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Job Guarantee or Money Back Data Science & AI Certification Course

The course has been designed specifically for working professionals with 2 to 8 years of experience who choose to advance their careers in Data Science and AI, and it comes with a 100% job guarantee or money back policy.

Eligibility

4+ yrs work exp.

Certification

Accredited with IBM

Projects

15+ Real Time Projects

No Cost EMI

₹ 8.5k/mon*(9 mon)

ABOUT COURSE
About This Program

Eligibility

Best for Professionals from Core technical and programming background having 4+ yrs of work experience.

Job Assistance

Resume sessions, Mock interviews, and job referrals to land your dream job for ML engineer, AI engineer.

Live Projects

15+ Real-time projects from multiple domains like healthcare, BFSI, retail, manufacturing, e-commerce, and AI.

Job Guarantee or Money Back Data Science & AI Certification Course

The course has been designed specifically for working professionals with 2 to 8 years of experience who choose to advance their careers in Data Science and AI, and it comes with a 100% job guarantee or money back policy.

Eligibility

4+ yrs work exp.

Certification

Accredited with IBM

Projects

15+ Real Time Projects

No Cost EMI

₹ 8.5k/mon*(9 mon)

ABOUT COURSE
About This Program

Eligibility

Best for Professionals from Core technical and programming background having 4+ yrs of work experience.

Job Assistance

Resume sessions, Mock interviews, and job referrals to land your dream job for ML engineer, AI engineer.

Live Projects

15+ Real-time projects from multiple domains like healthcare, BFSI, retail, manufacturing, e-commerce, and AI.
Key Highlights
Program Overview

  For Professionals from coding domain

  Real-Time Use Cases from Multiple Domains

  Industrial Certificate which is accredited with IBM

  One-on-One with Industry Mentors

  Guaranteed Job Referrals For Working Professionals

  Crack AI expert interviews in top Product based MNC

  Interview Prep Session And Mock Interview

  15+ Industry Projects & 2 capstone

  Special Classes For Non-Programming Background

   Interest Free Loan and No Cost EMI on Credit cards

Key Highlights
Program Overview

  For Professionals from coding domain

  Real-Time Use Cases from Multiple Domains

  Industrial Certificate which is accredited with IBM

  One-on-One with Industry Mentors

  Guaranteed Job Referrals For Working Professionals

  Crack AI expert interviews in top Product based MNC

  Interview Prep Session And Mock Interview

  15+ Industry Projects & 2 capstone

  Special Classes For Non-Programming Background

   Interest Free Loan and No Cost EMI on Credit cards

Data Science & AI Certification Course
Job Guarantee or Money Back
Specially crafted program for professionals having 2 to 8 year of experience with 15+ Industrial projects and 2 Capstone projects.
With a 100% job guarantee, become IBM Certified in Data Science and AI,If we fail to provide a job get total money back.
Program is led by top Industrial Experts working as a Data Scientist and AI Specialist in product-based MNC’s.
TOP SKILLS
Top Skills You Will Learn

Top Skills You Will Learn

Python for data science, Statistics, ML, Advance Deep Learning using Tensorflow, Pytorch, Keras, NLP, Computer vision, Reinforcement learning, Cloud Deployment.

Job Opportunities

AI Expert, AI Engineer, Machine Learning Engineer, Senior Data scientist in Top Product based MNCs and fintech startups

Details About Real Time Projects.

2 Capstone Project and 15+ Real-time projects from multiple domains help you to showcase practical skills to the recruiter and get your dream job.

Minimum Eligibility

Working Professionals With 4+ Years of experience in the core tech and Programming domain. Fresh graduates and non programmers are not eligible.
TOP SKILLS
Top Skills You Will Learn

Top Skills You Will Learn

Python for data science, Statistics, ML, Advance Deep Learning using Tensorflow, Pytorch, Keras, NLP, Computer vision, Reinforcement learning, Cloud Deployment.

