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Machine Learning - Empatika Open
This document provides an overview of machine learning concepts and processes. It discusses types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. It also outlines the typical machine learning process of data collection/preparation, modeling, training models, evaluating results, and improving models. Specific examples discussed include using machine learning for email marketing conversion prediction, rhythmic gymnastics image classification, and recommendation systems.
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Machine Learning - Empatika Open
- 1. Machine Learning Bayram Annakov,Empatika Open
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- 11. Baby first steps •GOAL: better purchase conversion from Trial Emails • Knowledge: internal Empatika Open • Whats next? • Load new data & apply
- 12. First results -I’m genius! KNN: 97% score on test set
- 13. … & firstdisappointment Too many Negative - unbalanced dataset Balanced: 64% Better do something: email conversion 2 times less
- 14. Start from scratch + How: 1.Small dataset (own) • Time • Value 2. Balanced 3. Don’t hurry lots of answers
- 15. Process Data Model 1 Model2 Model N……… Results 1 Results 2 Results N Reducing features size Scaling … other data stuffBest result Train model (parameters) ………
- 16. Results Test dataset New dataset 30%better 7% better Email conversion 2 times better Vs. previous model 416 different inputs
- 17. Next level • Features •Volume • Understand Model parameters • Train model harder (24/7) • Whole picture: not only 1 score, but Precision, Recall, f1-score, etc.
- 18. Lessons & Knowledgesource • Think about features (valuable VS lots VS less) balance • Models are sensitive to different data • Model tuning is important, but long road • Sources: • O’REILLY: Introduction to Machine Learning with Python • scikit-learn.org • Github
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- 20. Process Data collection &preparation Modeling Training Evaluation
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- 26. Approach • Collect data SimpleiPhone app that helps draw and export • Prepare data Image = Grid. Each cell = 1 (black) or 0 (white) Convert Grid to Line Image = 000100011000011100011… • Train + Analyze Until satisfied with the score
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- 29. Train and Analyze 1.K-neighbors 78% from sklearn.neighbors import KNeighborsClassifier clf = KNeighborsClassifier(n_neighbors=1) clf.fit(x_train, y_train) clf.score(x_test, y_test)
- 30. Train and Analyze 2.K-neighbors + PCA 81% from sklearn.decomposition import PCA pca = PCA(n_components=40, whiten=True) pca.fit(x_train) x_train_pca = pca.transform(x_train) x_test_pca = pca.transform(x_test) //repeat KNN
- 31. May be someonehas already solved it?
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- 33. 3. SVM 90% fromsklearn import svm classifier = svm.SVC(gamma=0.001) classifier.fit(x_train, y_train) predicted = classifier.predict(x_test)
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- 38. Problems with images Too bigvectors (200x200x3 = 120,000) Pixel position matters
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- 62. Attributes Proprietary data sets Domain-specifictasks Domain-specific knowledge
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- 64. Useful links “The MasterAlgorithm” Andrew Ng “AI is new electricity” fast.ai course “Introduction to ML with Python” “Python Machine Learning”
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- 66. Please donate anysum to any fund
- 68. Plans 3 universities inParis Crowdfunding Platform
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- 71. How you canhelp? Finances Introductions Ideas Expertise Media Tech even frequent flyer miles :)
- 72. Thanks Lucy Evstratova +79165884397 Unicore.pro AlfaBank 4154 81200093 9516 Sberbank 4276 3800 1234 3302
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