Data Science |
Artificial Intelligence |
Machine Learning
Real Time Projects &
Job Referral Program
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Should have completed Term 1,2 and Term 3 of our program (Refer
Course brochure for details)
Should have more than 1 Years of work experience (in any Domain)
Should have scored passing marks in IBM final Certification exam
Should have completed 70% of Assignments and case studies
At-least completed 2 Projects (Mentored and guided by our expert)
Course Brochure for Data Science & AI
After
completion of
your program
you have to
pass final exam
to get IBM
Certificate.
Attend project
sessions from
industry
experts to get
a hands on
experience of
real time
projects.
After
certification
and project
session update
your resume.
Start preparing
youself with
mock interviews
and guided
interview
sessions.
Once you get
eligible, you will
start getting
guarenteed
Interview Calls
JOB ASSISTANCE PROGRAM
Data Science | Artificial Intelligence | Machine Learning
ELIGIBILITY CRITERIA
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1
Certificate
2
Project
3
Resume
Update
4
Preparation
5
Job Referrals
Course Brochure AI & Machine Learning

Students Placed in Top MNC's
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SUCCESS STORIES
Students Placement & Reviews
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Bikash Bhuyan
Data Scientist at Shell
All the faculties/trainers are
superb.They know the concepts of
their respective areas.. They are
well versed that what a new
comer wants to know &
understand..Really a superb
institute & awesome
trainers.Outstanding institute for
Data Science for professionals.
Senior Data Scientist at EY
Srikanth Saurav
Machine Learning concepts &
Statistics are very well explained by
Utkarsh. Best thing was completing
the syllabus on-time as they have
promised. Trainers are clearing the
doubts . Got multiple joining offers
from different MNCs for Data
Science and AI developer
1 Y E A R
S U B S C R I P T I O N
I Joined Learnbay as Fresher
And Attended training in data
science And Artificial
Intelligence.Course is job
oriented, Practical and in-
depth .To the point, well
versed trainers, well
engineered course. Superb!!
LB does what it says.They have good
faculties for machine learning,
statistics, python and good project
sessions as well. Krishna and Abhisekh
helped till I got placed. I have got
multiple offers after doing the course
from here and some extra effort from
my end as well.So nothing is bad about
it.In one word I would say it’s excellent
Manu Agrawal
Working at Microsoft
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Learnbay is one of the best
institutes in Bangalore. The faculty
members are experienced working
professionals and they help you to
build the concepts in order to
achieve your goals. The whole
course and practical sessions are
very helpful specially in the field of
data science.
Senior Associate at Affine
I have taken Data Science course
from Learnbay 3 years back, it is
Excellent training center. After my
training I was equal to 3+ exp. I had
a very good trainer , Real-Time
Project Oriented Classes, but one
thing I have to say to all that daily
practice is very much needed.
Aswini Dindukurthy
Working at Deloitte
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Shakti Suwan
Lead Analyst at Amex
Afrin Sultana
Data Analyst at Fossil
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Keerti Bafna
Data Scientist at Antuit
I joined the Data Science batch of
September 2018. The trainer was
Amritansh. And since then i have
evolved in Machine Learning
drastically . The trainer is very
educated and teaches passionately
The staff is supporting and you can
re-attend and switch classes anytime
Everything about this program is
credible
If you miss any class you can watch
recorded sessions
All practice and real time codes
are available in repository and the
best part is you can shift batches
as per your convenience
Dhruv Satyam
Working at Infosys
My journey with Learnbay has
excelled me, bringing out my talent
to its utmost. I would personally like
to thank Krishna, Pankaj, Utkarsh
and all the people whose efforts
are involved with this institute. The
trainers here are very friendly and
professional at the same time.
