Skip to content

Kafeel8686/appliedml_workshop_dhs_av_2019

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

46 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Workshop: Applied Machine Learning

Workshop @ DataHack Summit 16 November,2019

Have you ever wondered how to apply machine learning to business problems? This workshop is specially designed to help learn the concepts, tools and techniques involved. You will go through real-life case studies and experience how this is done in the industry. The focus of this workshop will be on the machine learning pipeline data cleaning, feature engineering, model building and evaluation. You will also learn how to structure a business problem as an ML problem, and then go on to build, select and evaluate the model.

Agenda:

  • Introduction to ML Ecosystem and Data Science
  • Data Preprocessing and Analysis
  • Data Visualizations and Storytelling
  • Regression Models
  • Classification Models
  • Advanced ML - Feature Engineering and Regularization
  • Model Hyperparameter Tuning
  • Advanced ML - Supervised Models
    • Support Vector Machines
    • Decision Trees
    • Ensembles
  • Pipelines
  • Advanced ML - Deployment
  • Unsupervised ML - Clustering
  • Advanced ML - Natural Language Processing
  • Advanced ML - Building and Deploying Deep Learning Models

Pre-requisites

  • System Requirement and Setup

    • Laptop with at least 4-8 GB of RAM
    • We will be using Google Colab for the workshop, hence make sure you have a google login and know how to use colab
  • Offline Setup [Optional]

    • GPU good to have but optional
    • Install Anaconda link
    • Install Python3.6 on Anaconda link
  • Pre-reads

    • Programming knowledge in Python (Python: link)
    • Jupyter Notebook Environment familiarity (link1, link2)
    • Basics of Machine learning (link)
    • Google Colab Intro (link)

    Instructors

About

Content for Applied ML Workshop @ DataHack Summit 2019

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.8%
  • Other 0.2%