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Main Syllabus
Learning Objectives: Business Intelligence Vs Data AnalysisVs Data Scientist
Data Scientist Roles
Different disciplines of data science:
 Machine Learning
 Natural Language Processing
 Deep Learning
Applications of Machine Learning
Why Machine Learning is the Future
 Creating the “Hello World” code
 Demonstrating Conditional Statements
 Variables
 Demonstrating Loops
What are prerequisites for Data Science
 Statistics
 Python essentials for Data Science
 Descriptive statistics and Inferential Statistics
 Sample and Population
 Variables and Data types
 Percentiles
 Measures of Central Tendency
 Measures of Spread
 Skewness, Kurtosis
 Degrees of freedom
 Variance, Covariance, Correlation
 Descriptive statistics and Inferential Statistics in Python
 Test of Hypothesis
 Confidence Interval
 Sampling Distribution
 Standard Probability Distribution Functions
 Bernoulli, Binomialdistributions
 Normal distributions
 Data Transformations
 Outlier Detection and Management
 Charts and Graphs
 One Dimensional Chart
 Box plots
 Bar graph
 Histogram
 Scatter plots
 MultiDimensional Charts
Learning Objectives: You will get a brief idea of what Python is and touch on the basics.
Topics:
 Overview of Python
 Diﬀerent Applications where Python is used
 Values, Types, Variables
 Conditional Statements
 Command Line Arguments
 The Companies using Python
 Discuss Python Scripts on UNIX/Windows
 Operands and Expressions
 Loops
 Writing to the screen
HandsOn/Demo:
 Creating the “Hello World” code
 Demonstrating Conditional Statements
 Variables
 Demonstrating Loops
Skills:
 Fundamentals of Python programming
Learning Objectives: In this module, you will learn how to create generic Python scripts, how to address errors/exceptions in code and ﬁnally how to extract/ﬁlter content using regex.
Topics:
 Functions
 Global Variables
 Lambda Functions
 Standard Libraries
 The Import Statements
 Package Installation Ways
 Handling Multiple Exceptions
 Function Parameters
 Variable Scope and Returning Values
 ObjectOriented Concepts
 Modules Used in Python
 Module Search Path
 Errors and Exception Handling
HandsOn/Demo:
 Functions – Syntax, Arguments, Keyword Arguments, Return Values
 Sorting – Sequences, Dictionaries, Limitations of Sorting
 Packages and Module – Modules, Import Options, Sys Path
 Lambda – Features, Syntax, Options, Compared with the Functions
 Errors and Exceptions – Types of Issues, Remediation
Skills:
 Error and Exception management in Python
 Working with functions in Python
Learning Objectives: Through this module, you will understand indetail about Data Manipulation
Topics:
 Basic Functionalities of a data object
 Concatenation of data objects
 Exploring a Dataset
 Merging of Data objects
 Types of Joins on data objects
 Analysing a dataset
HandsOn/Demo:
 Pandas Function Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
 Aggregation
 Merging
 GroupBy operations
 Concatenation
 Joining
Skills:
 Python in Data Manipulation
Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.
Topics:
 Python Revision (numpy, Pandas, scikit learn, matplotlib)
 Machine Learning UseCases
 Machine Learning Categories
 Gradient descent
 What is Machine Learning?
 Machine Learning Process Flow
 Linear regression
HandsOn/Demo:
 Linear Regression – Boston Dataset
Skills:
 Machine Learning concepts
 Linear Regression Implementation
 Machine Learning types
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classiﬁer, etc.
Topics:
 What are Classiﬁcation and its use cases?
 Algorithm for Decision Tree Induction
 Confusion Matrix
 What is Decision Tree?
 Creating a Perfect Decision Tree
 What is a Random Forest?
HandsOn/Demo:
 Implementation of Logistic regression
 Random forest
 Decision tree
Skills:
 Supervised Learning concepts
 Evaluating model output
 Implementing diﬀerent types of Supervised Learning algorithms
Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing the LDA model.
