Are you confused with multiple terminologies, multiple articles on ML, AI, data science? Yes.. so Let’s make it simple to understand all this…
Everyone knows about Google self-driving car so let’s understand all this with the self-driving car…
Now you understand what AI, ML, DS help to make better human life. Everyone heard about data analysis. Every developer in the IT industry does some kind of data analysis so each IT person is a data analyst. Basically, data analysis is just a process to understand data in a better way.
A cycles industry business wants the answer of “What percentage of profit made in the year 2018?”. A data analyst acquires data from the IT department for the year 2018 then he does some kind of data cleaning (removing not require columns, conversion of integer to decimal for accuracy). Based on data he will plot some graph and shows all this finding to business. A business/sales department will take actions according to his data insights.. (Ah.. now you have feeling oh… this is very Simple… ).
In real life which problems we solve using ML in eCommerce(A emerging tech field)?
Ocado uses ML to prioritized customer emails to give the response. The customer service receives thousands of email each day. It is very difficult to give a response all in once. ML help to categorize all these emails. It helps to determine which ones needed to answer first and which could wait.
You can use ML, data science to develop recommendations system, sentiment analysis, predictive analysis and much more in eCommerce ..
Now let’s understand the basics of ML?
A curious mind always asks these questions (How, Why, What).
Every day, there is a lot of data generated per day. If we stored the data generated in a day on Blu-ray disks and stacked them up, it would be equal to
the height of four Eiffel towers! Machine learning helps analyze this data easily and quickly. It helps find hidden data patterns and relationships, and
extract information to enable information-driven decisions and provide insights.
ML start with below 5 steps
- Understand the problem & data set.
- Extract the feature from data sets
- Identify problems type
- Chose right models
- Train & test models
There are 2 types of ML 1)supervised 2) Unsupervised
The goal of supervised learning is the generalized data set where unsupervised is a represent data a meaning way. (Hmm… Not understand? )
Let me know if this article is helpful?