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Big Data Applications: Machine Learning at Scale

Approx. 28 hours to complete

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Course Summary

Learn how to apply machine learning to big data and solve real-world problems in this course. Gain hands-on experience with tools like Apache Spark, Hadoop, and Python.

Key Learning Points

Understand how to apply machine learning algorithms to big data sets
Gain hands-on experience with popular big data tools like Apache Spark and Hadoop
Learn how to solve real-world problems using machine learning techniques

Learning Outcomes

Apply machine learning algorithms to big data sets
Use popular big data tools like Apache Spark and Hadoop
Solve real-world problems using machine learning techniques

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming in Python
Familiarity with big data tools like Apache Spark and Hadoop

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Hands-on projects

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Description

Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? The answer is one button away.

During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system. As a practical assignment, you will - build and apply linear models for classification and regression tasks; - learn how to work with texts; - automatically construct decision trees and improve their performance with ensemble learning; - finally, you will build your own recommender system! With these skills, you will be able to tackle many practical machine learning tasks. We provide the tools, you choose the place of application to make this world of machines more intelligent. Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting.

Outline

Welcome
Machine Learning Applications for BigData
Course Structure
Meet Alexey
Meet Pavel
Meet Ilya

(Optional) Machine Learning: Introduction
(Optional) Intuition
(Optional) Basic concepts
(Optional) Types of problems and tasks
(Optional) Supervised learning
(Optional) Unsupervised learning
(Optional) Business applications of the machine learning
Slack Channel is the quickest way to get answer to your question

Spark MLLib and Linear Models
Introduction to large scale machine learning
First example. Linear regression
How MLlib library is arranged
How to train algorithms. Gradient descent method
How to train algorithms. Second order methods
Large scale classification. Logistic regression
Regularization
PCA decomposition
K-means clustering
How to submit your first assignment
How to Install Docker on Windows 7, 8, 10
Grading System: Instructions and Common Problems
Docker Installation Guide
Assignments. General requirements
Large scale machine learning. The beginning
Large scale regression and classification. Detailed analysis
Regularization and Unsupervised Techniques
Spark MLLib and Linear Models

Machine Learning with Texts & Feature Engineering
Welcome
Feature Engineering for Texts, part 1
Feature Engineering for Texts, part 2
N-grams
Hashing trick
Categorical Features
Feature Interactions
Spark ML. Feature Engineering for Texts, part 1
Spark ML. Feature Engineering for Texts, part 2
Spark ML. Categorical Features
Topic Modeling. LDA.
Word2Vec
Feature Enginering for Texts
Categorical Features & Feature Interactions
Spark ML Tutorial: Text Processing
Advanced Machine Learning with Texts
Machine Learning with Texts & Feature Engineering

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

Recommender Systems
Recommender Systems, Introduction. Part I
Recommender Systems, Introduction. Part II
Non-Personalized Recommender Systems
Content-Based Recommender Systems
Recommender System Evaluation
Collaborative Filtering RecSys: User-User and Item-Item
RecSys: SVD I
RecSys: SVD II
RecSys: SVD III
RecSys: MF I
RecSys: MF II
RecSys: iALS I
RecSys: iALS II
RecSys: Hybrid I
RecSys: Hybrid II
Recommender Systems. Spark Assignment
Basic RecSys for Data Engineers
Moderate RecSys for Data Engineers
Advanced RecSys for Data Engineers
Recommender Systems

Recommender Systems (practice week)
Recommender Systems. Spark Assignment

Summary of User Reviews

Discover how to apply machine learning to big data problems with this Coursera course. Users rave about the practical approach to learning and the knowledgeable instructors. Many found the hands-on assignments to be particularly effective.

Key Aspect Users Liked About This Course

hands-on assignments

Pros from User Reviews

Practical approach to learning
Knowledgeable instructors
Great hands-on assignments
Excellent support from the community
Great introduction to machine learning

Cons from User Reviews

Some concepts may be difficult to grasp without prior knowledge
Some users found the course to be too short
Not enough detail on some topics
Some users found the course to be too basic
Some users experienced technical issues with the platform

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