Topics:
 Introduction to Dimensionality
 PCA
 Scaling dimensional model
 Why Dimensionality Reduction
 Factor Analysis
 LDA
HandsOn/Demo:
 PCA
 Scaling
Skills:
 Implementing Dimensionality Reduction Technique
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classiﬁer, etc.
Topics:
 What is Naïve Bayes?
 Implementing Naïve Bayes Classiﬁer
 Illustrate how Support Vector Machine works?
 Grid Search vs Random Search
 How Naïve Bayes works?
 What is Support Vector Machine?
 Hyperparameter Optimization
 Implementation of Support Vector Machine for Classiﬁcation
HandsOn/Demo:
 Implementation of Naïve Bayes, SVM
Skills:
 Supervised Learning concepts
 Evaluating model output
 Implementing diﬀerent types of Supervised Learning algorithms
Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
Topics:
 What is Clustering & its Use Cases?
 How does the Kmeans algorithm work?
 What is Cmeans Clustering?
 How Hierarchical Clustering works?
 What is Kmeans Clustering?
 How to do optimal clustering
 What is Hierarchical Clustering?
HandsOn/Demo:
 Implementing Kmeans Clustering
 Implementing Hierarchical Clustering
Skills:
 Unsupervised Learning
 Implementation of Clustering – various types
Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with the Apriori algorithm.
Topics:
 What are Association Rules?
 Calculating Association Rule Parameters
 How does Recommendation Engines work?
 ContentBased Filtering
 Association Rule Parameters
 Recommendation Engines
 Collaborative Filtering
HandsOn/Demo:
 Apriori Algorithm
 Market Basket Analysis
Skills:
 Data Mining using Python
 Recommender Systems using Python
Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyse the data.
Topics:
 What is Reinforcement Learning
 Elements of Reinforcement Learning
 Epsilon Greedy Algorithm
 Q values and V values
 α values
 Why Reinforcement Learning
 Exploration vs Exploitation dilemma
 Markov Decision Process (MDP)
 Q – Learning
HandsOn/Demo:
 Calculating Reward
 Calculating Optimal quantities
 Setting up an Optimal Action
 Discounted Reward
 Implementing Q Learning
Skills:
 Implement Reinforcement Learning using Python
 Developing Q Learning model in Python
Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught diﬀerent models for time series modeling such that you analyse a real timedependent data for forecasting.
Topics:
 What is Time Series Analysis?
 Components of TSA
 AR model
 ARMA model
 Stationarity
 Importance of TSA
 White Noise
 MA model
 ARIMA model
 ACF & PACF
Handson/demo:
 Checking Stationarity
 Implementing the DickeyFuller Test
 Generating the ARIMA plot
 Converting a nonstationary data to stationary
 Plot ACF and PACF
 TSA Forecasting
Skills:
 TSA in Python
Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
Topics:
 What is the Model Selection?
 CrossValidation
 How Boosting Algorithms work?
 Adaptive Boosting
 The need for Model Selection
 What is Boosting?
 Types of Boosting Algorithms
HandsOn/Demo:
 CrossValidation
 AdaBoost
Skills:
 Model Selection
 Boosting algorithm using Python
Learning Objectives: Learn diﬀerent types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to ﬁles.
Topics:
 Python ﬁles I/O Functions
 Strings and related operations
 Lists and related operations
 Sets and related operations
 Numbers
 Tuples and related operations
 Dictionaries and related operations
HandsOn/Demo:
 Tuple – properties, related operations, compared with a list
 Dictionary – properties, related operations
 List – properties, related operations
 Set – properties, related operations
Skills:
 File Operations using Python
 Working with data types of Python
Learning Objectives: This module helps you get familiar with the basics of statistics, diﬀerent types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualisation.