Job Opportunities

AI Expert, AI Engineer, Machine Learning Engineer, Senior Data scientist in Top Product based MNCs and fintech startups

Details About Real Time Projects.

2 Capstone Project and 15+ Real-time projects from multiple domains help you to showcase practical skills to the recruiter and get your dream job.

Minimum Eligibility

Working Professionals With 4+ Years of experience in the core tech and Programming domain. Fresh graduates and non programmers are not eligible.

Download Program Brochure

FEATURES
Course Features
Program Eligibilty
Work Experience :
  • Working Professionals With 4+ Years of experience in core technical and programming and coding domain.
  • Should be good understanding of mathematics concept.

To know which Program is best suitable for your profile, Apply for Career counselling   session with our expert.

Who Should Apply
  • Professionals having more than 4+ yrs exp. in core technical domain.
  • Have good understanding in any programming language(Working knowledge)
  • Good understanding in mathematics.

To know which Program is best suitable for your profile, Apply for Career counselling   session with our expert.

Course Prerequisite

There is No Prerequisite for this course as we cover programming and statistics from basics. We provide special classes & support for professionals from non-programming/non-technical domains.

Special Support to Non Programmers

we provide special Python programming fundamentals classes designed for non-programmers and non-technical domain professionals which helps you to learn programming concepts easily.

About Instructors
  • Our instructors are working professionals who graduated from premier institutes and working in companies as Data Scientist/ML Engineer & AI Expert.
  • Students will be guided by our mentors until the end of a course. A student can seek help by our mentors even after course completion for knowledge and placement purposes.
Fees and Duration
Weekday Program Batches: 7.5 Months

Monday – Friday – 2 hours every day

Weekend Batches: 9 Months

Saturday & Sunday – 4 hours every day

Program Fee: Rs. 95,000/-

  Interest Free Instant Loan Without Credit Card

No Cost EMI on Major Credit Cards

Hybrid Model Classes

Attain 2 Capstone & 12+ Real Time AI and ML Projects in Classroom-mode OR Live-online led by industrial experts.
Locations: Pune, Mumbai, Delhi, Hyderabad, Chennai, Bengaluru

It is completely optional to attain project sessions in classroom. You can also prefer Live-Online Interactive Mode guided by Industrial Expert.

COURSE
Syllabus

Introduction to Programming 3hr

What is a programming language ? Source code Vs bytecode Vs machine code
Compiler Vs Interpreter ,C/C++, Java Vs Python

Jupyter notebook basics 1hr

Different type of code editors in python introduction to Anaconda and
jupyter notebookFlavours of python.

Python Programming Basics 2hr

Variable Vs identifiers Vs strings, Operators Vs operand Procedure
oriented Vs modular programming

Statistics basics 2hr

Introduction to statisticsMean,median, mode, Standard deviation,
AverageIntroduction to probability,permutations and
combinationsIntroduction to linear Algebra

Git and GitHub 2hr

Learn the key concepts of the Git source control system,Configure SSH for authentication,Create and use a remote repository on GitHub,Git Overview

Tools Covered

Programming Basics &Environment Setup hr

Installing Anaconda, Anaconda, Basics, and Introduction, Get familiar with version control, Git, and GitHub.Intro to Jupyter Notebook environment. Basics Jupyter notebook Commands.Programming language basics.

Python Programming Overview hr

Python 2.7 vs Python 3,Writing your First Python Program
Lines and Indentation,Python Identifiers,Various Operators and Operators
Precedence ,Getting input from User,Comments,Multi line Comments.

Strings, Decisions And Loop
Control
hr

Working With Numbers, Booleans and Strings,String types and
formatting, String operations Simple if Statement, if-else Statement
if-elif Statement. Introduction to while Loops. Introduction to for Loops,Using
continue and break.