Rahul Anand
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E-CommerceAutomotive Finance
Banking
Telecom HealthCare
Manufacturing
Supply
Chain
NLP
Social
Media
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Real-time Industry Projects
by applying datasets of different companies from various domains
Project Sample Recordings
Rapido Project - Getting insights of top 50 user with
most booking, bottom 50 users,frequent pickup points
etc. Performing feature engineering, data anomaly, etc.
Human Activity Recognition using Smart phone Data Set
Each person performs 6 action (walking,walking upstairs,
walking downstairs, sitting, lying) which is captured in Samsung
Galaxy ||.
Customer Segmentation Case Study - Identifying
Customer Segments with high likelihood of conversion
for Loan Offers.

Data Set : IBM Employee Data
Problem Statement : An analysis of IBM HR data
based on attrition of employee performance.
Powers the Human Resource department by precisely
identifying on masked inconsistency of the employees.
The model catches the variation in graph of
performance of more than 1400 employees.
Regression techniques and different tests will be
applied for accurate prediction of attrition rate
and its dependencies based on various factors
present in the data set.
The goal is to maintain the quality in result by
maintaining the quality of delight in the coworkers.
Data Set : Tableau Dashboard
Problem Statement : For swift and reliable update
of dynamic data with the help of Tableau.
Uber, Ola, Rapido, and other such ride-sharing
systems have become a norm in urban transportation.
The goal here is to create an interactive dashboard
using Tableau, the Dashboard of which can be used
to get historical insights into a neighborhood.
The purpose of this model will to Obtain dynamic
data, Google Cloud storage system, Real-time and
Transform, Modeling and Evaluation.
2. Forecasting Uber/
Rapido Demands
1. IBM HR Analytics
Domain | Human Resource
Domain | Demand/Supply
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Data set : Health care software
Problem statement : Recognition of Human Activity
using the smart gesture sensitive software in phones
and gadgets.
Activities like walking, racing, upstairs and downstairs
movement and such will be tracked by the mobility of
smart gadgets like Samsung Galaxy II.
The core process is of breaking down the data
collected by the system and tracking the result by
analyzing them.
Data Set : Media service providers
Problem Statement : Easy, Responsive and
Intelligent search engine and recommendation
bar of Netflix.
The project focuses on algorithms that
understands country-specific contents,
smart identification of contents on text
based input, relation of contents with
similar data of cast, makers and popularity.
3. Netflix Movies
and TV Shows
4. Analyzing Health
Data and tracking
human activity
Domain | Entertainment
Domain | Health Care
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Data Set : Amazon Data set
Problem statement : Classification of Amazon
products according to their rate of likes and dislikes.
Among the several other products present in the e-
commerce site Amazon, this model will classify
each product based on the review it beholds.
It will constantly keep track on the number of
ratings each product gets and will update the
result simultaneously in the data set.
Maps the keywords in the review text against the
review ratings to help train sentiment models.
Data Set : Supply Chain of Data sets
Problem Statement : Analyzing the supply chain of
Data sets applying Data Analytics
A Data set of Supply Chains was used by company
DataCo Global for the Analysis Report. It uses the
Machine Learning Algorithms and R Software in
areas of important registered activities like
Provisioning, Production, Sales, Commercial
Distribution. It also allows the correlation of
Structured Data with Unstructured Data for
knowledge generation.
6. Smart Supply
Chain for Big
Data Analysis
5. Custumer
Reviews on Amazon
Products
Domain | E-Commerce
Domain | Supply Chain
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Data Set : Sales Forecasting
Problem Statement : Maintaining the sales in retail by
prior predictions.
This model stores the dataset of the Walmart's in which
entire details of each department of every Walmart store
will be present. Each store's selected holidays, marked
events, unusual events will lower the sales to some
extent, the model works on this regard, predicts by
analyzing the rate of decrease in sales based on such
data. This will keep the retailers notified to take
precautionary actions.
Data Set : Testing of the products
Problem Statement : To ensure the quality, safety and
reliability of the products.
