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CLASSIFICATION FAST ARTIFICIAL NEURAL NETWORK
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Classroom Contents
Machine Learning for a Rescue
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- 1 Intro
- 2 RESCUE
- 3 Source Ministry
- 4 CLIENT PROBLEM
- 5 M BACKLINKS CLASSIFY THEM
- 6 1ST APPROACH IF-OLOGY UGLY CODE FOR POC
- 7 ND APPROACH NAIVE MACHINE LEARNING
- 8 DOING WITHOUT KNOWING ISA. RECIPE FOR A FAILURE
- 9 RD APPROACH, FINAL DATA ORIENTED MACHINE LEARNING WORKFLOW
- 10 CLASSIFICATION REGRESSION CLUSTERING DIMENSIONALITY REDUCTION ASSOCIATION RULES
- 11 SUPERVISED LEARNING UNSUPERVISED LEARNING REINFORCEMENT LEARNING
- 12 OUR PROBLEM
- 13 DEVELOPERS DATASET
- 14 REGRESSION PREDICTING VALUES
- 15 CLUSTERING K-MEANS
- 16 1936, RONALD FISHER IRIS DATASET
- 17 RESULTS STABILITY
- 18 CLASSIFICATION FAST ARTIFICIAL NEURAL NETWORK
- 19 HOW TO CLASSIFY OUR DATASET AUTOMATED WAY TO FIND JUNIOR/SENIOR DEVELOPER?
- 20 TECHNOLOGY
- 21 FOCUS ON IDEAS NOT TOOLS
- 22 ML IS NOT A SINGLE RUN
- 23 IT'S A PROCESS
- 24 DEFINE A PROBLEM ANALYZE YOUR DATA UNDERSTAND YOUR DATA PREPARE DATA FOR ML SELECT & RUN ALGO(S) TUNE ALGO(S) PARAMETERS SELECT FINAL MODEL VALIDATE FINAL MODEL
