Big Data Applications: Machine Learning at Scale
- 3.8
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
Related Topics for further study
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
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Applied Data Science with Python
- Big Data Essentials: HDFS, MapReduce and Spark RDD
- Data Mining
Related Education Paths
Related Books
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.
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 assignmentsPros 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