Bosch, one of the world's leading manufacturing
companies, has an imperative to ensure its products are
of the highest quality and are of safety standards.
The model is used to predict internal failures using
thousands of measurements and tests made for
each component along the assembly line. This
would enable Bosch to bring quality products at
lower costs to the end-user.
8. Bosch Production
Line Performance
7. Walmart Sales
Forecasting
Domain | Retail
Domain | Supply Chain
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Data Set : ChatBot's
Problem Statement : Knowledgeable Chatbot's with
advanced NLP intelligence.
ChatBot's are the new age customer service providers
because of their ability to serve various queries of various
users, they are used in smart electronic devices, websites
and software applications.
This model works in more efficiency by the implementation
of NLTK library in python, to perform tasks such as
automatic summarization, translation, named entity
recognition, relationship extraction, sentiment
analysis, speech recognition, and topic segmentation.
Data Set : Airbnb
Problem Statement : Predicting the probability of
the clients desire of travel destination by following
the patterns of Data.
The model accurately predicts to which destination a
new traveler may desire to book the vacation. With such
accurate data Airbnb can share more personalized
content with their community, decrease the average
time to first booking, and better forecast demand.
10. Airbnb New
User Bookings
9. Generating a
ChatBot
Domain | NLP
Domain | Travel & Hospitality
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Data Set : BMW Dataset
Problem Statement : Predict the value of a used
car
The data provided consists of almost 5000 real
BMW cars that were sold via a B2B auction in 2018.
With the help of statistical approach the model
will evaluate the risks and pros of each vehicle
on terms of its manufactured date, for how
much years been used, efficiency, historical
data of the service and more such.
Data Set : Banking & Finance Dataset
Problem Statement : Catching of the pseudo or
fraudulent
The model helps in recognizing fraudulent credit card
transactions or a disguised transaction with by the
processes like Data Cleaning, Data Visualization,
Insights generation, Model generation, Feature
Engineering and so on.
12. Credit Card
Fraud Detection
11. BMW Pricing
Challenge
Domain | Automotive
Domain | Finance
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Data Set : Statistical take on Youtube
Problem Statement : Daily statistics for trending
YouTube videos
This dataset will be collected using the YouTube API,
by which the daily record of the top trending YouTube
videos will be analyzed and be kept on track.
Things the algorithm catches:
Sentiment analysis in a variety of forms,
categorizing YouTube videos based on its
comments and statistics, training of ML
algorithms like RNN's to generate their own
YouTube comments, analyzing what factors affect
on a popular YouTube video, Statistical analysis
over time.
Data Set : Telecom Dataset
Problem Statement : Identifying And Predicting of
Customer churn in telecom industry
The goal is to develop a churn prediction model which
assists telecom operators to predict customers who are
most likely subject to churn. Also to understand the
customer behavior and reasons for churn. Apply
multiple classification models to predict the
customer churn in telecom industry.
13. Trending YouTube
Video Statistics
Domain | Social Media
14. Identify And Predict
Customer churn in
telecom industry
Domain | Telecom
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Data Set : LSTM
Problem Statement : Building and training the LSTM
model for Stock Price Prediction.
Determining the future value of a company stock or other
financial instrument traded on an exchange. Prediction of
the Closing Stock Price of a given Company.
Focusing on:
Rolling Linear Regression, ARIMA, Neural Networks,
LSTM, Momentum/Mean-Reversion Strategies,
Security clustering, portfolio construction, hedging,
ROI.
Data Set : Credit Risk
Problem Statement : Process of Credit and
Loans handled by Machine Learning.
Credit Risk is the nightmare every banks faces,
with the help of Machine Learning it can be
soughted with organised way.
The model identifies the capable and reliable
clients on basis of their behavior of
transactions, loan history, payment sincerity
and other such.
15. Home Credit
Default Risk
Domain | Banking
16. Predicting Stock
Prices Using LSTM
Domain | Stock Market
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