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Lesson Posted on 27 Jan IT Courses/Machine Learning

Introduction to ML

Talla Veerendranath

I am a Software Developer and a Trainer in Data Science using Python,R.

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Answered on 04/12/2019 IT Courses/Machine Learning IT Courses

Prashant G

SAP MM /S4 HANA WITH 9 YEARS OF EXPERIENCE

machine learning is something related to AI, i feel it would be tough for you to learn. i suggest go for SAP FICO/ SAP FSCM as you have Finance background it would be easy for you to learn and grab the job.
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Answered on 07 Jan IT Courses/Machine Learning IT Courses

Deepak Rattan

IT Professional Trainer with 10+ years of experience in IT industry

No, because machine learning requires knowledge of linear algebra, calculus, differential equations, statistics, programming language and many more. So , if you work on your maths poriton to strong. then you can try.
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Lesson Posted on 10/09/2019 IT Courses/Machine Learning IBPS SO/IBPS SO Professional Paper/IT/Software Engineering

Essential Architectural Patterns for a Data Scientist

Sayan Mukhopadhyay

I have 13+ yrs experience in organizations like Credit-Suisse, Paypal, CSC, CA Technology, Mphasis, TCG-Digital,...

Data is not an isolated entity. It needs to collect from some application or system, and then needs to be stored in some storage with the efficient format and after building the model on it, that model also needs to be exposed as an API to integrate with other systems. Sometimes this API needs to be... read more

Data is not an isolated entity. It needs to collect from some application or system, and then needs to be stored in some storage with the efficient format and after building the model on it, that model also needs to be exposed as an API to integrate with other systems. Sometimes this API needs to be available within specific latency around the globe. So there is much engineering involved in building an effective intelligent system and in today startup world which itself is a billion dollar sector, an organization cannot effort to hire so many experts to build an original feature in his product. So the data scientist needs to be a full stack analytic professional in the startup world. So in this chapter, we discuss some essential architectural patterns which every data scientist should know.

Potato Anti Pattern:

Tom is hired as a data scientist to an online company to build a real-time analytics product. So the very first step is to collect the data from their application. They make their storage auto-scaled using the cloud, and from the application, they push the data directly to the database. Everything looks beautiful in the test environment. They use a TCP connection to make sure there should not be any data loss. However, when they go live though they do not make any change in the main application, it goes down. The company faces a massive loss within half an hour, and Tom gets real-time feedback for his first step of the real-time analytic system, he is fired.

Now, the question is why the main application goes down when there is no change in it. If we look at it’s from classic computer science points o view, this is known as a busy consumer problem. Here the main application is the sender of data, and the database is the consumer. Now when the consumer is busy, which is a widespread scenario in any database lots of query running in it, it is unable to process the incoming data. Now, as TCP connection grantees the delivery data, the sender sends the data again and again and which load back the sender and here It is the main application. The situation is very similar when one person giving a potato to another person and receiver sending back to the sender and it is happening iteratively. That’s why it is called Potato Anti-Pattern. Below sequence diagram explain the situation visually.

 

The problem has two aspects. If the data which flows between sender and receiver is not necessary, then we can use UDP protocol which drops the data is unable to deliver. It is one reason why all network monitoring protocol like SNMP, Net-flow based on UDP. It does not load the device to do monitor. However, if the data is essential like the financial sector, then we have to put a messaging queue between sender and receiver. It acts as a buffer to track data when the receiver unable to process. However, if the queue memory becomes full, then it loses the data or put the load in the sender. There is a something called zero messaging queues or ZMQ which is nothing but UDP socket.

There are many readymade solutions in cloud platforms; we discuss detail in our chapter “Essential Cloud Pattern for Data Scientist. Below Node JS code is an example of a collector using Rabit-MQ exposed as REST API to sender and here receiver is Google Big Query.

 

Proxy Pattern and Layering:

Tom joins a new company. The company is big, so no job insecurity. Here he does not take the risk of collecting the data. Data is in a MySql server. Before that, Tom has no idea about the database. Very enthusiastically, he learned MySql. Write many queries in his code. The owner of the database is some other team and their manager like much R&D. So every Monday Tom gets a call the database changes to Mysql to Mongo then Mongo to SQL Server, and Tom has to make changes all over the code. Now Tom is not jobless, but every day he returns from office at 12 o clock night.

I think everyone says the solution is to organize the code correctly. However, I think the knowledge of Proxy and Layering pattern is handy. In the proxy pattern, instead of using raw Mysql or Mongo connector in your code, use a wrapper class as a proxy. In layering pattern, organize your code in multiple layers where a layer use method only form it’s the next lower layer. In this case, database configuration things should come in the lowest layers or core layer. In above that database utility layer which contains the queries to the database. Above that business entity layer which uses those database queries. Below python code give you a more clear picture. Now Tom know if there are any changes in database level, he has to look into core layer, if there are any changes in query he has to look into database utility layer and if there are any changes in business actors he has to look into entity layer. So his life is easy now.