Python Data Types hr

List,Tuples,Dictionaries,Python Lists,Tuples,Dictionaries,Accessing Values,Basic Operations,Indexing, Slicing, and Matrixes,Built-in Functions & Methods,Exercises on List,Tuples And Dictionary

Functions And Modules hr

Introduction To Functions – Why Defining Functions. Calling Functions
Functions With Multiple Arguments.Anonymous Functions – Lambda
Using Built-In Modules, User-Defined, Modules, Module Namespaces,Iterators And Generators

File I/O And Exceptional Handling and Regular Expression hr

Opening and Closing Files,open Function,file Object Attributes
close() Method ,Read,write,seek.Exception Handling, try-finally Clause
Raising Exceptions, User-Defined Exceptions. Regular Expression- Search
and Replace. Regular Expression Modifiers. Regular Expression Patterns,re module.

Data Analysis Using Numpy And
Pandas
hr

Introduction to Numpy. Array Creation, Printing Arrays, Indexing, Slicing and Iterating, Shape Manipulation -Changing the shape, stacking, and splitting of the array. Vector stacking, Broadcasting.

Pandas : Introduction to Pandas,Importing data into Python
Pandas Data Frames, Indexing Data Frames, Basic Operations With Data frame,
Renaming Columns, Subletting, and filtering a data frame.

Data Visualisation using Python:
Matplotlib and Seaborn
hr

Matplotlib: Introduction,plot(),Controlling Line Properties,Subplot with Functional Method, MUltiple Plot, Working with Multiple Figures,Histograms

Seaborn :Intro to Seaborn And Visualizing, statistical relationships , Import and Prepare data.Plotting with categorical data and Visualizing linear relationships Seaborn Exercise

Tools Covered

Fundamentals of Math and
Probability
hr

Basic understanding of linear algebra,Matrics, vectors. Addition and
Multimplication of matrics.Fundamentals of Probability
Probability distributed function and cumulative distributed function.

Descriptive Statistics hr

Describe or sumarise a set of data Measure of central tendency and
measure of dispersion.The mean,median,mode, curtosis and skewness. Computing Standard deviation and Variance.Types of distribution.

Inferential Statistics hr

What is inferential statistics,Different types of Sampling techniques
Central Limit Theorem,Point estimate and Interval estimate
Creating confidence interval for,population parameter
Characteristics of Z-distribution and T-,Distribution. Basics of Hypothesis
Testing. Type of test and rejection,region. Type of errors in Hypothesis yesting,

Hypothesis Testing hr

Hypothesis Testing Basics of Hypothesis Testing ,Type of test and Rejection Region,Type o errors-Type 1 Errors,Type 2,Errors. P value method,Z score
Method. The Chi-Square Test of, Independence.Regression. Factorial Analysis of
Variance. Pearson Correlation, Coefficients in Depth. Statistical,Significance, Effect Size

Data Processing & Exploratory
Data Analysis
hr

What is Data Wrangling, Data Pre-processing and cleaning?
How to Restructure the data? What is Data Integration and Transformation

Introduction To Machine Learning 8hr

What is Machine Learning? What is the Challenge?
Introduction to Supervised Learning, Introduction to Unsupervised
Learning, What is Reinforcement Learning? Machine Learning applications
Difference between Machine Learning and Deep Learning

Supervised Learning 8hr

Support Vector Machines,Linear regression,Logistic regression
Naive Bayes, Linear discriminant analysis, Decision tree
the k-nearest neighbor algorithm, Neural Networks (Multilayer perception) Similarity learning

Linear Regression 8hr

Introduction to Linear Regression,Linear Regression with Multiple
Variables, Disadvantage of Linear Models, Interpretation of Model Outputs
Understanding Covariance and Colinearity, Understanding Heteroscedasticity

Case Study – Application of Linear Regression for Housing
Price Prediction

Logistic Regression hr

Introduction to Logistic Regression.–Why Logistic Regression .
Introduce the notion of classification, The cost function for logistic regression
Application of logistic regression to multi-class classification.
Confusion Matrix, Odd’s Ratio, And ROC Curve
Advantages And Disadvantages of Logistic Regression.

Decision Trees hr

Decision Tree – data set How to build decision tree? Understanding Kart Model
Classification Rules- Overfitting Problem. Stopping Criteria And Pruning. How to Find the final size of Trees? Model A decision Tree.
Naive Bayes. Random Forests and Support Vector Machines.Interpretation of Model Outputs

Unsupervised Learning hr

Hierarchical Clustering k-Means algorithm for clustering –groupings of unlabeled data points.Principal Component Analysis(PCA)-Data. Independent components analysis(ICA) Anomaly Detection Recommender System-collaborative
filtering algorithm

Case Study– Recommendation
Engine for e-commerce/retail chain

Natural language Processing hr

Introduction to natural Language Processing(NLP).
Word Frequency Algorithms for NLP Sentiment Analysis

Case Study :Twitter data analysis using NLP

Introduction to Time Series Forecasting hr

Basics of Time Series Analysis and Forecasting ,Method Selection in Forecasting,Moving Average (MA) Forecast Example,Different Components of
Time Series Data, Log Based Differencing, Linear Regression For Detrending

ARIMA and Multivariate Time
Series Analysis
hr

Introduction to ARIMA Models,ARIMA Model Calculations,Manual ARIMA
Parameter Selection,ARIMA with Explanatory Variables Understanding Multivariate Time Series and Their Structure,Checking for Stationarity and Differencing the MTS

Case Study : Performing Time Series
Analysis on Stock Prices

Introduction to Deep Learning And Tensor Flow hr

Neural Network Understaing Neural Network Model Installing TensorFlow Simple Computation ,Contants And Variables. Types of file formats in TensorFlow. Creatting A Graph –Graph Visualization. Creating a Model– Logistic Regression Model Building using tensor flow TensorFlow Classification Examples

Introduction to Tensor Flow hr

Installing TensorFlow Simple Computation,Contents And Variables Types of file formats in TensorFlow Creatting A Graph – Graph Visualization Creating a Model – Logistic Regression Model Building TensorFlow Classification Examples

Understanding Neural Networks With Tensor Flow hr

Basic Neural Network, Single Hidden Layer Model,Multiple Hidden Layer Model,Backpropagation – Learning,Algorithm and visual representation,
Understand Backpropagation – Using Neural Network Example TensorBoard
Project on backpropagation

Convolutional Neural Network(CNN) hr

Convolutional Layer Motivation ,Convolutional Layer Application,
The architecture of a CNN, Pooling Layer Application Deep CNN, Understanding and Visualizing a CNN,Project: Building a CNN for Image Classification

Introduction to NLP & Text Analytics hr

Introduction to Text Analytics, Introduction to NLP
What is Natural Language Processing?, What Can Developers Use NLP Algorithms For?

NLP Libraries. Need of Textual Analytics Applications of Natural Language
Procession
,Word Frequency Algorithms for NLP Sentiment Analysis

Text Pre Processing Techniques hr

Need of Pre-Processing Various methods to Process the Text
data Tokenization ,Challenges in Tokenization,Stopping ,Stop Word Removal
Stemming – Errors in Stemming,Types of Stemming Algorithms -Table
lookup Approach ,N-Gram Stemmers

Distance Algorithms used in Text Analytics hr

string Similarity,Cosine Similarity Mechanishm -Similarity between Two text
documents. Levenshtein distance -measuring the difference between two sequences. Applications of Levenshtein distance LCS (Longest Common Sequence ) Problems and solutions ,LCS Algorithms

Information Retrieval Systems hr

Information Retrieval -Precision,Recall,F- score,TF-IDF,KNN for document retrieval,K-Means for document retrieval,Clustering for document retrieval

Topic Modelling & Dirchlett Distributions hr

Introduction to Topic Modelling,Latent Dirchlett Allocation,Adavanced Text Analytics & NLP,Introduction to Natural Language,Toolkit,POS Tagging NER

Projects And Case Studies hr

a. Sentiment analysis for twitter, web
articles
b. Movie Review Prediction
c. Summarization of Restaurant
Reviews

Introduction to Computer Vision hr

Introduction to Computer Vision, Computer Vision overview Historical Perspective
Introduction to the four Rs of Computer Vision

Image Processing hr

Histogram equalization,Thresholding and Convolution,Sharpening and edge detection Morphological transformations Image pyramid

Image Classification and segmentation hr

Data Driven approach,K-nearest Neighbor,Linear Classification,Contours and segmentation,Contour properties,Circle detection,Line detection,Watershed segmentation

OpenCv Library hr

Opencv Installation And Python API,Drawing shapes ,Image Processing
Image Rotation and Thresholding,Image Filtering – Gaussian Blur,Median Blur
Feature Detection – Canny Edge, Detector, Use of Neural Network in CV Multi-Layer Perceptron

Object Detection(SSD) hr

Single Shot MultiBox Detector,Object Localization,How would you find an object in an image?,The Problem of Scale and Shape SSD in Tensorflow,Haarcascade – face and eye detection

Project On Computer Vision and Opencv hr

AI Based Live Face Identification System for Crowd

Introduction To GCP Cloud ML Engine hr

Introduction to Google CloudML Engine,CloudML Engine in Machine,Learning WorkFlow,+3Components of Cloud ML Engine -Google Cloud Platform Console.
gcloud command-line tool and Rest API
API

Training Machine Learning
Model
hr

Developing a training application Packaging a training application
Running and monitoring a training job Using hyperparameter tuning Using GPUs for training models in the cloud

Deploying Machine Learning Model hr

Deploying Models ,Understanding training graphs and serving graphs,
Check and adjust model size Build an optimal prediction graph Creating input function creating a model version Getting Online Prediction

Introduction Reinforcement Learning hr

What is Reinforcement Learning -Basics Setting up Environment & Installing
OpenAI Gym OpenAI Gym Basics . Terminology & Environment
Dynamic Programming -Prediction, Control, and Value Approximation

OpenAI Gym and Basic RL Techniques hr

Building Blocks of Reinforcement Learning,OpenAI Gym Tutorial,Random Search,
Markov Decision Processes Monte Carlo Methods

Approximation Methods for Reinforcement Learning hr

RBF Networks with CartPole TD Lambda and Policy Gradient
Algorithms Temporal difference learning N-Step Methods, TD lambda ,Policy
Gradient Methods Policy Gradient in TensorFlow for
CartPole Mountain Car Continuous using Tensorflow

Deep Q-Learning Intro hr

Deep Q-Learning Techniques,Deep Q-Learning in Tensorflow for CartPole
Projects and Case Studies :
Solving Taxi Environment
Solving Frozen Lake Environment
Reward Discounting

Project – AI Based Live Face Identification System for Crowd

Domain – Face Detection

Artificial intelligence-based facial recognition systems for security
purpose . Track down criminals in crowded place like malls ,airport and other crowded public places

Project -Analyzing Health Data and tracking human activity
Domain – Healthcare

DataSet : Samsung

The goal is to breakdown all the data that the Samsung Health app has collected and see what useful insights we can gain by analyzing it.

Project -IBM HR Analytics
Domain – Human Resource
DataSet : IBM

Applying analytic processes to the human resource department of an organization in the hope of improving employee performance This is especially concerning if your business is customer facing, as customers often prefer to interact with familiar people.

Project -Consumer Reviews of Amazon Products
Domain – E-Commerce

The goal is to analyze Amazon’s most successful consumer electronics product launches, discover insights into consumer reviews. What are the most
reviewed Amazon products? How do the reviews in the first 90 days after a product launch?

Project -Self-Driving Car
Domain – Automotive

Simulate a Self-Driving Car with Convolution Neural Networks and Computer Vision. Here you will learn to use essential Computer Vision techniques to identify lane lines on a road

Project -Emotions Sensor
Domain – Machine Learning

Emotions Sensor Data Set Contain Top 23 730 English Words Classified
Statistically Using Naive Bayes Algorithm Into 7 Basic Emotion Disgust, Surprise ,Neutral ,Anger ,Sad,Happy and Fear.To Detect Emotions In Text or Voice Speech to build a Sentiment Analysis Bot

Project -Natural Language Procession
Domain – Information Extraction

Natural Language ProcessionTraining a machine learning model
that classifies a given line of text as belonging to one of the books/Articles.
developing a machine learning model(deep learning preferred) for the same.

Project -Airbnb New User Bookings
Domain – Travel & Hospitality

DataSet : Airbnb

The goal is to predict which country a new user’s first booking destination
will be.By accurately predicting where a new user will book their first travel
experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand.

Project -Speech Emotion Detection Model
Domain – Voice Recognition

Analyse audio samples .Building a CNN Model for Emotion Detection.Training and Testing the Model and Use Trained CNN Model on New Audio Samples

Project -Walmart Sales Forecasting
Domain – Retail

DataSet : Walmart

This dataset contains the sales for each department from the Walmart
dataset containing data of 45 Walmart stores, selected holiday markdown events are also included These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the
impact.

Project -Detecting Smiles in your Camera App using CNN
Domain – Sentiment Analysis

This Project will detect whether an Image contains a Smile with High Accuracy. The goal is to extract high-level features by a well-designed deep convolutional networks (CNN)

Project -Bosch Production Line Performance
Domain – Manufacturing
DataSet : Bosch

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. The goal is to predict which parts will fail quality control

Project -Generating Chatbot
Domain – Machine Learning

In this project we will build a simple retrieval based chatbot based on NLTK library in python, to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment
analysis, speech recognition, and topic segmentation.

Project -Forecasting Uber Demand
Domain – Demand/Supply
DataSet :Uber & Rapido

The goal is to create an interactive dashboard using Tableau This Tableau Dashboard can be used to get historical insights into a neighborhood,
For example,see its upcoming forecasted demand, increase the accuracy,decrease surge pricing events.

Project -Predicting Stock Prices Using LSTM
Domain – Predictive Analytics

Trying to determine the future value of company stock or other financial
instruments traded on an exchange.Predict the Closing Stock Price of a given Company. Build and train LSTM model for Stock Price Prediction

Project -Smart Supply Chain for Big Data
Analysis

Domain – Supply Chain

DataSet : Dataco

A DataSet of Supply Chains used by the company DataCo Global is used
for the analysis. Dataset of Supply Chain , which allows the use of
Machine Learning Algorithms and R Software.It also allows the correlation of
Structured Data with Unstructured Data for knowledge generation.

260+

Content Hours Available

15+

Industry Projects

20+

Live Learning Sessions

25

Expert Coaching Sessions

14

Programming Tools/ Languages
MODULES
Course Modules

Python for Data Science

Statistics Mathematics

Machine Learning

Deep Learning

NLP / Text Analytics

Computer Vision

Project Management

R programming

Domain Training

Google Cloud

mySQL

Mongo DB

Power BI

Tableau

Apache Spark

Reinforcement Learning

Real Time Projects

AI/ML Project Management

MODULES
Course Modules

Python for Data Science

Statistics Mathematics

Machine Learning

Deep Learning

NLP / Text Analytics

Computer Vision

Project Management

R Programming

Domain Training

Google Cloud

mySQL

Mongo DB

Power BI

Tableau

Apache Spark

Reinforcement Learning

Real Time Projects

AI/ML Project Management

Program Schedule
Advanced AI and ML Certification for Professionals

Module 0: Special Sessions For Non-Programmers

Time duration: 4 days (10 hours)
One Week before your Batch Starts

We believe that professionals coming from non-programming backgrounds need special assistance and support. we provide special programming fundamentals classes designed for non programmers and non technical domain professionals which helps you to learn programming concepts easily.

Term 1: Python For Data Science

Time duration: 5 Weeks (40 hours)
1.5 Months

Python is one of the most commonly used programming languages in the field, in this section, we start Basic python & Environment Setup and advanced Python scripts such as List, Tuples, Dictionaries, File Operations, Regular Expressions, dealing with binary data, and using the extensive Python module features. Python libraries and tools,Numpy ,Pandas,Matplotlib and Seaborn etc

Term 2: Statistics And Machine Learning

Time duration: 9 Weeks (72 hours)
2 Months

Statistics are the use of mathematics to conduct technical predictive analytics, IN statistics covering math and probability fundamentals, hypothesis testing, data processing, and exploratory data analysis, etc. Machine learning curriculum that will provide you to learn techniques such as supervised learning, unsupervised learning, and natural language processing, etc.

Term 3: TensorFlow, Deep Learning, NLP& Computer Vision And Opencv

Time duration: 12 Weeks ( 96 hours)
3 Months

TensorFlow is a platform built by Google. Deep Learning is a machine learning category. TensorFlow and deep learning cover topics such as Neural Network Model, Graph Visualization, Logistic Regression, CNN Architecture, Deep CNN Pooling Layer Application, etc.

Term 4: Cloud Deployment of ML Model using GCP & Reinforcement Learning

Time duration:4 Weeks ( 32 hours)
1 Months
GCP Offered by Google , is a suite of cloud computing services that runs on the same infrastructure that google uses internally for its end-user products. GCP consists of a set of physical assets like-computers, hard disk drives and Virtual Machines. Reinforcement Learning is the branch of ML that permits systems to learn from the outcomes of their own decision.

Projects And Job Assistance

Integrated with the curriculum
When: After every module

Candidates Effective completion of Term 1, Term 2, and Term 3, become eligible for the Career Assistance Program, In this programmer, we have offered Interview Prep Session & Mock Interview, Participating in Live Kaggle Competitions. Guaranteed Career References for Data Science/ML positions of an engineer.
Take part in guided workshops from various domains for real-time projects and get project support/mentorship from expert instructors.

SAMPLE CERTIFICATE

Global Accreditation and Certification

In Collaboration With IBM

Our Data Science & AI Certification Program helps you to start your career in data science and AI.

Enhancing your profile

Add this globally recognized certificate to uplift your profile and stand out of the crowd.

Real Time Project & Job Assurance

Course features 12+ Real time industry projects which can be added as a learning experience in your profile with 100% Interview Guarantee.
SAMPLE CERTIFICATE

Global Accreditation and Certification

In Collaboration With IBM

Our Data Science & AI Certification Program helps you to start your career in data science and AI.

Enhancing your profile

Add this globally recognized certificate to uplift your profile and stand out of the crowd.

Real Time Project & Job Assurance

Course features 12+ Real time industry projects which can be added as a learning experience in your profile with 100% Interview Guarantee.
TRANSITION STEPS
Learning Track

Step 1

Learn Full Stack DS & AI
Learn basics Python, Statistics, ML, NLP , DL, SQL and other modules in depth with real time case studies.

Step 2

Choose domain & get trained
You can select multiple domains and get trained based on your work experience, academics and career goal.

Step 3

Work on real time projects
Work on multiple real time industry projects and capstone from multiple domains with mentorship of our expert.

Step 4

Resume Prep & Mock Interview Session
Update your resume and practice mock interviews. Get 100% guaranteed job referrals for data scientist & ML Engineer.
DATA SCIENCE
Real Time Projects
DATA SCIENCE
Real Time Projects
FAQs
Frequently Asked Questions

What are the Prerequisites for data science certification Course?

