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

Introduction to deep learning

• 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.

© AsciiShell (Aleksey Podchezertsev), 2020.

Licensed under the Apache License, Version 2.0. See LICENSE file for more details.