Acerca de este Curso

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Certificado para compartir
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Fechas límite flexibles
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Nivel avanzado
Aprox. 30 horas para completar
Inglés (English)
Certificado para compartir
Obtén un certificado al finalizar
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. 30 horas para completar
Inglés (English)

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Yandex

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

7 minutos para completar

Welcome

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

(Optional) Machine Learning: Introduction

1 hora para completar
6 videos (Total 43 minutos), 1 lectura
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 lectura
Slack Channel is the quickest way to get answer to your question10m
7 horas para completar

Spark MLLib and Linear Models

7 horas para completar
11 videos (Total 94 minutos), 3 lecturas, 5 cuestionarios
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 beginning30m
Large scale regression and classification. Detailed analysis30m
Regularization and Unsupervised Techniques30m
Spark MLLib and Linear Models30m
Semana
2

Semana 2

4 horas para completar

Machine Learning with Texts & Feature Engineering

4 horas para completar
12 videos (Total 70 minutos)
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 Texts30m
Categorical Features & Feature Interactions30m
Spark ML Tutorial: Text Processing30m
Advanced Machine Learning with Texts30m
Machine Learning with Texts & Feature Engineering30m
Semana
3

Semana 3

9 horas para completar

Decision Trees & Ensemble Learning

9 horas para completar
13 videos (Total 64 minutos)
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 Trees30m
Bootstrap, Bagging and Random Forest30m
Gradient Boosted Decision Trees30m
Spark ML Programming Tutorial: Decision Trees & CV30m
Decision Trees & Ensemble Learning30m
Semana
4

Semana 4

4 horas para completar

Recommender Systems

4 horas para completar
15 videos (Total 118 minutos), 1 lectura, 4 cuestionarios
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 lectura
Recommender Systems. Spark Assignment10m
4 ejercicios de práctica
Basic RecSys for Data Engineers30m
Moderate RecSys for Data Engineers30m
Advanced RecSys for Data Engineers30m
Recommender Systems30m

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