Topics:
 NumPy – arrays
 Indexing slicing and iterating
 Pandas – data structures & index operations
 matplotlib library
 Markers, colours, fonts and styling
 Contour plots
 Operations on arrays
 Reading and writing arrays on ﬁles
 Reading and Writing data from Excel/CSV formats into Pandas
 Grids, axes, plots
 Types of plots – bar graphs, pie charts, histograms
HandsOn/Demo:
 NumPy library Creating NumPy array, operations performed on NumPy array
 Matplotlib – Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot
 Pandas library Creating series and data frames, Importing and exporting data
Skills:
 Probability Distributions in Python
 Python for Data Visualisation
This course comprises of 40 case studies that will enrich your learning experience. In addition, we also have 4 Projects that will enhance your implementation skills. Below are a few case studies, which are part of this course:
 Case Study 1: Maple Leaves Ltd is a startup company that makes herbs from diﬀerent types of plants and leaves. Currently, the system they use to classify the trees which they import in a batch is quite manual. A laborer from his experience decides the leaf type and subtype of the plant family. They have asked us to automate this process and remove any manual intervention from this process.
 Case Study 2: BookRent is the largest online and oﬄine book rental chain in India. The company charges a ﬁxed fee per month plus rental per book. So, the company makes more money when the user rents more books. You as an ML expert and must model a recommendation engine so that the user gets a recommendation of books based on the behavior of similar users. This will ensure that users are renting books based on their individual tastes. The company is still unproﬁtable and is looking to improve both revenue and proﬁt. Compare the Error using two approaches – UserBased Vs Item BasedYou have to classify the plant leaves by various classiﬁers from diﬀerent metrics of the leaves and to choose the best classiﬁer for future reference.
 Case Study 3: Handle missing values and ﬁt a decision tree and compare its accuracy with a random forest classiﬁer. Predict the survival of a horse based on various observed medical conditions. Load the data from ‘horses.csv’ and observe whether it contains missing values. Replace the missing values by the most frequent value in each column. Fit a decision tree classiﬁer and observe the accuracy. Fit a random forest classiﬁer and observe the accuracy.
 Case Study 4: Principal component analysis using scikit learn. Load the digits dataset from sklearn and write a helper function to plot the image. Fit a logistic regression model and observe the accuracy. Using scikit learn to perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Compare it with a model and also comment on the accuracy. Compute the confusion matrix and count the number of instances that have gone wrong. For each of the wrong sample, plot the digit along with the predicted and original label.
 Case Study 5: Handling GIS data and working with maps. Creating, cleaning, collating and visualizing maps of India at different levels – state, district, taluka, and villages. Using Geopandas, Mapviz, and leaflet in Python to perform spatial analytics and visualizing statistics with geographical context. Using public data of government expenditure, identify the areas and districts with the highest expenditure per capita in different states and all over India.
Project #1: Industry: Social Media
 Problem Statement: You as ML expert have to do analysis and modeling to predict the number of shares of an article given the input parameters.
 Actions to be performed: Load the corresponding dataset. Perform data wrangling, visualisation of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your Learning Objectives. Also, use scaling processes, PCA along with boosting techniques to optimise your model to the fullest.
 Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years.
 Actions to be performed: You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the ﬁnal solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across the years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components that explain the max variance.
