Data Science Certification Program

  18 Weeks Classroom Lectures with Mini Projects on Kaggle Live Data Sets
  6 Weeks E2E Industry Projects, Kaggle Competitions & Case Studies

UPCOMING BATCHES

   Batch Start Date Timings
  Weekend 21-Nov-20 6:00 PM - 8:00 PM IST
  Weekend 28-Nov-20 6:00 PM - 8:00 PM IST

Download program brochure

DEMO CLASS : LINEAR REGRESSION

Experience the Difference

DEMO CLASS : BIAS & VARIANCE TRADE OFF

Experience the Difference

"AI will add 2.3 million jobs by 2022" ~ Gartner
Program Features

"Artificial Intelligence is the New Electricity" ~ Andrew Ng

Technology giants are rapidly migrating to an AI-ML world. The industry is getting disrupted. It is very obvious where tomorrow’s jobs are going to be. Upgrading your expertise in AI/ML is the best way to sustain your career growth and professional stability.
Instructor led Live Online Program.
Sessions recording available to every student after each session.
Certification Program.
Program offers Blended Learning Pedagogy with Minimal Disruption To Work Schedule.
Program is specifically designed for industry professionals/Freshers who are looking for a lucrative career in premier domain of Data Science and Machine Learning.
Curriculum is Industry algined with domain expertise and cultivate individual skills to build Data Science Products for Live Industry Use cases and design E2E solution to business problems.
Avanced Data Science and artificial intelligence concepts such as Neural Networks,Deep Learning Natural Language Processing and Computer Vision.
Learn cutting-edge applications of learned concepts through industry projects created under guidance of industry experts: Actual Predictive Modelling OR Product Design with Real Life applications.
Apply for suitable Data Science, Machine Learning and AI profiles and you will get expert mentorship which will help you prepare for best of the industry jobs.
Skills : Data Science | Machine Learning | Deep Learning | Computer Vision | Natural Language Processing (NLP)
This course covers in-depth understanding of Mathematics, Statistics & Computer Science working behind complex algorithms and its applications.
Why this Program ?
Your current organization does not justify your potential!!
Concerned about job security & looking for a prolonged, tension fee career!!
Looking for a high paying job in a top IT company!!
BUT Your current skills are obsolete and not taking you anywhere?

DATA SCIENCE is the ANSWER!!

