ML is the acquisition of “structural patterns” from sample data. Supervised ML is a set of adaptive methods to discover those patterns. The methods used have 2 core components: 1. Algorithms for making computations and 2. Structures for storing the result model. This presentation is outlined as follows: - Data Mining vs. Machine Learning - ML Techniques and Use Cases - Supervised ML Pipeline - Supervised ML Model Training

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Abbas Taher
Founder & Product Manager
* Flek is the Norwegian word for Spot
Introduction to Supervised
Machine Learning
GoFlek*
Intelligent Data Mining

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Outline
I. Data Mining vs. Machine Learning
II. ML Techniques and Use Cases
III. Supervised ML Pipeline
IV. Supervised ML Model Training

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Math is sometimes called the
science of patterns
Roland Graham
Some ML techniques tell their reasoning
when predicting
Others read it from a crystal ball

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What is …
Data Mining
Machine
Learning
Nuggets &
Insights
Solving business problems through data
analysis. The process of discovering
patterns in data using ML methods and
tools amongst others.
Adaptive methods for uncovering the
“structural patterns” encapsulated
within the data
Hidden Patterns, Models, Predictions,
Statistics, Rules, Clusters, Classification,
Maps, Recurring Data, Outliers,
Groupings

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Data Mining Process
Data
Mining
ML ML ML
Process of
Discovering Patterns
ML used to find and
describe “structural
patterns” in the data
Infer
Patterns hidden in the
data that might provide
insight
Data
Structural
Patterns
computed
from the data
Compute
Decision
Tree
Vectors &
Matrixes
Graphs Neural
Networks
Rules

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What is Machine Learning
ML is the acquisition of
“structural patterns” from sample data
In a nutshell, ML is a set of adaptive methods to
discover those patterns. The methods used
have 2 core components:
 Algorithms for making computations
 Structures for storing the result models

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What makes a good
ML method?
 The structure (model) should be rich, flexible and
allow to be examined, reasoned about and queried for
information
 The model may be used directly or indirectly to make
predictions that are traceable and interpretable
 The computational algorithms should be based on
sound mathematical formulation
 The algorithms should be consistent – i.e generate the
same results with the same sample data

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Main ML Techniques
Classification k-nearest neighbors, decision tree, random
forest, rule induction, naive Bayes, Bayesian
networks, neural networks, support vector
machines, ensembles, boosted trees
Regression Linear, logistic, local, nonlinear, GLM
Clustering K-means, hierarchical, density, distribution
based, Latent Dirichlet Allocation (LDA)
Dimension Reduction Principal component analysis (PCA),
Linear Discriminant Analysis (LDA),
Singular Value Decomposition (SVD)
Outlier Detection Fraud, hacking, abnormal activity
Supervised
Learning
Model training
using labels
Unsupervised
Learning
Model training
without labels

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ML Use Cases
Supervised
Learning
Used for
predictions
Unsupervised
Learning
Used for grouping
or screening
Classification Recommendation, spam detection, market
basket analysis, online date matching, sales
opportunity scoring, equipment failure
prediction, churn, marketing leads generation,
document classification, sentiment analysis
Regression Drug response, stock returns, scenario
analysis, risk estimates, customer retention,
demand forecasting
Clustering Customer segmentation, topic modeling,
document grouping, sequence analysis, social
networks
Dimension Reduction Feature extraction & reduction, genetic
visualization, face recognition, model reduction,
correlation analysis, word co-occurrence, NLP
Outlier Detection Fraud, hacking, abnormal activity

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ML Classifiers & Techniques
k-nearest neighbors
decision tree
random forest
rule induction
naive Bayes
Bayesian networks
support vector machines
ensembles
boosted trees
neural networks
Linear regression
logistic regression
nonlinear regression
bagging
deep learning
association rules
probabilistic inference
logistic regression
influence graphs

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Supervised ML Flow
Sample
Data
Feature Matrix
Collect Labels
Selection & Extraction
Data Preparation Model Training & Tuning

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Supervised ML Flow
Sample
Data
Feature Matrix
Collect
Data Preparation
Labels
ML Algorithm
Multiple
Models
Models
Testing &
Validation
Best Classifier
ModelSelection & Extraction
Model Training & Tuning

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Supervised ML Flow
ML Algorithm
Multiple
Models
Best Classifier
Model
Predicted
Labels
New
Input
Data
ML Predictive Model
Data Preparation Model Training & Tuning
Models
Testing &
Validation
Prediction
Sample
Data
Feature Matrix
Collect Labels
Selection & Extraction

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Dataset
Train Model
Score Model
Tune &
Validate
Prepare Split
Supervised ML Flow
Test Model
Training
Data
Final
Model
Test
Data
Model
Template
Parameters
Select

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Choice of Classifier
Choice of classifier/model depends on:
A. Structural patterns needed to
understand and gain insights into
available data
B. Data type of target label:
 Categorical or yes/no: Discrete Classifiers
 Continuous: Linear Regression
 Probability estimates: naive Bayes

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Thank You
Q & A
abbas@goflek.com
Slide share/Abbas Taher