Machine Learning Implementation

Java SE 8    |    Intermediate
  • 12 videos | 1h 26m 39s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 220 users Rating 4.5 of 220 users (220)
Explore the various machine learning techniques and implementations using Java libraries, and learn to identify certain scenarios where you can implement algorithms.

WHAT YOU WILL LEARN

  • Identify the critical relation between machine learning and artificial intelligence
    Specify the various classifications of machine learning algorithms
    Describe the differences between supervised and unsupervised learning
    State how to implement k-means clusters
    Describe how to implement knn algorithms
    Implement decision tree and random forest
  • Recall how to use and work with linear regression analysis
    Implement gradient boosting algorithms using java
    Illustrate the implementation of logistic regression using java
    Recognize the usage and objective of probabilistic classifiers for statistical classification
    Implement naïve bayes classifier using java
    Demonstrate how to use the k-mean algorithm in ml applications

IN THIS COURSE

  • 3m 11s
    In this video, find out how to identify the critical relation between machine learning and artificial intelligence. FREE ACCESS
  • 4m 59s
    After completing this video, you will be able to specify the various classifications of machine learning algorithms. FREE ACCESS
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    3.  Supervised and Unsupervised Learning
    6m 34s
    After completing this video, you will be able to describe the differences between supervised and unsupervised learning. FREE ACCESS
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    4.  K-Means Cluster
    7m 18s
    In this video, you will learn how to implement K-Means clusters. FREE ACCESS
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    5.  KNN Algorithms
    9m 45s
    Upon completion of this video, you will be able to describe how to implement KNN algorithms. FREE ACCESS
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    6.  Decision Tree and Random Forest
    12m 58s
    Find out how to implement a decision tree and random forest. FREE ACCESS
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    7.  Linear Regression Analysis
    8m 50s
    After completing this video, you will be able to recall how to use and work with linear regression analysis. FREE ACCESS
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    8.  Gradient Boosting Algorithms
    10m 41s
    Learn how to implement gradient boosting algorithms using Java. FREE ACCESS
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    9.  Logistics Regression
    8m 9s
    Upon completion of this video, you will be able to illustrate the implementation of logistic regression using Java. FREE ACCESS
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    10.  Probabilistic Classifier
    3m 10s
    After completing this video, you will be able to recognize the usage and objective of probabilistic classifiers for statistical classification. FREE ACCESS
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    11.  Naïve Bayes Classifier
    7m 18s
    In this video, learn how to implement the Naïve Bayes classifier using Java. FREE ACCESS
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    12.  Exercise: Implementing Machine Learning Algorithms
    3m 45s
    In this video, you will learn how to use the K-Means algorithm in ML applications. FREE ACCESS

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