The course helps you gain an advanced level understanding of Machine Learning application and algorithm like regression, clustering, classification, and prediction. It also covers deep learning and Spark Machine learning. The course includes 2 industry-based projects on designing recommendation and prediction system. It’s best suited for data scientists and analytics professionals.
4. One size never fits all…
• Improving an algorithm:
– First option: better features
• Visualize classes
• Trends
• Histograms
– Next: make the algorithm smarter (more complicated)
• Interaction of features
• Better objective and training criteria
WEKA or GGOBI
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Categories of ML algorithms
By training:
Supervised (labeled) Unsupervised (unlabeled)
By model:
Non-parametric
Raw data only
Parametric
Model parameters only
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7. Training a ML algorithm
• Choose data
• Optimize model parameters according to:
– Objective function
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Regression Classification
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Max Margin
8. Pitfalls of ML algorithms
• Clean your features:
– Training volume: more is better
– Outliers: remove them!
– Dynamic range: normalize it!
• Generalization
– Over fitting
– Under fitting
• Speed: parametric vs. non
• What are you learning? …features, features, features…
14. K-Means clustering
•Planar decision boundaries,
depending on space you are in…
•Highly Efficient
•Not always great (but usually
pretty good)
•Needs good starting criteria
15. K-Nearest Neighbor
•Arbitrary decision boundaries
•Not so efficient…
•With enough data in each class…
optimal
•Easy to train, known as a lazy classifier
16. Mixture of Gaussians
•Arbitrary decision boundaries
with enough boundaries
•Efficient, depending on number
of models and Gaussians
•Can represent more than just
Gaussian distributions
•Generative, sometimes tough to
train up
•Spurious singularities
•Can get a distribution for a
specific class and feature(s)… and
get a Bayesian classifier
21. Hidden Markov Models
•Arbitrary Decision boundaries
•Efficiency depends on state
space and number of models
•Generalizes to incorporate
features that change over time
22. More sophisticated approaches
• Graphical models (like an HMM)
– Bayesian network
– Markov random fields
• Boosting
– Adaboost
• Voting
• Cascading
• Stacking…