Artificial Intelligence and Machine Learning : Infographics view
- 1. MA Artificial Intelligence & