Machine Learning

Machine Learning vs AI

  • AI aims to help machine carry out task, whereas Machine Learning aims to identify patterns in the data

  • AI are broadly categorized into 2 buckets - generalized and applied.

  • Applied AI is focussed to solve a specific task - for example, wealth management, autonomous car

  • Generalized AI - "one ring that that will rule them all" ... well, we are not there yet!

  • What factors are pushing forward the development in Machine Learning today?

    • Realization that machines can instructed to learn by themselves using data

    • Abundance of available data that can fodder the machines

Neural Network

A computer system designed to work like human brain to classify information. It is used to solve some of these tough

  • Infer the author's sentiment behind piece of text

  • Whether a piece is music is likely to make one happy or sad

  • Infer the meaning of natural language in the form of text or audio

AI And Machine Learning Use Cases

  • Data Security - identify the pattern of data access of malware to detect breaches

  • Personal Security - analyze security security screenings at airports, large gathering to identify threats as much faster rate and higher accuracy than human screeners

  • Financial Trading - find which stocks will rise and which will fall

  • Healthcare - understand the risk factors of diseases in large population, media diagnostics

  • Marketing - personalized ad targeting on web properties

  • Fraud Detection - identity and prevent fraudulent transactions in e-commerce and banking

  • Recommendation - product / service recommendation

  • Search - refine searches based on the context, search history and user profile

  • Natural Language Processing - simplify the essential meaning of a text for fast consumption

Github Repo with code solutions

In the above repositories you can see code examples for the following case studies

  • Estimation for insurance premium based on the customer demographic information

  • House price prediction problems based on Kaggle competition data

  • Power demand forecasting based

  • Stock price forecasting using time series analysis

  • Credit risk assessment

  • Credit card transaction fraud detection

  • Predict customer participation of bank marketing campaign

  • Ad-click prediction using web analytics

  • Customer churn prediction for tele comm customers

  • Segmentation of retail customers

  • OCR - image classification

  • Image classification using cutting edge deep learning for high accuracy

  • Detection of DOS attack using the netflow data

  • Sentiment analysis

  • Movie recommendation