Data Science Architect Program
Data Science Architect Program
We assure you 100% money return in case of being not placed successfully After course completion.
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Gain 1.5 years of Research Project Experience Certificate
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100% Job Guaranteed
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320+ hours Live Interactive Sessions with Eminent Data Scientists
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Scholarships from day one up to Rs 2 lakhs
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Become an IBM Certified Data Science Architect Professional
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Remote or Hybrid International internship opportunities in USA, UK, Europe & APAC for Merit Students with 2 years of Project Experience Certificate
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Job Guarantee* Program in collaboration with Top PAN India Consulting Firms.
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Hackathons (Cash rewards upto Rs.1 Lakh will be awarded to top performers)
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320+ hours Live Interactive Sessions with Eminent Data Scientists
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Opportunity to conduct research work with GEEKLURN AI Singapore
Geeklurn is now a Member of NASSCOM

GeekLurn is now a Member of NASSCOM & IBM Partner


We assure you 100% money return in case of being not placed successfully After course completion.
-
Gain 1.5 years of Research Project Experience Certificate
-
100% Job Guarantee*
-
320+ hours Live Interactive Sessions with Eminent Data Scientists
-
Scholarships from day one up to Rs 2 lakhs
Geeklurn is now a Member of NASSCOM

GeekLurn is now a Member of NASSCOM & IBM Partner



2200+
Alumni Students

Online
Format

24 Months
Program Duration

110+
Hiring Partners

EMI Options
No Cost EMI
GeekLurn is now a Member of NASSCOM & IBM Partner




Program Overview
Key Highlights
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24-Months Course Duration
- 6 Months of live interactive classes with Principal/Senior industry Data Scientists
- 18 Months of Sponsored Project Work at Authorised Research Centres funded by IISC, ISB, IIM
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320+ hours Live Interactive Sessions with Eminent Data Scientists.
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Gain 1.5 years of real-time sponsored Project Expierence Certificate from recognised research centres
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Start paying your EMI only after placements at the end of the course
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100% Job Guarantee Program in collaboration with Top HR Consulting Firms across India
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Scholarships from day ONE up to Rs 2 lakhs based on the type pf Research Projects
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Get the opportunity to conduct research work with Singapore based GEEKLURN AI
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50+ Sponsored & Funded Research Projects
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Get Certified from Top Companies (IBM, Accenture, Microsoft, Oracle...)
Data Science Job Guarantee Overview

Job Guarantee*
Secure exciting Data Science jobs in top companies through our network of over 500 hiring partners and HR companies.

4 million+ Data Jobs
Data Science professionals earn an average salary of ₹16 lakhs, with professionals getting a minimum 30% salary hike compared to the previous jobs.

1-on-1 Career Mentorship
Enhance your resume, prepare for technical interviews, and gain career growth hacks with valuable insights from industry experts and principal data scientists.

Easy Financing Options
GeekLurn offers an easy financing option at 0% interest rate with no hidden costs.
Placement Stats

30% Average Salary Hike

16 LPA Average Salary

8000 + Jobs Sourced

500+ Hiring Partners
Our Authorised Hiring Partners


































Our Alumni

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Data Scientist

Student Reviews
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Alumni Success Meet at GeekLurn HQ








Core Stages of Our DSAP Program
Stage 1
A conducive learning environment with 6 months of live interactive real-time training sessions with 1:1 mentorship offered by top industry experts who are working as Principal & Senior Data Scientists
Stage 2
After hands-on applied training, start working as a Data Science Intern on real-time industry assignments at an authorized GeekLurn incubation zone, which is recognised as a trusted research centre and is funded by IISC, IIM and ISB.
Stage 3
Get an opportunity to work on real- time industry-led projects officially funded by IISC, ISB and IIM. By displaying efficiency, calibre and good performance, you can be eligible for a scholarship of up to Rs. 2 lakh
Stage 4
The successful completion of the project will fetch you an Experience Certificate of 1+years issued by the respective research centre and will to help you add credible projects to your resume, which will prove to be advantageous during placements.
Stage 5
Along with the Experience Certificate, you can fine-tune your resume by getting certified as a Data Science Specialist accredited by top brands in the industry like Microsoft, IBM, HarvardX, DASCA, and SAS.
Stage 6
With 100% Job Guarantee & career support from industry experts in resume building and mock interviews practices, you will be eligible to get hired by more than 110+ Data Science Companies across India and earn up to Rs. 25 lacs/annum
Stage 7
From the 7th months after the completion of the live training session, you can start attending interviews while simultaneously working on research projects.
Data Science Architect Program Overview
GEEKLURN’s Data Science Architect Program is designed by industry experts and practitioners. It will provide you with tech-enabled job-relevant skills through the design, development and deployment of Big Data to convert it into real-time applications. The course is intended for both freshers and working professionals and will aim to positively and significantly impact their careers. Work on real-time case studies, & gain project mentorships & certifications through our 100% Job Guarantee program that, will make you job-ready and more relevant in the industry for various data science job roles.
- Students from NON-IT background like B.Com & MBA who wants to build a career in Business Analytics / Data Analytics / Data Science Project Management.
- Any Engineering / BCA / MCA graduate who wants to build a career in Data Science.
- IT and Computer Science Graduates who want to build a career in Data Science.
- IT Professionals who wish to make a career shift to Data Sciences.
- Data Science Professionals who wish to Upskill and become Data Architects.
- On a daily basis, over 5 billion consumers interact with data and thatnumber will increase to 6 billion by 2025, representingthree-quarters of the world’s population.
- The Global Big Data Market is expected to reach $122 billion in revenue by2025 – Frost & Sullivan
- Research shows 94% of data science graduateshave gotten jobs in this field since 2011.
After completion of this course, the Learner will be eligible for any of the following Job Roles:
Tech Talks by Eminent Industry Experts
Advisors









