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+# Learning: the hows and whys of machine learning
+
+Liam Wiltshire
+https://liam-wiltshire.github.io/talks/?talk=machinelearning&conference=phpuk
+https://joind.in/event/php-uk-conference-2019/learning-the-hows-and-whys-of-machine-learning
+
+## Overivew
+
+Charge backs
+
+## Supervised learning
+Training data
+Learning functions
+Categorisation / Classification
+Regression - Where do we sit on a line
+
+## Naive Bayes classifier
+Standardise words
+- Un pluralise
+- Un gender
+- Un tense
+- etc
+
+More data == better
+
+## Tokenisation
+https://en.wikipedia.org/wiki/Benford%27s_law
+https://php-ml.readthedocs.io
+
+Unique tokens for each unique context
+
+## Imbalanced data
+One category has more database
+99% data not charge back
+Just being accurate, not very helpful
+    - Started by flagging 100% as fine.
+    - Need to collect more data, change methods, resample data
+
+## Understand data
+- context
+- Common data vs specific data
+- Continuous vs discrete data
+
+## KNN
+K Nearest Number
+https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
+    - Distances
+    - less sensitive to imbalance
+    - Keep K odd (no draws)
+
+## Handling nominal data
+
+Binary
+- Increase amounts of dimensions
+- normalisation required
+- equal scales
+
+## Contextless data is meaningless
+Is it normal?
+
+## Next to try
+Weighting
+Different dimensions
+Change K value (was 3NN)
+Remove outliers
+Diff distance function
+weighted distance
+
+
+
+
+# Useful links
+https://en.wikipedia.org/wiki/Benford%27s_law
+https://php-ml.readthedocs.io
+https://liam-wiltshire.github.io/talks/?talk=machinelearning&conference=phpuk
+https://joind.in/event/php-uk-conference-2019/learning-the-hows-and-whys-of-machine-learning
+https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm