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| Lecture-19: Ensemble learning; Bagging; Boosting; Random forests |
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| Lecture-18: Text and NLP; Sentiment Analysis |
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| Lecture-15: Unsupervised Learning example; Text and NLP |
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| Lecture-14: Clustering; K-Means algorithm |
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| Lecture-13: Decision Trees; Entropy; Information gain |
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| Lecture-12: Quiz, Decision Trees |
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| Lecture-11: Features and Dimensionality Reduction; PCA; Regression -- Linear;Logistics |
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| Lecture-10: Accuracy Metrics, k-fold crossvalidation |
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| Lecture-9: Overview of Modeling, Confusion Matrix, Accuracy Metrics |
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| Lecture-8: Regular expressions, Overview of Modeling |
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| Lecture-7: HW-3, Quiz and Strings as features |
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| Lecture-6: Visualizations and Web scraping |
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| Lecture-5: Overview of Machine Learning and Data manipulation; feature engineering |
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| Lecture-4: Numpy, Pandas and hw demo |
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| Lecture-3: Python loops, conditionals, functions; Numpy |
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| Lecture-2: Python basics: Variables and Data types |
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| Lecture-1: Course overview, logistics and intro to data science. |
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