Advance Certification Program to grab niche Data Science opportunities with product companies/service companies and high paying start-ups.
Instructor led Live Online Program.
Sessions recording available to every student after each session.
EASY EMI OPTION: Pay in Two Equal Installments - 1st installment after the First Class and 2nd installment after 4 weeks.
After the program you will become an expert in Data Science ,Deep Learning, NLP and Computer Vision.
Program offers Blended Learning Pedagogy with Minimal Disruption To Work Schedule.
Live Projects : Hands-On Industry Projects at Kaggle.
You will Learn how to Align current experience with new DS learning and cracking DS job interviews.
Get domain experience counted in a big way, get data science knowledge work for you and learn how you can apply knowledge at your client and company for implementing new ideas and projects using Data Science and AI.
Surprise them with Ideas, Start thinking in like a DATA MAN, start looking for optimization of existing business modules, functionality. Start giving them solutions that can help them to innovate and excel in competitions with clear picture of Profit.
If You are a Manager - You will learn how to Manage E2E projects, get the right team on board and deliver prjoects successfully in Data Science.
If You are a Architect/Business Analyst - You will learn how to design E2E projects, architect the popeline right and deliver prjoects successfully in Data Science.
You are not a fresher You will Learn how to get all your experience counted and how business/domain knowledge actually makes you a key player in the game.
You will get clear Career Path after course completion and would be able to crack Data Science Interviews soon after completion of the program.
Coming from differnt doamin experience and business line !! Do Not worry, If you are passionate about learning, our dedicated mentor with targeted curriculum will turn the table for you in short while
Coming from non IT Background and No prior exreience in Coding/Programming !! Do Not worry, we have dedicated modules that will help you acquire all the required skills in a very short duration.
Program Mentors
Ajay Sir from Oracle with 14+ years of industry experience
Gaurav Sir from BITS Pilani with 14+ years of industry experience
Who should take this course?
  • Analytics professionals who want to work in Data Science or artificial intelligence domain.
  • Professionals working in eCommerce and other online consumer based organization and want to enhance skills in AI and Data science.
  • Industry professionals looking for big leap in career and want to switch into the field of Data Science.
  • Entry level engineers looking to build a career in Data Science and Machine Learning.
  • Experienced professionals,Architect, Business Analyst, Project managers who want to harness Data Science skills in their fields to effectively manage new projects coming in to data sciences & ML and giving technical insight to project team to deliver best solutions to the customer.
Program Details:
  • Registration OPEN for new batch starting from:
    21-Nov'20 , 6:00 PM-8:00 PM IST
    28-Nov'20 , 6:00 PM-8:00 PM IST
  • Program Duration : 24 Weeks
  • Live Lectures with Mini Projects on Kaggle Data sets : 18 Weeks - 72 Hours
  • Project Duration : 6 weeks - 24 Hours - E2E Industry Project at Kaggle
  • Weekly Assignments & Practice Projects
  • Weekend Program : 2 Classes a Week (Sat & Sun)
  • Session Length : 2 hours
Registration Process
  • Register for Program (Make sure all the details given are correct).
  • Registration confirmation will be sent by an automatic mail.
  • Online session details (ZOOM Meeting invite) to join classes will be shared on registred mail id TWO days before starting of the program.
Explore Program
Statistics for Machine Learning
  • Statistics and it's applications
  • Descriptive Statistics / Infernetial Statistics
  • Measures of Central Tendency - Mean,Median,Mode
  • Measures of Dispersion - Range,IQR,Standars Deviation,Variance,Corelation
  • Measures of Position - Percentiles,Quartiles,Z Score Deviation,Variance,Correlation,Covariance
  • Probability density function
  • Probability theory and Probability Distributions
  • Normal Distribution
  • Central limit theorem
  • Hypothesis Testing
  • Test Statistics
  • T-value
  • P-value
  • Hypothesis Test for Regression Slope
  • Mathematics for Machine learning
  • -Linear Algebra
  • -Multivariate calculus
  • -Probability theory and Probability Distributions
  • -Matrices, Eigen Vectors and their application for Data Analysis.
  • Computer Science & Algorithms: Matrix implementation
  • Functions and Graphs
  • Flow, Conditions & Loops, Variables, Operations, Funcations
  • Data structures (Array, Lists,Strings, Sets, Dictionary, Tuples, Series, Tensors)
  • Data Frame Manipulation and Data Visualization.( Metplotlib, Plotly)
  • What is Data Science – Examples and Applications
  • Numpy and Pandas Tutorial
  • Scikit Learn Tutorial
  • Machine Learning Algorithms
  • Cost Function
  • Metrics for Model Evaluation and Validation
  • Training and Testing
  • Model Overfitting-Underfitting
  • Bias & Variance
  • Gradient Descent Optimization & Learning Rate
  • Bias & Variance
  • Hyperparameters Tuning & Model Optimization
  • Mini-Project to understand and implement Machine Learning Basics
  • Data Wrangling
  • Data Pre-processing
  • Feature Transformations
  • Outlier Detection and Handling
  • Handling Missing Values
  • Feature scaling techniques
  • Encoding Methodologies
    • Label Encoding
    • Binary Encoding
    • OneHot Encoding
    • Helmert Encoding...
  • Binning or Discretization Methods
  • Feature Selection
  • Feature Extraction
  • Feature Engineering Concepts and best practices
  • Introduction to Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Naïve Bayes Classifier
  • Bayesian Statistics and Inference
