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Java Data Science Solutions - Big Data and Visualization

Video Description

Explore the power of MLlib, DL4j, Weka, and more

About This Video

  • This video provides modern solutions in small steps to help an apprentice cook become a master chef in data science

  • Fast paced tutorial to handle big data and perform visualization techniques to learn deeply from the data

  • Learn how to get your data science applications to production and enterprise environments effortlessly

  • In Detail

    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.

    Table of Contents

    1. Chapter 1 : Data Operations
      1. The Course Overview 00:02:32
      2. Creating and Saving an ARFF File 00:06:30
      3. Cross-Validating a Machine Learning Model 00:03:01
      4. Classifying Unseen Test Data 00:05:35
      5. Classifying Unseen Test data with a Filtered Classifier 00:02:43
      6. Generating Linear Regression Models 00:02:15
      7. Generating Logistic Regression Models 00:01:51
    2. Chapter 2 : Clustering and Feature Selection
      1. Clustering Data Points Using the K-means Algorithm 00:02:18
      2. Clustering Data from Classes 00:02:18
      3. Learning Association Rules from Data 00:02:08
      4. Selecting Features and Attributes 00:04:50
    3. Chapter 3 : Learning from Data
      1. Applying Machine Learning on Data Using the Java-ML Library 00:11:54
      2. Classifying Data Points Using the Stanford Classifier 00:05:41
      3. Classifying Data Points Using Massive Online Analysis (MOA) 00:03:34
      4. Classifying Multilabeled Data Points Using Mulan 00:05:33
    4. Chapter 4 : Retrieving Information from Text Data
      1. Detecting Tokens Using Java 00:04:13
      2. Detecting Sentences Using Java 00:01:37
      3. Detecting Tokens (words) and Sentences Using OpenNLP 00:04:32
      4. Retrieving Lemma and Part of Speech, and Recognizing Named Entities from Tokens Using Stanford CoreNLP 00:03:13
      5. Measuring Text Similarity with Cosine Similarity Measure Using Java 8 00:03:06
      6. Extracting Topics from Text Documents Using Mallet 00:07:36
      7. Classifying Text Documents Using Mallet 00:05:27
      8. Classifying Text Documents Using Weka 00:03:32
    5. Chapter 5 : Handling Big Data
      1. Training an Online Logistic Regression Model Using Apache Mahout 00:04:29
      2. Applying an Online Logistic Regression Model Using Apache Mahout 00:03:27
      3. Solving Simple Text Mining Problems with Apache Spark 00:02:52
      4. Clustering Using K-means Algorithm with MLib 00:02:08
      5. Creating a Linear Regression Model with MLib 00:02:54
      6. Classifying Data Points with Random Forest Model Using MLib 00:03:18
    6. Chapter 6 : Learn Deeply from Data
      1. Creating a Word2vec Neural Net 00:05:33
      2. Creating a Deep Belief Neutral Net 00:04:26
      3. Creating a Deep Autoencoder 00:03:00
    7. Chapter 7 : Visualizing Data
      1. Plotting a 2D Sine Graph 00:03:39
      2. Plotting Histograms 00:03:55
      3. Plotting a Bar Chart 00:02:14
      4. Plotting Box Plots or Whisker Diagrams 00:02:29
      5. Plotting Scatter Plots 00:01:50
      6. Plotting Donut Plots 00:02:56
      7. Plotting Area Graphs 00:04:57