Big Data Applications: Machine Learning at Scale by Yandex via Coursera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

Machine Learning (ML) is rapidly transforming the world. The Big Data Applications: Machine Learning at Scale certification programme is an advanced-level online course to provide you with comprehensive ML knowledge. During the course, you will explore what type of real-life problems can be solved with ML.

Offered by Yandex, the Big Data Applications: Machine Learning at Scale training by Coursera will equip you with skills to build ML models through practical assignments. You will also learn how to create your recommender system.

Throughout the Big Data Applications online training’s five-week duration, you will go through extensive reading material, practise exercises and quizzes. It will also include exclusive video lessons delivered by top-rated instructors.

Coursera’s Big Data Applications online course will educate you on the various applications of Big Data and ML. The skills you develop will enable you to undertake different kinds of pragmatic machine learning tasks. Upon course completion, you can also earn the coveted e-certificate, which is purchasable.

Course and certificate fees

Type of course

Free

You can enrol in the Big Data Applications: Machine Learning at Scale course for free
If you want the certificate and access to graded items, you must pay a fee of Rs. 2,127

Big Data Applications: Machine Learning at Scale fee structure

Course option	Fee
Big Data Applications: Machine Learning at Scale (Course content audit)	Free
Big Data Applications: Machine Learning at Scale (Monthly subscription with certificate)	Rs. 2,127

certificate availability

Yes

certificate providing authority

Yandex

certificate fees

₹2,162

Eligibility criteria

Certification qualifying details - You can get the Big Data Applications certification only when you complete the course entirely.

What you will learn

Machine learning Knowledge of big data

By the time you complete the Big Data Applications: Machine Learning at Scale certification syllabus, you will be able to:

Create, tune and implement linear models with Spark MLLib
Fit decision trees and enhance them with ensemble learning
Build and implement linear models for regression and classification tasks
Recognise pragmatic problems that can be solved with ML
Understand text processing methods
Build a recommender system

The syllabus

Week 1 Welcome

Machine Learning Applications for Big Data
Meet Ilya
Meet Pavel
Meet Alexey
Course structure

Week 2 Machine learning with texts and feature engineering

Welcome
N-grams
Feature Engineering for Texts – part 1
Feature Engineering for Texts – part 2
Hashing trick
Feature Interactions
Spark ML. Categorical Features
Word2Vec
Categorical Features
Topic Modeling. LDA.
Spark ML. Feature Engineering for Texts - part 1
Spark ML. Feature Engineering for Texts - part 2

Week 3 Decision tree and ensemble learning

Welcome
Decision Trees Basics
Decision Trees for Classification
Bootstrap & Bagging
Gradient Boosted Decision Trees: Intro & Regression
Stochastic Boosting
Spark ML. Decision Trees & Ensembles
Decision Trees for Regression
Decision Trees: Summary
Random Forest
Gradient Boosted Decision Trees: Classification
Gradient Boosted Decision Trees: Usage Tips & Summary
Spark ML. Cross-validation

Week 4 Recommender systems

Recommender Systems, Introduction. Part 1
Recommender Systems, Introduction. Part 2
Content-Based Recommender Systems
Collaborative Filtering RecSys: User-User and Item-Item
RecSys: MF 1
RecSys: MF 2
RecSys: Hybrid 1
RecSys: Hybrid 2
Non-Personalised Recommender Systems
Recommender System Evaluation
RecSys: SVD 1
RecSys: SVD 2
RecSys: SVD 3
RecSys: iALS 1
RecSys: iALS 2

Week 5 Recommender systems (practice week)

Admission details

Step 1 – Go to land on the Big Data Applications: Machine Learning at Scale online course page.
Step 2 – Access the signup window by selecting the ‘Enroll for Free’ button.
Step 3 – Sign up for an account using your email ID and tap the ‘Join for Free’ button. You can also link an active Facebook or Google ID to register.
Step 4 – After registration, a pop-up window will appear with two options - ‘Purchase Course’ and ‘Audit only’. If you buy the curriculum, you must pay its required fee. Alternatively, you can join the Big Data Applications programme directly if you go with the audit option.

Filling the form

The Big Data Applications online programme enrolment requires no application fill-up. Log on to the Coursera website, start an account and access the course. You simply need to enter your name, email address, and password to register. As a Coursera member, your current Google/Apple/Facebook/Coursera credentials are valid for signing in.

Scholarship Details

Coursera’s Big Data Applications programme comes with an option of financial aid. For this, you can tap the ‘financial aid available’ hyperlink on the course page. It will take you to the application form after you agree to Coursera’s terms.

You must enter important information on the form page, including your payment capacity, educational background, employment status and annual income. You should also specify if you want a low-interest loan to pay the Big Data Applications: Machine Learning at Scale fee.

There will be two questions that you must answer in 150 words or more. First, how will the Big Data Applications course help you realise your goals? Finally, why are you applying for financial support? It will take approximately 15 days to review your scholarship application after submission.

How it helps

The Big Data Applications training will provide you with practical assignments, video lessons and other learning material through a digital platform. Thus, you will have 24/7 access to the modules so you can learn according to your schedule.

Also, you can purchase the Big Data Applications: Machine Learning at Scale certificate after completing the course. This e-certificate will showcase your skills and learning. You can add it to your resume or LinkedIn profile to land lucrative job profiles.