•HandsOn Data Science with Java
•The Course Overview
•Environment Configuration Step
•Loading Data from Different Sources
•Accessing Different Objects from the Datasets
•Filtering Unwanted Data
•Handling the NAN and the Null
•Formatting Various Data Types
•Efficient Distribution of Data
•Correlation in the Data
•Trend Analysis for Features
•Visualizing Different Data Forms
•Using Unsupervised Learning
•Executing Supervised Learning Regression
•Executing Supervised Learning Classification
•Formatting the Data for Your Model
•Performing Cross Validation
•Fitting the Model
•Predicting and Determining the Accuracy of the Model
•Importing Deeplearningj into Your Environment
•Choosing and Preparing Data for Deep Learning Model
•Building and Training a Model with a Framework
•Building and Training a Model Without a Framework
•Test Your Knowledge
•Machine Learning Projects with Java
•Performing Feature Engineering
•Leveraging NDJ Library Input Vectors and Matrices
•Extracting INDArray Features
•Applying Scalar Transformations to Features Vectors
•Project Set Up Using Weka Library
•Data Mining of Input Data Set
•Building Classifier in Weka Library
•Performing CrossValidation of the Model
•Making Predictions Based on the Classification
•Extracting Feature Vector for Housing Data
•Performing Normalization of Data
•Building Regression Model
•Leveraging Regression Model for Predicting Price of House
•Saving Model for Further ReUsage
•Feeding DLJ Model with Gender Labeled Data
•Creating a java File for Automatic Feature Extraction
•Creating Neural Network with Multiple Layers
•Training of Deep Learning Model
•Performing Validation of a Model
•Extracting Feature Vector from Text Data
•Loading Raw Data That will be an Input for NLP Training
•Leveraging NLP Construct from DLJ
•Finding Words Based on the Similarity
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