  • K-Nearest Neighbor
  • Support Vector Machine - SVM
  • One mini project hands-on for each algorithm
  • Introduction to Unsupervised Learning
  • K-Means Clustering
  • Agglomerative Hierarchal Clustering
  • Clustering using DBSCAN
  • Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
  • Clustering Mini-Project
  • Feature Selection VS Feature Extraction Techniques
  • PCA
  • Kernel PCA
  • LDA
  • t-SNE
  • Oversampling
    • - SMOTE (Synthetic Minority Oversampling Technique)
    • - Borderline SMOTE
    • - ADASYN (Adaptive Synthetic Over Sampling)
  • Undersampling
    • - Tomek links
    • - Cluster Centroids
    • Cost-sensitive classifiers
    • class-specific weights
  • k-fold Cross Validation
  • Grid Search
  • Bagging
  • ADA boost
  • XGBoost
  • Light GBM
  • Ensembling Techniques
  • Stacking
  • Using Github for all ML models and Projects
  • Training Machine Learning/Deep Learning models at Google Colab
  • Introduction to Deep Learning
  • Machine Learning VS Deep Learning
  • Introduction to Neural Networks
  • TensorFlow/Theano/Keras
  • Deep Neural networks
  • Forward propagation
  • Back Propagation Learning
  • Training & evaluation with the built-in methods
  • Activation Functions
  • Loss Functions
  • Hyperparameter Tuning
  • DropOut Regularization
  • Batch Normalization
  • Early Stopping
  • Vanishing & Exploding Gradient Descent
  • Optimization Algorithms
    • Gradient descent with momentum
    • RMS Prop
    • Adam
  • Sequential/Feed Forward Models
  • Funtional API
  • Model Saving and Reloading
  • Kaggle Projects on Deep Learning
    • Classification Project on Kaggle
    • Regression Project on Kaggle
    • Image Classification Project on Kaggle
    • Digit Classification Project on Kaggle
  • Introduction to Computer Vision
  • Convolutional Neural Networks CNN
    • Archtiecture CNN
    • Concept of Padding
    • Concept of Stride
    • CNN Layer
    • Pooling Layer
    • FC Layer
  • Residual Netoworks- ResNets
  • Case Studies
    • Le-Net5
    • AlexNet
    • VGG16
    • Google Net( Inception Networks)
  • Transfer learning
  • Image Preprocessing
  • Image Augmentation - Keras Generators
  • Callbacks API
  • Optimizers (Adam,SGD,RAMSProp..)
  • Metrics and Losses
  • Kaggle Project on Computer Vision
    • Image Classification Project on Kaggle
    • Digit Classification Project on Kaggle
    • LIVE IMAGE Classification Project on Kaggle
  • Introduction to Natural language Processing
  • Application of NLP
  • NLTK
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop Words
  • Similarity Functions - Begging, Pasting, TFIDF
  • Word Representations
    • One Hot Encoding
    • Word Embedding
  • Learning word Embedding
    • Word2Vec
    • SkipGram , N-Gram
    • Negtaive Sampling
    • GloVe
  • Recurrent Neural Netoworks- RNN
    • BiDirectional RNN
    • Gated Recurrent Unit GRU
    • Long Short Term Memory LSTM
  • Kaggle Project on NLP
  • Different Approaches to Deploying Machine Learning Models in Production
  • System Architecture, Component Integration and Data Pipeline
  • Batch vs. Real-time Prediction
  • Best Practices and Industry Standards
  • Deploy Machine Learning models Using Flask Rest API on Heroku Server
    • What are APIs
    • Environment Setup & Flask Basics
    • Creating a Machine Learning Model
    • Saving the Machine Learning Model: Serialization & Deserialization
    • Creating an API using Flask
    • Test Flask App Locally
    • Deploy to Heroku
    • Test Working App
  • All ML/DL algorithms will be covered with hands-on mini projects
  • Machine Learning projects:-
    • Advance House price Prediction
    • Loan Approval Classification
    • Breast Cancer Detection
    • Mushroom Classification
    • IRISH Flower Multi Classification
    • Wine Multi Classification
    • Diabetes Prediction
    • Titanic Survival Project
    • Credit Card Fraud Detection Project
    • MNIST Digits Classification
  • Deep Learning Projects:-
    • Sentiment Analysis Project
    • MNIST Digits Classification Project
    • MNIST Image Classfication Project
    • Mails/Document Classification Project
    • Named Entity Recognition Project
  • Hackathons & Competitions
  • Introduction to Kaggle Platform and other Data Science Competitions
  • Challenge for Industry professionals:
    How to align current experience with Data Science?
    How to crack Data Science job interviews?
    What is the surety that after Data Science training I would be able to get a better job offer in ML?
  • Answer: You have to complete a domain-specific project to solve a real-world problem using different Machine Learning/Deep Learning.
  • After Data Science course completion, student need to identify and business problem with help of Mentors at IT Bodhi and implement solution end to end using ML.
  • You need to present this project in Data Science interview as white paper to showcase your in depth knowledge of giving ML/DS solutions to customer problems.
  • This project will help in aligning current domain experience to machine learning knowledge and would pave the way to crack ML job interviews.
  • Time duration to complete the project: Depends on the complexity of the problem but generally it takes 2-3 month approx.
  • Acing Data Science interviews
    • Booklet of all ML interview questions
    • Showcasing and presenting ML projects in interviews?
    • Presenting E2E Industry project in interviews?
    • How to align current industrty experience with Data Science learning?
    • How to make a big Impact with Domain knowledge + Data Science learning?
    • Handling E2E business problem solving questions
    • Handling Project management questions data sciences
    • Do's-Dont's in interviews
    • Interviews preparation
  • Resume Preparation
    • How to make a impressive resume?
    • Mention right ML Projects in the resume
    • A good & a Bad resume