WHAT YOU WILL LEARN
 Understand and implement word2vec
 Understand the CBOW method in word2vec
 Understand the skipgram method in word2vec
 Understand the negative sampling optimisation in word2vec
 Understand and implement GloVe using gradient descent and alternating least squares
 Use recurrent neural networks for partsofspeech tagging
 Use recurrent neural networks for named entity recognition
 Understand and implement recursive neural networks for sentiment analysis
 Understand and implement recursive neural tensor networks for sentiment analysis
WHAT YOU WILL LEARN
 Build natural language processing systems using TensorFlow
 Process text, including tokenisation and representing sentences as vectors
 Apply RNNs, GRUs, and LSTMs in TensorFlow
 Train LSTMs on existing text to create original poetry and more
Getting Started
Knowing each other
Welcome to the Course
About the Course
Introduction to Natural Language Processing
Exercise: Introduction to Natural Language Processing
Podcast with NLP Researcher Sebastian Ruder
Installation steps for Linux
Installation steps for Mac
Installation steps for Windows
Packages Installation
Introduction to Python
Variables and Operators
Exercise: Variables and Operators
Python Lists
Exercise: Python Lists
Dictionaries
 Exercise: Dictionaries
 Conditional Statements
 Exercise: Conditional Statements
 Loops
 Exercise: Loops
 Functions
 Python Functions Practice
 Exercise: Functions
 Packages
 Exercise: Packages
 Files
 Exercise: Files
 Welcome to Module
 Understanding Regular Expression
 Implementing Regular Expression in Python
 Exercise: Implementing Regular Expression in Python
 Regular Expressions in Action
 Welcome to Module
 Tokenization and Text Normalisation
 Exercise: Tokenisation and Text Normalisation
 Exploring Text Data
 Part of Speech Tagging and Grammar Parsing
 Exercise: Part of Speech Tagging and Grammar Parsing
 Implementing Text Preprocessing Using NLTK
 Exercise: Implementing Text Preprocessing Using NLTK
 Natural Language Processing Techniques using spaCy
 Welcome to Module
 Understanding Named Entity Recognition
 Exercise: Understanding Named Entity Recognition
 Implementing Named Entity Recognition
 Exercise: Implementing Named Entity Recognition
 Named Entity Recognition and POS tagging using spaCy
 POS and NER in Action: Text Data Augmentation
 Assignment: Share your learning and build your profile
Introduction to Text Feature Engineering
Count Vector, TFIDF Representations of Text
Exercise: Introduction to Text Feature Engineering
Understanding Vector Representation of Text
Exercise: Understanding Vector Representation of Text
Understanding Word Embeddings
Word Embeddings in Action – Word2Vec
Word Embeddings in Action – GloVe
Introduction to Text Cleaning Techniques Part 1
Exercise: Introduction to Text Cleaning Techniques Part 1
Introduction to Text Cleaning Techniques Part 2
Exercise: Introduction to Text Cleaning Techniques Part 2
Text Cleaning Implementation
Exercise: Text Cleaning Implementation
NLP Techniques using spaCy
Project I – Social Media Information Extraction
 Introduction to Topic Modelling
 Exercise: Introduction to Topic Modelling
 Understanding LDA
 Exercise: Understanding LDA
 Implementation of Topic Modelling
 Exercise: Implementation of Topic Modelling
 LSA for Topic Modelling
 Understanding the Problem Statement
 Importing Dataset
 Text Cleaning and Preprocessing
 Categorising Articles using Topic Modelling
 Types of Machine Learning Algorithms
 Logistic Regression
 Decision Tree
 Naive Bayes
 SVM (Support Vector Machine)
 Random Forest
 Overview of Text Classification
 Exercise: Overview of Text Classification
 Assignment: Share your learning and build your profile
 Getting started with Neural Network
 Exercise: Getting started with Neural Network
 Understanding Forward Propagation
 Exercise: Forward Propagation
 Math Behind forwarding Propagation
 Exercise: Math Behind forwarding Propagation
 Error and Reason for Error
 Exercise: Error and Reason for Error
 Gradient Descent Intuition
 Understanding Math Behind Gradient Descent
 Exercise: Gradient Descent
 Optimiser
 Exercise: Optimiser
 Back Propagation
 Exercise: Back Propagation
 Why Keras?
 Exercise: Why Keras?
 Building a Neural Network for Text Classification
 Why CNN?
 Exercise: Why CNN?
