dbadeev,Dmitry Badeev,github

CV

Skills 🛠️

Languages: Python, C
DS: NumPy, SciPy, Pandas, Scikit-Learn, Keras, Tensorflow, PySpark
Big Data: Hadoop
DevOps: Docker, Git

Education 🎓

Mathematician, Specialist, Diploma of Completion Faculty of Mechanics and Mathematics, Lomonosov Moscow State University (1980-1985)
Curriculum of Architect In Digital Technologies, Certificate of Completion 42 with the final level: Level 21, Association 42 (2019-2023)
Curriculum, Certificate of Completion 21, Senior Degree with the final level: Level 21.42, 21 School SBER (2019-2023)
Curriculum of Project Management in IT, Diploma of professional retraining, Skillfactory (2022-2023)

Projects 🐾

Python

Computor 1 - Program that solves a polynomial second or lower degree equation and plots the function (including the complex roots of the equation).

DS

Ft Linear Regression - Model implementation that predicts the price of a car depending on it's mileage using a linear regression function trained with a gradient descent algorithm w/o special Libraries and plots the data into a graph
Datascience X Logistic Regression - Implementation of multi-classifier model using a logistic regression one-vs-all w/o special Libraries; statistical data analysis; implementation of batch gradient descent, mini-batch gradient descent, stochastic gradient descent; data visualization using different methods
Multilayer Perceptron - Implementation of Multilayer Perceptron model with two hidden layers w/o special Libraries
Tweets - Sentiment analysis of tweets
Churn prediction - Implementation of various models (Naive, RandomForest, Keras, TensorFlow) using NumPy matrix calculations to predict which customers are going to stop being customers of the bank
Myspotify - Application of different type algorithms (non-personalized, content-based and collaborative filtering) used for music recommendations
City Life - Geospatial analytics based on Taxi Routes and Neighborhoods: GeoPandas, clustering, making maps
Understanding customer - Intent classification based on Deep Learning algorithms applied to NLP tasks (RNN, LSTM, BERT, CNN)
Uber - Taxi demand forecast based on time series analysis: stationarity, exponential smoothing, SARIMA
Amazon - Amazon’s recommender system – “Customers Who Bought This Item Also Bought” using Apache Spark and its 3rd-party libraries
Gender Profiling in Social Network - Gender profilng in single and cross genre tweets (Russian language)
Summarization of Habr articles - Abstractive summarization of Habr articles using YaGPT

C

LIBFT - Rewritten library of C standard functions
Fillit - Algorithmic project: Problem of finding a square of the minimum size, in which all the issued tetrimines can be placed without intersections; the solution is based on Knuth's Algorithm X using 'dancing links'
Push swap - Algorithmic project: Sorting data on a stack, with a limited set of instructions, using the lowest possible number of actions; the solution is based on original 'chunks' algorithm
Lem-in - Algorithmic project: The goal is to find the quickest way to get N ants across the farm (finding the minimum node-disjoint paths and maximum flow from a source to destination in a nondirected graph); the solution is based on Suurballe's algorithm

DevOps

Init - Introduction to System and Network Administration
Roger skyline 1 - Virtual Machine installation; the basics about system and network administration; services used on a server machine
Docker 1 - The basics for understanding the idea of containerization of services using docker and docker-machine

Security

Rainfall - An introduction to the elf-like binary exploitation in i386 system
Snow Crash - An introduction to cyber security
Over Ride - Next level tasks concerning the elf-like binary exploitation in i386 system after Rainfall project

Certifications 📜

DS

Python Programming (with honors) @ Stepic
Python: Basics and Applications(with honors) @ Stepic
Mathematics and Python for Data Analysis @ Coursera
GPT Week @ Yandex

DevOps

Introduction to Git and GitHub @ Coursera
Hadoop. System for processing large amounts of data (with honors) @ Stepik

Dmitry Badeev's Projects

dslr

Реализация классификатора на основе стратегии «один-против-всех» с помощью модели логистической регрессии, без использования специальных библиотек; статистический анализ данных; реализация пакетного градиентного спуска, мини-пакетного градиентного спуска, стохастического градиентного спуска; визуализация данных различными методами

fillit

Цель проекта - разместить заданный набор тетриминок (фигурок тетриса, состоящих из 4 блоков) в квадратной области наименьшей площади так, чтобы тетриминки не пересекались друг с другом (незаполненные «дырки» в области допускаются). При реализации использовалась интерпретация Алгоритма Х Д.Кнута с использованием техники “танцующих ссылок”.

ft_linear_regression

Реализация модели линейной регрессии без использования специальных библиотек для задачи прогнозирования цены автомобиля в зависимости от его пробега; регуляризация; MSE, R-squared statistic; визуализация функций потерь и предсказаний цены по данной выборке

dbadeev Goto Github PK

CV

Skills 🛠️

Education 🎓

Projects 🐾

Python

DS

C

DevOps

Security

Certifications 📜

DS

DevOps

Dmitry Badeev's Projects

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