Personal notebooks for IAD course
Introduction to machine learning
- hw_01 html: Numpy, Pandas and Matplotlib
- hw_02 html: Self-made linear regression with regularization
- hw_03 html: Text preprocessing (BOW, TFIDF, stemming), analyzing and logistic regression training
- hw_04 html: Gradient boosting with LightGBM and Catboost, feature selection, categorical features, blending, feature importance
- hw_01 html Optimization methods (SGD, momentum, ADAM)
- hw_02 html Tensorflow and MNIST
- hw_03 html Tensorflow CNN for CIFAR-10
- hw_04 html Chinese character recognition competition
- hw_05 html Sentiment analysis competition
Applied problems of data science
- hw_01 html Articles RecSys; SVD, LightFM
- hw_02 html Statistic, AB testing, hypothesis testing
- hw_03 html Time series. COVID-19 analytics. Exploratory data analysis. Time series forecasting.
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