 Understanding the working of CNN Filters
 Exercise: Understanding the working of CNN Filters
 Introduction to Padding
 Exercise: Introduction to Padding
 Padding Strategies
 Exercise: Padding Strategies
 Padding Strategies in Keras
 Exercise: Padding Strategies in Keras
 Introduction to Pooling
 Exercise: Introduction to Pooling
 CNN architecture and it’s working
 Exercise: CNN architecture and it’s working
 Deep Learning for NLP Part 1
 Exercise: Deep Learning for NLP Part 1
 Deep Learning for NLP Part 2
 Exercise: Deep Learning for NLP Part 2
 Text Generation Using LSTM
 Exercise : Text Generation Using LSTM
 Dataset download
 Text Cleaning
 Feature Engineering
 Advanced Feature Engineering
 Combining Features
 ML Classifier
 Spam Classification using Deep Learning
 Project III
 Overview of AutoTagging System
 Introduction to Dataset and Performance Metrics
 AutoTagging Implementation Using Machine Learning Part1
 AutoTagging Implementation Using Machine Learning Part2
 AutoTagging Implementation Using Deep Learning
 Why RNN
 Introduction to RNN: Shortcomings of an MLP
 Introduction to RNN: RNN Architecture
 Training an RNN: Forward propagation
 Training an RNN: Backpropagation through time
 Need for LSTM/GRU
 Long Short Term Memory (LSTM)
 Gated Recurrent Unit (GRU)
 Project: Categorisation of websites using LSTM and GRU I
 Dataset and Notebook
 Project: Categorisation of websites using LSTM and GRU II
 Overview: Language Modelling
 What is a Language Model in NLP?
 Ngram Language Model
 Implementing an Ngram Language Model – I
 Implementing an Ngram Language Model – II
 Neural Language Model
 Implementing a Neural Language Model
 Intuition Behind SequencetoSequence Modelling
 Need for SequencetoSequence Modelling
 Understanding the Architecture of SequencetoSequence
 Understanding the Functioning of Encoder and Decoder
 Case Study: Building a Spanish to English Machine Translation Model
 Preprocessing of Text Data
 Converting Text to Integer Sequences
 Model Building and Inference
 Introduction
 Preprocessing and Feature Creation
 Model Building and Summary Genera
 Introduction
 About this module
 Overview of Conversational Agents
 Project – Foodbot
 Overview of Rasa Framework
 System Setup
 Rasa NLU: Understanding user intent from a message
 Rasa NLU: Extracting intents from a user’s message
 Rasa Core: Making your chatbot conversational
 Working with Zomato API
 Create a Workspace in Slack
 Deploying to Slack
 Assignment: Share your learning and build your profile
 Getting started with Bonus Section
 Text Classification & Word Representations using FastText (An NLP library by Facebook)
 Introduction to Flair for NLP: A Simple yet Powerful StateoftheArt NLP Library
 Introduction to Stanford NLP: An Incredible StateoftheArt NLP Library for 53 Languages (with Python code)
 A StepbyStep NLP Guide to Learn Elmo for Extracting Features from Text
 Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
 8 Excellent Pretrained Models to get you started with Natural Language Processing (NLP)
 Geocoding using NLP by Shantanu Bhattacharyya and Farhat Habib
 Demystifying the What, the Why and How of Chatbot by Sonny Laskar
 Sentiment Analysis using NLP and Deep Learning by Jeeban Swain
 Identifying Location using Clustering and Language Model – By Divya Choudhary
 Building Intelligent Chatbots from Scratch
This is another interesting machine learning project idea for data scientists/machine learning engineers working or planning to work with the finance domain. Stock prices predictor is a system that learns about the performance of a company and predicts future stock prices. The challenges associated with working with stock price data are that it is very granular, and moreover there are different types of data like volatility indices, prices, global macroeconomic indicators, fundamental indicators, and more. One good thing about working with stock market data is that the financial markets have shorter feedback cycles making it easier for data experts to validate their predictions on new data. To begin working with stock market data, you can pick up a simple machine learning problem like predicting 6month price movements based on fundamental indicators from an organisations’ quarterly report. You can download Stock Market datasets from Quandl.com or Quantopian.com.
