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Lesson Posted on 19/05/2020 Learn Machine Learning +1

Talla Veerendranath

I am a Software Developer and a Trainer in Data Science using Python,R. I have an experience in project...

Regularization In Machine Learning, Regularization is the concept of shrinking or regularizing the coefficients towards zero. It helps the model to prevent overfitting. Overfitting in Machine Learning is referred to as an algorithm while getting model has a lot of features or a low number of observations.... read more

Regularization

In Machine Learning, Regularization is the concept of shrinking or regularizing the coefficients towards zero.

It helps the model to prevent overfitting.

Overfitting in Machine Learning is referred to as an algorithm while getting model has a lot of features or a low number of observations.

A linear model tends to overfit and when an algorithm gets modelled has a lot of features or a low number of observations, at that time the variable selection becomes tricky.

In Machine Learning, Regularization is achieved by the help of:

1. Lasso Regression (L1)

2. Ridge Regression (L2)

3. Elastic Net Regression (L1+L2).

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Lesson Posted on 11/05/2020 Learn Machine Learning +1

Talla Veerendranath

I am a Software Developer and a Trainer in Data Science using Python,R. I have an experience in project...

Linear Regression A Linear regression is a Regression Analysis technique which is used for modeling the predictions on the continuous data. A Linear Regression can be modelled using 1. A Simple Regression technique 2. A multi regression technique 1. Simple Regression: It is a kind of regression... read more

Linear Regression

A Linear regression is  a Regression Analysis technique which is used for modeling the predictions on the continuous data.

A Linear Regression can be modelled using

1. A Simple Regression technique

2. A multi regression technique

1. Simple Regression:  It is a kind of regression technique where we have a single independent variable(X) and a single dependent variable(Y).

The main aim of this kind of modelling is to develop a regression line of the following form

Y=mX+c

Y -> Dependent Variable

m -> slope

X -> Independent Variable

c -> Y-intercept

2. Multi Regression:  It is a kind of regression technique where we have multiple independent variable(X1,x2,x3...) and a single dependent variable(Y).

The main aim of this kind of modelling is to develop a regression line of the following form

Y=c+m1X1+m2X2+...

Y -> Dependent Variable

m -> slope

X1,X2,X3...-> Independent Variables

c -> Y-intercept

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Lesson Posted on 27/01/2020 Learn Machine Learning

Talla Veerendranath

I am a Software Developer and a Trainer in Data Science using Python,R. I have an experience in project...

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Answered on 04/12/2019 Learn Machine Learning +1

Rigavoice English Speaking Mentor

Hi, Priya you may go for machine courses after B.com or MBA, and as per your field, you can learn the software related to Accounts.
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Answered on 10/01/2020 Learn Machine Learning +1

Yakkanti Hari Srinivasa Reddy

Expertise Architect with 19 years of experience in Data Warehousing/BI/Analytics Solution.

There will be 2.5L jobs will be created in Machine Leaning in next 3-5 years and there is so much demand in the market. I would suggest to you go for course for Business Analytics. There are course offered by IIMB. It cost you around 1Lac but once you come out you will have good career for sure. read more

There will be 2.5L jobs will be created in Machine Leaning in next 3-5 years and there is so much demand in the market.

I would suggest to you go for course for Business Analytics. There are course offered by IIMB. It cost you around 1Lac but once you come out you will have good career for sure.

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Answered on 03/01/2020 Learn Machine Learning +2

Mounika K.

Technical Trainer with 2 years of experience

People from any background can learn Machine Learning & Data Science concepts. But all it requires is you need to stay focus and continuous practice. It can be applied in any domain like Finance, Marketing, because all you do is analyzing the data with the help of tools lik Python, R, SAS, etc.,... read more

People from any background can learn Machine Learning & Data Science concepts. But all it requires is you need to stay focus and continuous practice. It can be applied in any domain like Finance, Marketing, because all you do is analyzing the data with the help of tools lik Python, R, SAS, etc.,

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Answered on 07/01/2020 Learn Machine Learning +1

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 Learn Machine Learning +1 Software Engineering

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 Learn Machine Learning +1

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 Learn Machine Learning +1

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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