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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 →
high level now: code driven → data driven
3.
What is Smart everyone
makes mistakes ○ looking at past to avoid future mistakes everyone misses what comes next ○ predicting what to expect clusters of items do exist ○ 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 - “know parameters well” Eg: stock market prediction disaster; no. of lawyers vs. no. of suicides
6.
Supervised vs Unsupervised we
know what we want vs. find what’s interesting NB: training data, accuracy, semi-supervised
7.
Classification / Regression
/ Clustering ★ rain prediction ★ digit recognition ★ customer segmentation ★ time-series prediction ★ spam filtering Algorithm Examples ★ K-means ★ SVR ★ SVC ★ Naive Bayes ★ Random Forest Decision Tree
9.
The ML Process plan
→ collect → execute → 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 ★ user
auto-login redirect ★ predict if a user will convert to paid
13.
Thank You Follow me
on twitter:@atmb4u
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