The smartphone dataset consists of fitness activity recordings of 30 people captured through smartphoneenabled with inertial sensors. The goal of this machine learning project is to build a classification model that can precisely identify human fitness activities. Working on this machine learning project will help you understand how to solve multiclassification problems. One can become a master of machine learning only with lots of practice and experimentation. Having theoretical surely helps but it’s the application that helps progress the most. No amount of theoretical knowledge can replace handson practice. There are many other machine learning projects for beginners like the ones mentioned above that you can work with. However, it will help if you familiarise yourself with the abovelisted projects first. If you are a beginner and new to machine learning then working on machine learning projects designed by industry experts at DeZyre will make some of the best investments of your time. These machine learning projects have been designed for beginners to help them enhance their applied machine learning skills quickly whilst giving them a chance to explore interesting business use cases across various domains – Retail, Finance, Insurance, Manufacturing, and more. So, if you want to enjoy learning machine learning, stay motivated, and make quick progress then DeZyre’s machine learning interesting projects are for you. Plus, add these machine learning projects to your portfolio and land a top gig with a higher salary and rewarding perks.
From Netflix to Hulu, the need to build an efficient movie recommender system has gained importance over time with increasing demand from modern consumers for customised content. One of the most popular datasets available on the web for beginners to learn how to build recommender systems is the Movielens Dataset which contains approximately 1,000,209 movie ratings of 3,900 movies made by 6,040 Movielens users. You can get started working with this dataset by building a worldcloud visualisation of movie titles to build a movie recommender system.
This course comprises of 40 case studies that will enrich your learning experience. In addition, we also have 4 Projects that will enhance your implementation skills. Below are a few case studies, which are part of this course:
Case Study 1: Maple Leaves Ltd is a startup company that makes herbs from diﬀerent types of plants and leaves. Currently, the system they use to classify the trees which they import in a batch is quite manual. A laborer from his experience decides the leaf type and subtype of the plant family. They have asked us to automate this process and remove any manual intervention from this process.
Build a system that can have a conversation with you. The user types messages and your system replies based on the user’s text. Many approaches here … you could use a large twitter corpus and do language similarity.
 Flask basics
 Deployment of the model on Heroku
 AWS basics
 S3
 EC2
 AWS Lambda
 Deployment of the model on EC2
 Deployment on AWS Lambda (Optional)
 Google Cloud Platform Basics
 Deployment of the Model on GCP
 Microsoft Azure basics
 Deployment of the Model on Azure
 Pyspark Basics
 DeVops Concepts
 Data Visualization with Tableau & Power BI
 SQL
 MSOFFICE
 Introduction to Operational , HR, Finance, Marketing Analytics
 Leading Data Science Teams & Processes
 Exploring Methodologies
 How to manage data science projects and lead a data science team
 Agile Data Science
 Scrum Data Science
 Emerging Approaches – Microsoft TDSP
 Data Science Methodology understanding
 Business & Data understanding
 Modelling & Evaluation
 Plan Deployment
 Data Science Project Report
 Introduction to Bigdata & HDFS along with Linux concepts , its importance in data science
 Core Components of Hadoop
 HDFS Architecture
 HDFS Commands
 Apache Scoop Fundamentals & basics
 Apache Spark fundamentals & advanced concepts and its importance in data science
 Introduction to Kafka
 Bigdata on Cloud and its importance in Data Science
Addon Syllabus
 Bigdata, Sparck & Kafka, MangoDB
 Business Analytics
 Data WareHousing & ETL
 Data Analytics
 Cloud Coumpting (AWS, AZURE, CGP)
 Deployment of Machine Learning Models to Cloud
 Data Science Project Management
Program Highlights

24 Months Program Duration

6 Months of live Interactive Classes by Principal or Senior Data Scientists across Industry

18 Months of Sponsored Project Work at Authorized Research Centre funded by IISC, ISB, IIM

Gain 1+ years of Real Time Sponsored Project Experience Certificate from Recognized Research Centres

100% Job Assistance Program In collaboration with Top HR Consulting Firms across PAN INDIA
Tools Covered
Data Science Architect Program & Google Certification:
Companies Associated with GEEKLURN
offers you with immaculate growth and up scaled expansion.