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Writing Smarter Applications with Machine Learning
1.
Writing Smart Programs Anoop
Thomas Mathew twitter: atmb4u Profoundis Inc.
2.
History then: low level â
high level now: code driven â data driven
3.
What is Smart everyone
makes mistakes â looking at past to avoid future mistakes everyone misses what comes next â predicting what to expect clusters of items do exist â automatically group things based on similarity
4.
Jargon Buster Dataset Data Cleaning Dimension Model Training Parameters Dimensionality
Reduction Accuracy Overfitting Underfitting Testing Domain
5.
Parameter Optimization Heuristic / Statistical
/ Machine Learning - âknow parameters wellâ Eg: stock market prediction disaster; no. of lawyers vs. no. of suicides
6.
Supervised vs Unsupervised we
know what we want vs. find whatâs interesting NB: training data, accuracy, semi-supervised
7.
Classification / Regression
/ Clustering â rain prediction â digit recognition â customer segmentation â time-series prediction â spam filtering Algorithm Examples â K-means â SVR â SVC â Naive Bayes â Random Forest Decision Tree
8.
9.
The ML Process plan
â collect â execute â test time: 50% 30% 5-10% 15-20%
10.
DEMO 1 SHOPPING PREDICT (https://github.com/atmb4u/smarter-apps-2016)
11.
DEMO 2 Support Vector
Machine (https://github.com/atmb4u/smarter-apps-2016)
12.
Dummy Tasks â user
auto-login redirect â predict if a user will convert to paid
13.
Thank You Follow me
on twitter:@atmb4u
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