Data Science with Python

Certification Program.
Interviews Preparation with Placement Assistnance
Data Science

LINEAR REGRESSION EXPLAINED

DEMO CLASS - 1

HANDLING IMBALANCE DATA SETS

DEMO CLASS - 2

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

"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.

What will you learn?

Become an Expert Data Scientist - with applied knowledge of Machine Learning, Deep Learning, NLP and Computer Vision.
Align current experience with data science and crack job interviews.
If You are a Manager - You will learn how to Manage E2E projects.
If You are a Architect/Business Analyst - You will learn how to design E2E projects.
Clear Career Path after course completion and would be able to get big jib opportunities in Data Science domain.
Learn cutting-edge applications with industry projects under guidance of industry experts: Actual Predictive Modelling OR Product Design with Real Life applications.

Program Features

Data Science | Machine Learning | Deep Learning | Computer Vision | Natural Language Processing
Advance learning with in-depth understanding of Mathematics, Statistics & Computer Science working behind all complex algorithms and real life applications.
Instructor led Live Online Program.
Program offers Blended Learning Pedagogy with Minimal Disruption To Work Schedule.
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.
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.
Coming from Non IT or Non Coding/Programming vackground !! Do Not worry, we have dedicated modules that will help you acquire all the required skills in a very short duration.
Industry Projects : Hands-On Industry Projects aligned with your domain.
EASY EMI OPTION: Pay in Two Equal Installments.

Challenge for Industry professionals !!

Coming from different domain experience and role?
Align current experience with Data Science?
Crack Data Science job interviews?

How our program helps?

DOMAIN SPECIFIC CAPSTONE PROJECTS You have to complete couple of domain-specific E2E projects solving real-industry problems under guidance of expert mentors.
DOMAIN SPECIFIC CASE STUDIES You have to work upon real case studies relevant to your business domain to learn latest tools and techniques used in industry.
LATERALIZE Acquiring the skills with practice that are adjacent to your current know-how and relavant to your business domain.
PROACTIVE Learn how to experiment with new ideas, THINK ! How can existing business work flow can be optimized using DATA.
INTERVIEWS PREPARATION Crack data science Interviews with targeted preparation under expert guidance.
If you aspire to join top 1% league of data scientists in the long-run, it’s all about your interest and you asking whether you are interested to work with data and envisage yourself in a role where data and decision making are aligned.

Program Mentors

Gaurav Goel Data Scientist from BITS Pilani with 14+ years of industry experience
Rajnish Chauhan Data Scientist from ST Microelectronics 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.

Course Fee and Payment Process

  • Full Payment Mode : Pay full course Fee ( ₹34,950/- ) and join the course.
  • Easy EMI Mode :
    • Pay Registration Fee : ₹1,000/- and attend 1 week classes.
    • Payment after 1 week : ₹24,000/-
    • Payment after 8 weeks : ₹10,950/-
  • No Refund Policy : Refer recorded demo class videos before registering for the course which will give you fair idea about the Mentor, kind of problems being disucssed and quality of the course. No refund of any kind after fee submission.

Course Contents

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
    • Project-1:Image Classification Project on Kaggle
    • Project-2:Digit Classification Project on Kaggle
    • Project-3:Glucoma detect blindness 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
  • Project-1:Building a Resume Classifier
  • Project-2:RASA Chatbot
  • Project-3:News Classification
  • Project-4:Mail Classification
  • Project-5:Sentiment Classification
  • Project-6:Reviews Classification
  • 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
    • -Covid19 Global Forecasting and Analysis
    • 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
  • What is Time Series data?
  • Different components of Time Series data
  • Stationarity and Time Series Smoothing
  • Exponential smoothing models
  • Implement ARIMA model for forecasting
  • Forecasting for given Time period
  • Acing Data Science interviews
    • Booklet for Data Science 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
"A breakthrough in machine learning will be worth ten microsoft's"~ Bill Gates
Mentor

Gaurav Goel


From Oracle

Mentor

Rajnish Chauhan


From ST Microelectronics

Mode of Classes: Online ( ZOOM Meeting )

Summary

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

SUCCESS STORIES

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

2nd 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



UPCOMING BATCHES

20-Nov21

StartDate

Sat,Sun

Days

Feb'21

EndDate

10:00 AM IST

Timings

84 Hours

Duration

20+

Weeeks

2+ Hours

ClassLength

40+

No.Of.Classes


PAYMENT OPTIONS AND 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
    Share Transaction ID with Name & Phone at info@itbodhi.com

  • Fee Refund Policy

    No Refund after fee deposit. Refer Trial video class recordings before registering for the course which are availble for each and every course. Recorded videos are designed to give you fair idea about the mentor and the quality of the course. After fee submission, there will be no refund of any kind. In case of any genuine issue fee can be transferred to the next batch but valid for next 2 months only.

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.

  • Would this program help if I have 10+ years of experience?

    Yes definitely. This is the right program for you to switch to Data Science/ML domain. Data science/AI solutions are not specific to any domain Having 10+ experience in any domain makes you 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?

    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 Data Scientist. But when you have experience 4+ then you have to learn applied knowledge of aplication, implementation and optimization to justify your candidature for Data Science profile. This program will guide you to implement end-to-end domain specific Industry project with practical use cases and design solutions.

  • How to crack Data Science job interviews?

    Strong data science fundamentals , deep understanding of algorithms and practical real life 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.

  • Non-IT background, Am I eligible for the course?

    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.

  • 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.

  • After course completition,any surety to get 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.

  • 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.

  • Would I be given certificate for the program?

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

  • Do I need to bring my own laptop?

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