Using Java 8
21 Extracting Topics from Text Documents Using Mallet
22 Classifying Text Documents Using Mallet
23 Classifying Text Documents Using Weka
24 Training an Online Logistic Regression Model Using Apache Mahout
25 Applying an Online Logistic Regression Model Using Apache Mahout
26 Solving Simple Text Mining problems with Apache Spark
27 Clustering Using K-means Algorithm with MLib
28 Creating a Linear Regression Model with MLib
29 Classifying Data Points with Random Forest Model Using MLib
30 Creating a Word2vec Neural Net
31 Creating a Deep Belief Neutral Net
32 Creating a Deep Autoencoder
33 Plotting a 2D Sine Graph
34 Plotting Histograms
35 Plotting a Bar Chart
36 Plotting Box Plots or Whisker Diagrams
37 Plotting Scatter Plots
38 Plotting Donut Plots
39 Plotting Area Graphs
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