Advisors




Learning Objectives: Business Intelligence vs Data Analysis vs 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, Binomial-Distributions
- Normal Distributions
- Data Transformations
- Outlier Detection and Management
- Charts and Graphs
- One Dimensional Chart
- Box plots
- Bar graph
- Histogram
- Scatter plots
- Multi-Dimensional 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
Hands-On/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
- Object-Oriented Concepts
- Modules Used in Python
- Module Search Path
- Errors and Exception Handling
Hands-On/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 in-detail 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
Hands-On/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 Use-Cases
- Machine Learning Categories
- Gradient Descent
- What is Machine Learning?
- Machine Learning Process Flow
- Linear Regression
Hands-On/Demo:
- Linear Regression – Boston Dataset
Skills:
- Machine Learning Concepts
- Linear Regression Implementation
- Machine Learning Types
Learning Objectives: In this module, you will explore 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?
Hands-On/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
Hands-On/Demo:
- PCA
- Scaling
Skills:
- Implementing Dimensionality Reduction Technique
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation,
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
Hands-On/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 analyse the data.
Topics:
- What is Clustering & its Use Cases?
- How does the K-means algorithm work?
- What is C-means Clustering?
- How does Hierarchical Clustering works?
- What is K-means Clustering?
- How to do Optimal clustering
- What is Hierarchical Clustering?
Hands-On/Demo:
- Implementing K-means 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 do Recommendation Engines work?
- Content-Based Filtering
- Association Rule Parameters
- Recommendation Engines
- Collaborative Filtering
Hands-On/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
Hands-On/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 time-dependent 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
Hands-on/demo:
- Checking Stationarity
- Implementing the Dickey-Fuller Test
- Generating the ARIMA plot
- Converting a non-stationary 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?
- Cross-Validation
- How Boosting Algorithms work?
- Adaptive Boosting
- The need for Model Selection
- What is Boosting?
- Types of Boosting Algorithms
Hands-On/Demo:
- Cross-Validation
- 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
Hands-On/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
Hands-On/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 start-up company that makes herbs from different types of plants and leaves. Currently, the system they use to classify the trees that they import in a batch is quite manual. A labourer 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 are an ML expert and must model a recommendation engine so that the user gets a recommendation of books based on the behaviour 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 – User-Based vs Item Based. You 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 Geo Pandas, 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.
Project #2: Industry: FMCG
- 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 skip-gram 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 parts-of-speech 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 Pre-processing Using NLTK
- Exercise: Implementing Text Pre-processing 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 Pre-processing
- 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 Auto-Tagging System
- Introduction to Dataset and Performance Metrics
- Auto-Tagging Implementation Using Machine Learning Part-1
- Auto-Tagging Implementation Using Machine Learning Part-2
- Auto-Tagging 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?
- N-gram Language Model
- Implementing an N-gram Language Model – I
- Implementing an N-gram Language Model – II
- Neural Language Model
- Implementing a Neural Language Model
- Intuition Behind Sequence-to-Sequence Modelling
- Need for Sequence-to-Sequence Modelling
- Understanding the Architecture of Sequence-to-Sequence
- Understanding the Functioning of Encoder and Decoder
- Case Study: Building a Spanish to English Machine Translation Model
- Pre-processing of Text Data
- Converting Text to Integer Sequences
- Model Building and Inference
- Introduction
- Pre-processing 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 State-of-the-Art NLP Library
- Introduction to Stanford NLP: An Incredible State-of-the-Art NLP Library for 53 Languages (with Python code)
- A Step-by-Step 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)
- Geo-coding 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 6-month 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 smartphone-enabled 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 multi-classification 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 hands-on 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 above-listed 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 world-cloud 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 start-up 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
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24 Months Program Duration
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6 Months of live Interactive Classes by Principal or Senior Data Scientists across Industry
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18 months of Sponsored Research Project Work at GEEKLURN AI
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Hackathons (Cash rewards upto Rs.1 Lakh will be awarded to top performers)
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Remote or Hybrid International internship opportunities in USA, UK, Europe & APAC for Merit Students with 2 years of Project Experience Certificate
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Job Guarantee* Program In collaboration with Top HR Consulting Firms across PAN INDIA
Our Data Science & AI Community
- Trusted by 100+ Data Science Experts
- Tech Talks, Webinars from Data Science Heads @ Forbes Technology Council & Reputed MNC’s
- 100+ Data Science Meetups
- 40+ powerful tools & resource to achieve Data Science goals.
- Innovative AI & Big Data conferences, Forums led by acknowledged speakers and proficient analysts.
Real-Time Projects

Identifying and classifying images between animals using CNN architecture according to its visual content
The main objective is to classify whether the image is of Dog or a Cat using CNN Architecture.