Download program brochure

"A breakthrough in machine learning will be worth ten microsoft's"~ Bill Gates
Mentor

Ajay Chauhan


From Oracle

Mentor

Gaurav Goel


From Oracle

Mode of Classes: Online ( ZOOM Meeting )

Summary

  Duration 24 Weeks
  Pre-requisites Passion for learning
  Batch size 5
  Program starts 21-Nov'20
28-Nov'20
  Mode Live Online Classes
  Instructor Data Scientist from Oracle
Need More Information,Please Write to Us

UPCOMING BATCHES & COURSE FEE

21-Nov'20

Start Date

Sat,Sun

Days

Apr'21

End Date

6:00 PM-8:00 PM IST

Timings

96

Hours

24 Weeks

Duration

2 Hours

Class.Length

48

No.Of.Classes

Fee: ₹ 60,000 ₹ 31,500 ( $450 )




28-Nov'20

Start Date

Sat,Sun

Days

Apr'21

End Date

6:00 PM-8:00 PM IST

Timings

96

Hours

24 Weeks

Duration

2 Hours

Class.Length

48

No.Of.Classes

Fee: ₹ 60,000 ₹ 31,500 ( $450 )
Fee is inc. Taxes.
Easy EMI Option: Pay in TWO installments.

DEMO CLASS : LINEAR REGRESSION

Experience the Difference

DEMO CLASS : BIAS & VARIANCE TRADE OFF

Experience the Difference

Payment Options & Refund Policy

  • Payment Options

    BY NET BANKING
    A/C # 916020018356139 , IFSC Code : UTIB0001082
    A/C Name: BODHIRAGA EDUCATION SERVICES LLP
    Bank: Axis Bank | A/C Type: Current
    Send Transaction ID with Name & Phone at info@itbodhi.com

  • Fee Refund Policy

    Course registration is FREE. After the First class,student has to deposit the fee as per the course fee plan to continue with the course. We ensure that you are satisfied with the demo class and want to join the program. Once student deposit the fee, there will be no refund of any kind. In case of any genuine issue if student is not able to attend the classes in current batch than fee can be transferred to the incoming batch but valid for next 2 months only.

My Story

The ML class undoubtedly was a perfect combination of knowledge, creativity and engaging course work. The course was precisely curated to make the complex points appear simpler. Assignments were intriguing. I was particularly impressed with the practical use of the material presented. The fact that almost everyone has the same feedback as mine proves just how powerful and influencing the course was. Thanks for guiding 'Naive' people like me and clearing our 'Biases' and 'Variances' towards ML and starting my journey in to ML world.

Parul Pandey

Second Kaggle Grandmaster Female in World


I love learning new technologies and contributing my share to change the world in a better way. AI & Machine Learning always thrilled me and I decided to understand how Data Science/Machine Learning can help core industries in innovation. I started my journey with IT Bodhi with no prior knowledge. Starting was difficult but interesting. I am still learning and getting more and more excited with new things. Mentors Ajay and Rajnish together are excellent combination of specialists in delivering quality lectures under extremely friendly environment.