Before We End:

Before we end, we put a footnote for Tom’s manager for which database is suitable for which kind of scenario. When data is highly structured, and entities have a clear and strict relationship, then relational database (Mysql, Oracle, SQL Server) is a better choice. However, when data is unstructured and unorganized, Mongo is a better choice. When data has a long textual field, and we are firing lot search in a substring of that Elastic text Search, or Solr is a better choice. Elastic Search also provides a free data visualization tool Kibana and ETL tool Logstash with it. So it is fashionable to become a full stack solution for data analytics. Sometimes data needs to be model as a graph. In that case, we require a graph database. Neo4j is very popular in the graph database as it also provides a lot of utility tool with it at a little cost. Some time we need application is speedy. In that case, we can use the in-memory database like SQLite. However, if you need to update your database from remote host SQLite does not support that. . If you want more detail please read the book “Advance Data Analytics in Python” written by Sayan Mukhopadhyayay,(link) we have a separate chapter with details of these DBs.

 

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Lesson Posted on 08/07/2019 IT Courses/Machine Learning IT Courses/Programming Languages/Python

How Machine Learning Predict things - A Short Video for absolute beginners

Damodar

I am an MBA Graduate with 10+ Years of experience in IT Industry. And I have expertise in following technologies ...

Here is a short video for Machine Learning Beginners, who want to know how Machine Learning Algorithm predict things
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Lesson Posted on 08/07/2019 IT Courses/Machine Learning IT Courses/Programming Languages/Python

Small ML Project on Simple Linear Regression

Damodar

I am an MBA Graduate with 10+ Years of experience in IT Industry. And I have expertise in following technologies ...

Here is a small Project on Simple Linear Regression
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Lesson Posted on 01/06/2019 IT Courses/Programming Languages/Python IT Courses/Machine Learning

Python programming - Applications

Sambasivarao Karlakunta

I have completed my masters in artificial intelligence and artificial neural networks. I have been working...

If you’re thinking of learning Python? Or if you recently started learning it? You may be asking yourself: "What exactly can I use Python for?" There are so many applications for python, but there are three main applications which are popular: - Data Science - Machine learning, data analysis... read more

If you’re thinking of learning Python? Or if you recently started learning it? You may be asking yourself:

"What exactly can I use Python for?"

There are so many applications for python, but there are three main applications which are popular:

- Data Science - Machine learning, data analysis etc.,

- Web Development

- Scripting

Data Science - Machine Learning:

I hope you have an idea of what machine learning is. If you don't have, let me give you a simple example.

"Any task which can't be solved by writing a set of instructions or pseudo code can be solved using Machine learning". Machine learning follows the simple rule - "Learning by examples". Let me give you an example. Let us say you want to identify a person in a picture. Can you write a program/algorithm for that? Well, you might say, Yes!. Maybe you could start by describing the person's attributes like face, height, weight, colour, etc., but how far will you go like this. Let's say that you have done a decent job and described the person in all possible ways. Now if you want to identify another guy, will you start from the beginning? Imagine how difficult it is to sit down and write every time for every person. It can be solved by using machine learning. Just throw a bunch of photos of persons to it, and it will learn how a person looks like.

Hope you got some idea out of it. Now lets come back to python.

Python is the most used programming language for machine learning. As machine learning is mostly dealing with data Python has several tools for data handling and operations on data such as Numpy, Scipy, sci-kit-learn etc.,

Web Development:

Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

These web frameworks help you create server-side code (backend code) in Python. That’s the code that runs on your server, as opposed to on users’ devices and browsers (front-end code).

Scripting:

Now comes our last and basic application scripting. The script usually refers to writing small programs that are designed to automate simple tasks. Python can be used for scripting in Arduino boards like raspberry pi; you can create secure embedded applications with python. Similarly, you can use Pygame to develop games in python.

There are a lot more applications like that. I just touched the few basic ones. Python has two versions - Python 2.x and Python-3.x. I would recommend using Python-3.x as the support for Python-2.x has ended long back.

That's all from this guys! Hope you got something out of it. Thank you!

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Asked on 08/11/2018 IT Courses/Machine Learning

Which is the best institute in Hyderabad for Machine learning?

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Asked on 17/08/2018 IT Courses/Machine Learning

How does Quora use machine learning in 2015?

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Asked on 17/08/2018 IT Courses/Machine Learning

Which are the best Machine Learning books?

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