Acerca de este Curso
7,380 vistas recientes

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 79 horas para completar

Sugerido: 5 weeks of study, 6-8 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 79 horas para completar

Sugerido: 5 weeks of study, 6-8 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
7 minutos para completar

Welcome

5 videos (Total 7 minutos)
5 videos
Course Structure1m
Meet Alexey2m
Meet Pavel37s
Meet Ilya1m
1 horas para completar

(Optional) Machine Learning: Introduction

6 videos (Total 43 minutos), 1 reading
6 videos
(Optional) Basic concepts11m
(Optional) Types of problems and tasks5m
(Optional) Supervised learning7m
(Optional) Unsupervised learning6m
(Optional) Business applications of the machine learning4m
1 lecturas
Slack Channel is the quickest way to get answer to your question10m
5 horas para completar

Spark MLLib and Linear Models

11 videos (Total 94 minutos), 3 readings, 5 quizzes
11 videos
First example. Linear regression10m
How MLlib library is arranged10m
How to train algorithms. Gradient descent method9m
How to train algorithms. Second order methods8m
Large scale classification. Logistic regression12m
Regularization8m
PCA decomposition9m
K-means clustering7m
How to submit your first assignment3m
How to Install Docker on Windows 7, 8, 104m
3 lecturas
Grading System: Instructions and Common Problems10m
Docker Installation Guide10m
Assignments. General requirements10m
4 ejercicios de práctica
Large scale machine learning. The beginning14m
Large scale regression and classification. Detailed analysis10m
Regularization and Unsupervised Techniques10m
Spark MLLib and Linear Models18m
Semana
2
2 horas para completar

Machine Learning with Texts & Feature Engineering

12 videos (Total 70 minutos), 5 quizzes
12 videos
Feature Engineering for Texts, part 17m
Feature Engineering for Texts, part 25m
N-grams4m
Hashing trick6m
Categorical Features6m
Feature Interactions2m
Spark ML. Feature Engineering for Texts, part 17m
Spark ML. Feature Engineering for Texts, part 25m
Spark ML. Categorical Features3m
Topic Modeling. LDA.7m
Word2Vec11m
5 ejercicios de práctica
Feature Enginering for Texts16m
Categorical Features & Feature Interactions6m
Spark ML Tutorial: Text Processing6m
Advanced Machine Learning with Texts8m
Machine Learning with Texts & Feature Engineering20m
Semana
3
6 horas para completar

Decision Trees & Ensemble Learning

13 videos (Total 64 minutos), 6 quizzes
13 videos
Decision Trees Basics4m
Decision Trees for Regression6m
Decision Trees for Classification3m
Decision Trees: Summary1m
Bootstrap & Bagging8m
Random Forest6m
Gradient Boosted Decision Trees: Intro & Regression7m
Gradient Boosted Decision Trees: Classification6m
Stochastic Boosting1m
Gradient Boosted Decision Trees: Usage Tips & Summary3m
Spark ML. Decision Trees & Ensembles6m
Spark ML. Cross-validation3m
5 ejercicios de práctica
Decision Trees16m
Bootstrap, Bagging and Random Forest6m
Gradient Boosted Decision Trees10m
Spark ML Programming Tutorial: Decision Trees & CV6m
Decision Trees & Ensemble Learning16m
Semana
4
3 horas para completar

Recommender Systems

15 videos (Total 118 minutos), 1 reading, 4 quizzes
15 videos
Recommender Systems, Introduction. Part II4m
Non-Personalized Recommender Systems9m
Content-Based Recommender Systems8m
Recommender System Evaluation10m
Collaborative Filtering RecSys: User-User and Item-Item10m
RecSys: SVD I7m
RecSys: SVD II8m
RecSys: SVD III5m
RecSys: MF I7m
RecSys: MF II6m
RecSys: iALS I6m
RecSys: iALS II11m
RecSys: Hybrid I7m
RecSys: Hybrid II7m
1 lecturas
Recommender Systems. Spark Assignment10m
4 ejercicios de práctica
Basic RecSys for Data Engineers14m
Moderate RecSys for Data Engineers10m
Advanced RecSys for Data Engineers4m
Recommender Systems16m

Instructores

Avatar

Pavel Mezentsev

Senior Data Scientist
PulsePoint inc
Avatar

Alexey A. Dral

Founder and Chief Executive Officer
BigData Team
Avatar

Ilya Trofimov

Principal Data Scientist
Yandex
Avatar

Evgeny Frolov

Data Scientist, PhD Student @Skoltech
Computational and Data Intensive Science and Engineering

Acerca de Yandex

Yandex is a technology company that builds intelligent products and services powered by machine learning. Our goal is to help consumers and businesses better navigate the online and offline world....

About the Programa especializado Big Data for Data Engineers

This specialization is made for people working with data (either small or big). If you are a Data Analyst, Data Scientist, Data Engineer or Data Architect (or you want to become one) — don’t miss the opportunity to expand your knowledge and skills in the field of data engineering and data analysis on the large scale. In four concise courses you will learn the basics of Hadoop, MapReduce, Spark, methods of offline data processing for warehousing, real-time data processing and large-scale machine learning. And Capstone project for you to build and deploy your own Big Data Service (make your portfolio even more competitive). Over the course of the specialization, you will complete progressively harder programming assignments (mostly in Python). Make sure, you have some experience in it. This course will master your skills in designing solutions for common Big Data tasks: - creating batch and real-time data processing pipelines, - doing machine learning at scale, - deploying machine learning models into a production environment — and much more! Join some of best hands-on big data professionals, who know, their job inside-out, to learn the basics, as well as some tricks of the trade, from them. Special thanks to Prof. Mikhail Roytberg (APT dept., MIPT), Oleg Sukhoroslov (PhD, Senior Researcher, IITP RAS), Oleg Ivchenko (APT dept., MIPT), Pavel Akhtyamov (APT dept., MIPT), Vladimir Kuznetsov, Asya Roitberg, Eugene Baulin, Marina Sudarikova....
Big Data for Data Engineers

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.