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ml-course-7bits's Introduction

Курс по машинному обучению в 7bits

Содержание курса

Первый семестр

Лекции

  1. Введение. Базовые термины ML.
  2. Детекция аномалий.
  3. Кластеризация.
  4. Линейная регрессия.
  5. Классификация. kNN. F-score.
  6. Классификация. Байес.
  7. Введение в нейросети.
  8. Решающие деревья.
  9. Логистическая регрессия. Предсказание с вероятностями.
  10. Генерация и отбор признаков и объектов.

Практика

  1. Python, jupyter.
  2. Pandas, numpy.
  3. Визуализация. Matplotlib, seaborn.
  4. Кластеризация. Рекомендательная система. KMeans, silhouette-score.
  5. Линейная регрессия. Ridge, Lasso.
  6. Классификация. Простейший анализ текстов. KNeighborsClassifier, GridSearchCV, MultinomialNB.
  7. Нейросети - keras.
  8. Решающие деревья. DecisionTreeClassifier, DecisionTreeRegressor, BaggingRegressor, RandomForestRegressor.
  9. Метрики качества. Precision, Recall, F1, ROC_AUC, LogLoss.
  10. Kernel-trick, Ensambling.

Второй семестр

TBDL

Контакты

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