About this Course

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100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 90 hours to complete

English

Subtitles: English, Korean

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 90 hours to complete

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Week
1

Week 1

7 minutes to complete

Welcome

7 minutes to complete
5 videos (Total 7 min)
5 videos
Course Structure1m
Meet Alexey2m
Meet Pavel37s
Meet Ilya1m
1 hour to complete

(Optional) Machine Learning: Introduction

1 hour to complete
6 videos (Total 43 min), 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 reading
Slack Channel is the quickest way to get answer to your question10m
5 hours to complete

Spark MLLib and Linear Models

5 hours to complete
11 videos (Total 94 min), 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 readings
Grading System: Instructions and Common Problems10m
Docker Installation Guide10m
Assignments. General requirements10m
4 practice exercises
Large scale machine learning. The beginning14m
Large scale regression and classification. Detailed analysis10m
Regularization and Unsupervised Techniques10m
Spark MLLib and Linear Models18m
Week
2

Week 2

2 hours to complete

Machine Learning with Texts & Feature Engineering

2 hours to complete
12 videos (Total 70 min)
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 practice exercises
Feature Enginering for Texts16m
Categorical Features & Feature Interactions6m
Spark ML Tutorial: Text Processing6m
Advanced Machine Learning with Texts8m
Machine Learning with Texts & Feature Engineering20m
Week
3

Week 3

6 hours to complete

Decision Trees & Ensemble Learning

6 hours to complete
13 videos (Total 64 min)
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 practice exercises
Decision Trees16m
Bootstrap, Bagging and Random Forest6m
Gradient Boosted Decision Trees10m
Spark ML Programming Tutorial: Decision Trees & CV6m
Decision Trees & Ensemble Learning16m
Week
4

Week 4

3 hours to complete

Recommender Systems

3 hours to complete
15 videos (Total 118 min), 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 reading
Recommender Systems. Spark Assignment10m
4 practice exercises
Basic RecSys for Data Engineers14m
Moderate RecSys for Data Engineers10m
Advanced RecSys for Data Engineers4m
Recommender Systems16m

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Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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