Build a Predictive Machine Learning Model

ML in broader sense is an optimization problem. In this workshop, you will learn about the fundamentals of Machine Learning and with this sound basics, you will step into the understanding of Machine Learning as an optimization problem. You will be introduced to various algorithmns and hyper parameters associated with it. You will get a hands-on experience of creating a ML model, parameter optimization, and exposing the model as an API endpoint.

We will have credit history based loan defaulter prediction as a reference dataset.

Prerequisites for the workshop:

  • Experience in programming (Python or R)

  • IBM Cloud Account(must pre-register(Free) for IBM Cloud here:https://ibm.biz/DataHackSummit)

  • Must carry laptops(Any laptop should suffice)


This is an 8-hour workshop and includes the following modules:
  1. Introduction to Machine Learning
    • AI vs ML vs DL
    • Supervised vs Unsupervised
    • Ml Algorithmns and its usecases
  2. Machine Learning Pipeline
    • Golden Rule of ML
    • Goals of Preprocessing
      • Handling missing data
      • Data Transformation
      • Outliers
      • Categorical Data
    • Feature Extraction
      • Co-relation of Features
      • Dimensionality Reduction
        • PCA
    • Model Training
      • Linear Regression
      • Decision Tree
      • Ensemble Models
      • Feedback and Deployment
  3. Train a Logistic Regression Model (HANDS ON Lab)
    • Hands on using “Credit history” dataset to classify the loan Defaulters
    • Introduction to Watson Studio , Python notebook
    • Loading the data and connecting to Cloud Object Storage
    • Splitting the data, Feature Engineering and model fitment
  4. Model Evaluation and improvisation
    • ROC Analysis
    • Hyper Parameter Optimization
  5. Model Improvisation – Hands On
    • One-hot encoding
    • Hyper parameter tuning
  6. Exporting the Model as API endpoint
    • Watson Machine Language
    • Python notebook based code demonstration
  7. Optional: Hands on to implement WML
    • Improvise the previous hands on Lab to implement WML
Key-take aways:
  • What is DataScience and How to get Started
  • Obtaining Datasets to Work on
  • Cleaning up the data
  • Visualizing and Analyzing data using Watson Analytics tool
  • Basics of Classification and Regression Modeling
  • Deploying Machine Learning Models using IBM DSX
  • Deep diving into DSX and ML Models
Who should attend
Any developer with basic knowledge on Data Science & Machine learning.


Krishna Chaitanya

Krishna Chaitanya is a Watson and Cloud Engineer at IBM. He is a part of Digital Business Group: determined to smoothen the journey for Developers and their startups with IBM’s visionary tech. In the past, he worked mainly with the start-up community. He undertook the roles of an IOT consultant, Computer Vision expert, Cloud Advocate, IOT trainer, Design Automation rookie and quite a few freelance consultancy roles as well, developing a good amount of street cred while doing so. Currently he’s working on creating novel PoCs demonstrating the cognitive capabilities of IBM Watson and its ability to transform the way businesses operate forever

Workshop Date:
24th November, 2018

Workshop Venue:
Hotel Royal Orchid Bangalore

Make sure you don’t miss this exciting workshop by IBM.

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