Developing a Machine Learning based Predictor to predict the different brand Laptop price
The main objective is to predict the price of different brand laptops based on their configurations.

Spam Classifier
The main objective is built an AI-ML model that can classify any SMS/Emails into Spam or Not Spam Message.

A content based movie recommender engine using cosine similarity
The main objective is to build a content based movie recommendation engine using cosine similarity

Vehicle Brand Classification using Densenet
The main objective is to identify and classify multiple brand vehicles

A content based anime recommender engine using cosine similarity
The main objective is to build a content based anime recommendation engine using cosine similarity

Deep Learning Facial Recognition Multi Task Cascade project using CNN archtiecture
The main objective is identifying similar faces and check various aspects in pictures which includes: facial shape, its skin tonality and positions and other aspects of facial parts

Analyzing behavioral pattern using Whatsapp Chat Data
The main objective is to analyze the behavioural pattern of individuals and understand how the behavioural pattern changes when they are conversing alone and when they are in social circle

Toxicity classification with Natural Langauage Processing using BERT Transformers
The main objective is to build a multi-headed model that's capable of detecting different types of toxicity like threats,obscenity, insults, and identity-based hate. The dataset is of comments from Wikipedia's talk page edits
Project Certificate
Data Science Architect Project Certification from Top MNCs

Program Certificates


Course Features

Guidance from Corporate Specialists
Resolve all theory and project related queries from our with our mentors, who are eminent industry experts.

Corporate Boost Camps
Participate in workshops and live webinars to understand technical terms and trends and make your learning reasonable.

Peer Networking
Build a cohesive learning network with workmates, mentors, and experts to share ideas, address intra-queries, and resolve projects, and practical learning ambiguities.

Placement Guarantee
Our one-of-a-kind learning program promises 100% Job Guarantee and prepares you to be industry ready and equipped with all the requisites for a successful career in Data Science Industry.
Tools Covered


Program Fees
₹ 2,78,000
Inclusive of 18% GST.
We assure you 100% money return in case of being not placed successfully After course completion.
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24-Months Program Duration
- 6 Months of live interactive classes
- 18 Months of Sponsored Project Work - 320+ Hours Live Training Sessions
- Gain 1.5 years of real-time Project Experience Certificate
- Scholarships from day one up to Rs 2 lakhs
- 50+ Sponsored Funded Research Projects
- Get Certified from Top Companies
- 100% Job Guarantee Program
Apply Now
Flexible Payment options Available from Finance Partners.





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Frequently Asked Questions
At GeekLurn, our Data Science Architect course has unique modules crafted in line with the latest industry trends. Being a member of NASSCOM & and an IBM partner, our corporate connect ensures our students are placed in top consulting firms among others.
With a robust course that has its modules crafted as per latest industry trends, a wide range of opportunities open up for freshers. Research data reveals that India recruits a high number of employees in the field of data science, second only to the United States. The demand for data experts span a wide range of industries including IT, Finance, Retail, E-commerce, Consulting & more. At GeekLurn we have a strong industry connect and we offer 100 percent job placements in collaboration with the top consulting firms to our fresh graduates.
Data Science opens up a wide range of opportunities across multiple industries all of which need to mine data into actionable insights for business growth. Whether it is Information Technology, Banking & Finance, Retail or Consulting, the demand for data science professionals is on the rise. After a robust data science course that is crafted in line with the latest industry trends you can look at building a career as a Business Analyst, Data Architect, Machine Learning Engineer and more.
- Business and financial analytics
- Bioinformatics
- Health & pharma
- Robotics & AI
- Chip design
- Cloud computing
- Marketing analysis
- Agriculture
- Gaming
- Education
At GeekLurn, we offer extremely robust courses that are in line with the latest industry trends and that keep you ahead of the curve. The courses come with the opportunity to conduct on ground research and also offer the opportunity to interact with eminent data scientists. With our strong industry connect we are able to offer 100 percent placement in collaboration with the top consulting firms across India.
The Data Science Architect Program at GeekLurn comes with robust modules that are in line with the latest industry trends and give you a definite edge. In addition the course offers you the opportunity to conduct on ground research and also interact with eminent data scientists. With GeekLurn’s strong industry connect, its membership with NASSCOM & IBM partnership, we offer 100 percent placement to our graduates.
Students are also provided career guidance and mentoring by industry professionals to face job interviews that GeekLurn provides through 400+ hiring partners and top HR companies in the industry.