Machine Learning with Java and Weka

Name: Machine Learning with Java and Weka
Rating: 3.5 (31 reviews)

Machine Learning and Statistical Learning with Java

Created byGoh Ming Hui

Last updated 12/2018

English

What you'll learn

Create a data product using Weka and Java

Course content

1 section • 29 lectures • 2h 20m total length

Introduction16:33
Discover Java as a high-level, cross-platform, object-oriented language for machine learning and data mining, including data gathering, model building, evaluation, deployment, and big data concepts.
Getting Started5:24
Install the Java JDK and set up the Java Virtual Machine to run Java programs cross-platform, and explore the IDE with syntax highlighting, debugging, and code completion.
Getting Started 21:53
Getting Started 35:12
Get started with Java and Weka by configuring system settings, environment variables, and selecting a folder to browse your computer to run your job.
Data Mining Process5:37
Explore the data mining process from business understanding to deployment, including data understanding, cleaning and normalization, modeling with classification and regression, and evaluation of accuracy.
Dataset1:15
Split Training and Testing Dataset3:34
Learn how to split data into training and testing datasets for machine learning tasks using Java and Weka, with steps to prepare and save the training and test sets.
Create Java Applications in Netbeans with Weka1:36
Create Java applications in NetBeans with Weka by building training and testing datasets and integrating JAR libraries to support job applications.
Simple Linear Regression3:07
Explore simple linear regression by training on 75 percent of data, testing on 25 percent, deriving the regression equation, and using it to predict numerical values like price.
LInear Regression using Weka and Java8:36
LInear Regression using Weka and Java 24:35
Learn how to build a linear regression model with Weka and Java, split data into training and test sets, train on instances, and generate predictions.
LInear Regression using Weka and Java 33:42
Learn how to perform linear regression using Weka and Java, building a multi-variable model with numeric continuous data, and generating predictions from training and testing data.
KMeans Clustering3:05
KMeans Clustering using Weka and Java6:32
Agglomeration CLustering3:45
Agglomerative clustering begins with individual data objects, computes distances between data objects, and repeatedly merges the closest data objects to form clusters, updating the distance matrix.
Agglomeration CLustering in Weka and Java4:03
Learn to implement agglomerative, hierarchical clustering in Weka and Java, and compare it to k-means, using libraries and sample code to generate and interpret clustering results.
Decision Tree Algorithm: ID39:15
Decision Tree Algorithm: ID3 using Weka and Java8:20
KNN Classification3:50
KNN Classification using Weka and Java1:50
Master knn classification using the ibk algorithm in Weka with Java, configure the classification, and implement the approach in code.
Naive Bayes ALgorithm5:36
Explain how the naive bayes classifier uses conditional independence to compute the probability of a hypothesis given evidence, using a dataset of categorical variables and frequency tables.
Naive Bayes ALgorithm using Weka and Java1:45
Explore how to apply the Naive Bayes algorithm with Weka and Java for classification, using flight data and other datasets, and implement the process with Java libraries.
Neural Network5:44
Explore neural network basics, including neurons, propagation, activation functions, bias, and weights; learn iterative training with backpropagation, error calculation, learning rates, and multi-layer perceptrons.
Neural Network in Weka and Java1:56
Explore neural networks for classification using a multilayer perceptron in Weka and Java. Build and compare perceptron-based classifiers and adjust MLP configurations to suit datasets.
What Algorithm to use?1:35
Learn how to select the right algorithm for data mining tasks in machine learning with Java and Weka, using a cheat sheet to guide regression, clustering, dimensionality reduction, and classification.
Model Evaluation3:44
Learn how to evaluate machine learning models using regression and classification metrics, including R-squared, residuals, SSE, SST, accuracy, and precision, with practical examples.
Model Evaluation using Weka and Java7:16
Create a Data Mining Software4:48
Develop a data mining software using Java and Weka, focusing on classification, evaluation, and training processes in a practical data mining workflow.
Create a Data Mining Software 26:44
Select a pre-specified algorithm and build a data mining workflow by preparing training and testing data for classification and evaluating model accuracy.

Requirements

Computer Skills, Java Programming

Description

Why learn Data Analysis and Data Science?

According to SAS, the five reasons are

1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.

2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.

3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.

4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.

5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths. Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.

The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

This is the bite-size course to learn Java Programming for Machine Learning and Statistical Learning with the Weka library. In CRISP-DM data mining process, machine learning is at the modeling and evaluation stage.

You will need to know some Java programming, and you can learn Java programming from my "Create Your Calculator: Learn Java Programming Basics Fast" course. You will learn Java Programming for machine learning and you will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course.

Content

Introduction
Getting Started
Getting Started 2
Getting Started 3
Data Mining Process
Data set
Split Training and Testing dataset
Create Java Application using Netbeans with Weka Jar
Simple Linear Regression
Linear Regression using Weka and Java
Linear Regression using Weka and Java 2
Linear Regression using Weka and Java 3
KMeans Clustering
KMeans Clustering in Weka and Java
Agglomeration Clustering
Agglomeration Clustering in Weka and Java
Decision Tree ID3 Algorithm
Decision Tree in Weka and Java
KNN Classification
KNN in Weka and Java
Naive Bayes Classification
Naive Bayes in Weka and Java
Neural Network Classification
Neural Network in Weka and Java
What Algorithm to Use?
Model Evaluation
Model Evaluation in Weka and Java
Create a Data Mining Software
Create a Data Mining Software 2

Who this course is for:

Beginner Data Analyst or Data Scientist interested in using Weka in Java