Rabindra Pal

AGM at BHEL, INDIA


I selected IT Bodhi after detailed research online and it came out to be a great decision to join IT Bodhi. I had an exceptional experience with IT Bodhi Classes. Commendable faculty that motivated me every time to be a smart learner. Supereminent class & environment to learn and practical application of all cocepts with real time use cases and projects. Great learning expereince at the course which really helped me rebuilding my career.

Rama Shankar Singh

Manager at Bennett, Coleman & Co. Ltd


I did my Machine Learning Training from IT Bodhi. I can bet you that IT Bodhi is the best machine learning training institute in Delhi NCR. It is the best course for beginners as well as professionals who wants to dig deep into the algorithms and concepts of Machine Learning. Ajay Sir and Rajnish Sir are the best trainers who helped me on live projects. I am truly grateful to IT Bodhi for their guidance at every step and it is hard to find mentors like them.

Katikr Tyagi

Placed at Grofers


Frequently Asked Questions

  • How much time will it take to become a Data Scientist?

    Data Science is all about passion and patience and It normally takes 6-8 months (daily 1-2 hours efforts minimum ) to learn all the data science concepts, algorithms and their applications. But at the same time you need to put regular efforts and complete all the projects and assignments on time to expedite the learning process. After completing this course, it is advised to take 1-2 months for self-learning and interviews preparation. Afterwards you are ready to apply Data Science Profile jobs as per your expereince level.

  • I am an industry professional with 10+ years of experience. Would this program help me to change the domain?

    Yes definitely. This is the right program for you to switch to Data Science/ML domain. Data science projects and solutions are not specific to any domain but its' applications are getting implemented everywhere. Having 10+ experience in a domain is actually very preferable for any ML/DS position because you are the right person to engage with client, understand customer problem/business data and applying all your domain knowledge to execute and implement data collection, data preprocessing and end-end solution in a rapid manner.

  • How to align current experience with Data Science learning?

    This is the RIGHT questions to ask. Competing data science certification program at IT Bodhi is very much sufficient for entry level professionals 0-4 years to get very good job offer as machine learning engineer. But when you have experience 4+ then you have to put extra efforts to align your current experience with new data science learning and justifying your candidature in interview to grab position as ML Architect, Data Scientist, Data Science Project Manager etc. This is where end-to-end domain specific Industry project implementation comes in to the picture under guidance of IT Bodhi mentors. Successful implementation is guarantee of cracking job interviews. It requires dedicated and disciplined efforts to complete project in stipulated time duration.

  • How to crack Machine Learning job interviews?

    Strong data science fundamentals , Deep understanding of algorithms and applications. E2E Industry project completion is necessary to make sure you crack job interviews becuase this project will help you to show case your learning and its business application in real time. Also Resume Preparation, Interview Strategy and Interviews FAQs will be shared and discussed in the program.

  • I am from Non IT/CS background with NO or little knowledge of programming, Am I eligible for this program and will it help me to get job in Data Science?

    Yes, definitely. After doing our courses, you will have sufficient theoretical knowledge to take data science interviews with hands-on experience in E2E industry project. Python is part of the program and Mentor will help you in learning programming basics and gradually you will be proficient in python as course progress. Python is very user friendly language and can be easily learned.

  • Is any specific programming language experience required?

    The Program will use Python libraries. The necessary python skills can be easily picked up by anyone with programming experience in any language. Tutorial introduction to the Python language will be organized by Mentor and that is part of the program. Learning material on Python for self-study will also be made available.

  • What is the surety that after course training I would be able to get a better job offer in Data Science?

    Industry is aggressively looking for Data science resources in a big number.E2E Industry project and strong fundamentals with dedicated efforts are surety to get job offer in ML/DS.

  • Do I need to bring my own laptop?

    Yes, You have to bring your laptops. Exercises will be on a cloud based platform.

  • Would I be given certificate for the program?

    Yes, Certificate will be given at the end of the program.

  • What happens if I miss a class?

    It is recommended that you do not miss a class. In case it is unavoidable,session recording are always available for your reference and we would also try to conduct a special session to cover missed topics.