I find the two fields inseperable, the whole concept of machine learning is premised on optimization. This is why I delved into machine learning in the first place.
Machine Learning and Big Data as such have no direct relation. Although one can say that Big Data Techniques can be used in Machine Learning. I will tell you the difference between both the fields for you to understand better. Machine Learning usually works with huge chunks of data and this where Big Data comes into picture.
Machine learning -
Machine Learning is the science of creating algorithms and program which learn on their own. Once designed, they do not need a human to become better. Some of the common applications of machine learning include following: Web Search, spam filters, recommender systems, ad placement, credit scoring, fraud detection, stock trading, computer vision and drug design. An easy way to understand is this - it is humanly impossible to create models for every possible search or spam, so you make the machine intelligent enough to learn by itself. When you automate the later part of data mining - it is known as machine learning. The term machine learning is self explanatory. Machines learn to perform tasks that aren’t specifically programmed to do. Many techniques are put into practice like supervised clustering, regression, naive Bayes etc.
Machine learning is just a part of data science. Data science is a big umbrella covering each and every aspect of data processing and not only statistical or algorithmic aspects. To mention, data science includes
- data visualization
- data integration
- dashboards and BI
- distributed architecture
- automated, data-driven decisions
- automating machine learning
- deployment in production mode
- data engineering
Machine learning helps data science by making a provision for data analysis, data preparation and even decision making like real time testing, online learning. Data science clubs together algorithms derived from machine learning in order to provide a solution. Data science carries out this activity by taking a lot of ideas from basic mathematics, statistics and domain expertise.
Big Data Analytics
Big Data Analytics is studying large datasets (big data) to identify hidden patterns, market trends, consumer preferences and other valuable information helping organizations to form strategic business decisions.
With Big data analytics, data scientists and other analytics professionals can examine huge amounts of structured data as well as the untapped data by deploying analytics and business intelligence.
Big Data Analytics comprises of specialized software and analytics systems benefiting business in many ways like
- Cost efficiency: Hadoop and cloud based analytics are big data analytics technologies are very cost effective when storing huge amounts of data. Moreover, this also helps in finding more effectual ways of doing business.
- Faster decision making: Organizations can examine data immediately with superfast Hadoop and in-memory analytics. Decisions can taken with much ease on the basis of what they have experienced.
- New products and services: Big data analytics helps to easily understand consumer needs and preferences giving more power to serve customers what they want. More products and services can be developed to fulfill customer’s needs.
@ Venkatesh Gauri Shankar Can we work in BigData optimization problem, solving ML or others Algo.? Problem like multi Objective.
Jesús S. Aguilar-Ruiz Sir, Can we solve multi objective problem using ML or Algorithm.
I agree to Temitayo Bankole. Also statistical methods estimation of means, regression analyses, multi linear regression, Anova and tools like Mat lab, mini tab R programming can be very helpful.
Rabel Guharoy Yes we can work together. feel free to contact me with any of proposal.
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