IBM Certified Accelerated Bootcamp Series on Data Science & AI

Attend accelerated bootcamp series designed by IBM. Learn Python, NLP, Machine learning & 20+ data tools to become an IBM certified Data Science expert. Get started today.

7 Days Live Bootcamp Online Session

July 11 - 17, 2022

08:00 PM - 10:00 PM

Program Overview

Key Highlights

Become an IBM certified Data Science expert.

6 months LMS access provided by IBM Data Science.

Get project experience and course completion certificate from Geeklurn (IBM Silver Business partner and Nasscom).

Live training bootcamp series sessions from Data Scientist.

Learn Python, Machine learning, NLP & 20+ Data Science tools

Get an opportunity to work as a project intern in real time AI companies across the globe.

Meet your Instructor

Udayan Goswami

Sr.Data Scientist at Verizon

Udayan Goswami is a University of London Data Science expert. Python, PySpark, Hive(basic), R and SPSS, SQL, Spark, and Retail Banking are just some of his areas of expertise. He has a solid statistical understanding and a wealth of experience in constructing predictive and statistical models.

Thejasvi T V

Sr.Data Scientist at Motorola

Thejasvi TV is a professional Data Scientist and Certified Six Sigma Black Belt (ASQ) with over 15 years of expertise. He has extensive knowledge of emerging analytics, data mining developments, and machine learning techniques/algorithms with a practical expertise in R and KNIME programming languages.

Student Reviews

Who is this course for?

Working professionals IT, Graduate Students

Anyone who is comfortable programming languages, using loops, lists, dictionaries, etc.

Anyone who want to learn more about machine learning

Anyone with interest in Natural Language Processing (NLU / NLG) and it’s applications


Day 01 - An introduction to Big Data Analytics, DS and AI

  • What is Big Data, DS and AI
    • Terminologies, explanation and examples
  • Data Pipeline
  • Data Representation 
    • Structured data
    • Unstructured data
  • Data driven inferences
    • Data insights/visualization 
    • Data storytelling
  • Ethics in AI and AI Governance
  • State of the art in AI

Day 02 - A Review of Mathematics for Data Science

  • Statistics 
    • Descriptive and inferential statistics 
  • Probability 
    • Random variables 
    • Probability distributions 
    • Sampling methods
    • Regression
    • Hypothesis testing 
  • Differential Calculus 
    • Functions, distance metrics and gradients (slope)
    • Ordinary differential equations  
    • Partial differential equations 
  • Linear Algebra 
    • Vectors & matrices
    • Matrix transformations
    • Vector spaces
    • Least square methods
    • Eigenvalues and vectors

Day 03 - A Survey of preliminary Python programming and libraries required for Data Science

  • Python 3.9+, core python libraries and environment
    • Jupyter notebook 
    • PyCharm 
  • Numeric python 
    • Numpy 
    • Scipy
  • Data Wrangling 
    • Pandas
  • ML libraries
    • Scikit learn 
    • Statsmodel
    • XGBoost
  • NLP
    • NLTK
    • Spacy
    • Gensim
  • CV
    • Opencv
    • Scikit-image
  • DL
    • Pytorch 
    • Tensorflow
    • Transformers (hugginface transformers)

Day 04 - Machine Learning Algorithms

  • Regression
    • Linear regression 
    • Polynomial regression
  • Classification 
    • Logistic regression
    • K-nearest neighbors 
    • Naive Bayes algorithm 
    • Tree based models
      • Decision trees
      • Random forests
    • Boosting models
      • XGBoost
    • Support vector machines 
      • Linear SVM 
      • Kernel SVM
  • Clustering 
    • K-means
    • DBScan 
    • Hierarchical agglomerative clustering
    • Spectral clustering

Day 05 - A general discussion on the tools and techniques to improve ML models and generalization.

  • Feature engineering
  • Dimensionality reduction 
  • Feature selection 
  • Overfitting & regularization 
  • Cross validation & model selection
  • Bias-variance tradeoff
  • Data drift 
  • Modeling metrics

Day 06 - Building and Analysing a simple Machine Learning model on a small data set

  • Hands-on/walkthrough of an end-to- end ML problem (like Churn Prediction or Customer Segmentation)
  • Overview of CRISP-DM agile ML lifecycle:
    • Business understanding 
    • Data understanding 
    • Data representation 
    • Modeling
    • Evaluation 
    • Deployment
    • Iterating the above to improve model performance
  • Take home assignment

Day 07 - Deep learning for NLP, CV and other unstructured data

  • Deep learning 
    • Deep neural networks (DNN)
    • DNN architectures for CV
      • Convolution Neural Nets (CNNs)
      • Vision Transformers
    • DNN architectures for NLP
      • Recurrent Neural Nets (RNNs)
      • Long Short Term Memory networks (LSTM)
      • Transformers 
  • Applications
    • NLP applications
      • Chatbots, Search engines, voice assistants, etc.
    • CV applications
      • Face detection and recognition, OCR, image search, etc.

Get Certified

Get certified by the experts with the Certificate of Achievement on successful completion of the bootcamp

Official and verified

Receive an instructor signed certificate with institution’s logo to verify your achievements and increase your job prospects

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Add the certificate to your CV or your Resume or post it directly on LInkedin. You can even post it on instagram and twitter.

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