There are no prerequisites for this course as all the modules are covered from basics and scratch. If you are from a non-technical and non-programming background, We provide special classes and assistance so that you can learn to program.

What if i miss few classes?What is flexi pass Option ?

In case you miss classes, You will be provided backup classes in other batches. If you miss more classes, You can request for batch change and join our next batch. To solve this issue, We provide a flexi-pass option for our data science course.

Those who enroll for live classroom training are eligible for a Flexi-pass of 6 months. With this option, We share your access to all ongoing batch details for a period of 6 months, so that you can attend live classroom sessions from any batches and learn at your own pace. This option also helps you to finish the course early as you can attend multiple batches simultaneously.
This option is best for someone working in shifts or needs to work on weekends sometimes.

What is the duration of this Course?

Duration Of this course is around 6 months, weekends only (4 hours on Sat and Sunday)
Course Duration: 200 hours (Approx)

The total duration of this course is 6 months but after 4 Months( once50% Of Machine learning is over) you can start working on Real-Time Projects and attend job interviews.

What is the mode of training?

We provide both options i.e Classroom training( in Marathahalli – Bangalore ) and instructor-led live training option. Based on your requirement and need, you can choose training mode.

How long will i get support for Job Assistance?

For 1 Year (After your course completion)

What is Job Readiness Program?

Job Readiness program helps you to prepare for data science interviews.

Resume Preparation Session: Expert guidance for writing a resume for data scientist Role

Preparing Project For interviews: Will help you to prepare and write a project description in your resume

Interview Guidance And Prep Session: 6 hours of interview readiness session to help you to prepare for interviews

One on One Mock Interviews

What is the eligibility for job assistance program?

You should have completed the course with

    • A minimum score of 70% in Assessment
    • Should have completed Assignments and case studies
    • Worked and successfully completed a min. the recommended number of real-time projects

Will i get referral for jobs?

Yes,We provide placement Assistance by referring your profile through our partnered consultancies and companies for relevant positions(Data Science/Data Analyst Roles).

Contact course manager/Counsellor to know the eligibility of job assistance program

Note: We don’t provide any 100% Job Guarantee

Can i Pay in installment?

There ai 0% EMI Available on Major credit cards.Please check with our sales team to know how 0% EMI on credit cards works.

We have promotions and early bird offer from time to time. Please check on the website or talk to our sales team to know more about the applicable discounts.

Group Discount: Group discount is applicable if you are joining with your friends.

  • 5% extra discount: Group of 2
  • 8% extra discount: Group of 3
  • Max 10% group discount: Group of 4+ members

How many minimum number of Projects should i work?

Number of Projects depends on your overall industry experience.Please find the recommended number of projects With industry experience.
2 yrs to 5 Yrs : 3 Projects
5 to 8 yrs: 5 Projects
8 yrs to 12 yrs: 7 + Projects
For your knowledge and practice, You can work on all the projects with us.
Contact our course expert to know more details
Note: Projects are guided by our expert industry mentors.

How many Real Time Projects will be covered in this Course?

There is a set of  12 Real-time projects from multiple domains like Banking, Finance, Insurance, Retail, Healthcare, and manufacturing. Based on your interest and domain, You can choose the project to work on.

Click here to download the list of project

How Projects Will be executed?

Your Project group will be assigned a project mentor and will be given the chance to work on the project With guidance and Assistance.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

Whether you want to develop as a professional or discover a new hobby, there’s an online course for that.

What if I did not receive a password reset message?

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The course has been designed specifically for working professionals with 2 to 8 years of experience who choose to advance their careers in Data Science and AI, and it comes with a 100% job guarantee or money back policy.

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Get The Learnbay Advantage For Your Career
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GET THE LEARNBAY ADVANTAGE FOR YOUR CAREER
Note : Our programs are suitable for working professionals(any domain). Fresh graduates are not eligible.