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Data Science Bootcamp: Hands-On Java Training
01. Course Introduction
Course Introduction (2:02)
Course Introduction Slides
Apache Math Library (For Upcoming Projects)
02. K-Nearest Neighbors (K-NN) Project
Introduction To K-Nearest Neighbors (11:20)
Dataset for KNearestNeighbors Project
00 Set Up The Data (8:30)
01 Enable User Input (10:29)
02 Handle User Input (14:40)
03 Set Up K-Nearest Neighbors (9:17)
04 Divide Matrices With Intstream (10:29)
05 Store User Input As A Realmatrix (7:59)
06 Build Matrix Methods (11:47)
07 Find Euclidian Distances (11:19)
08 Sort Euclidian Distances (6:17)
09 Show Classification (2:58)
10 Calculate Range And Normalize Values (11:54)
11 Test The Application (2:50)
KNearestNeighborsLab Source Code
Introduction to K-Nearest Neighbors PDF
03. Decision Trees Project
00 Introduction To Decision Trees, Information Gain And Entropy (6:12)
DataSet for Decision Trees Project
01 Set Up Dataset And Classes (14:55)
02 Represent Each Feature Value (6:29)
03 Build A Dataset Of Feature Values (10:11)
04 Build A Dataset From A Larger Dataset (8:05)
05 Build A Dataset Class (6:27)
06 Display Information Gain Table (9:40)
07 Display Feature To Split On (6:32)
08 Test And Run The Project (3:40)
Decision Trees Project Source Code
Introduction to Decision Trees, Information Gain and Entropy PDF
04. Neural Network Project
00. Introduction To Neural Networks (13:44)
Target Result Data for Neural Networks Lab
01 Set Up The Project (13:26)
02 Weight And Activation Function (8:12)
03 Adjust Weights (12:09)
04 Test The Application (2:31)
Neural Networks Source Code
Introduction to Neural Networks PDF
05. Naive Bayes Project
01 Introduction To Naive Bayes (17:34)
Dataset for Naive Bayes Project
02 Set Up Data And User Input (10:37)
03 Classify User Input (10:21)
04 Calculate Log Sum (9:54)
05 Calculate Probability (7:35)
06 Calculate Conditional Properties (11:36)
07 Test The Application (12:46)
Naive Bayes Lab Source Code
Introduction to Naive Bayes PDF
Introduction To K-Nearest Neighbors
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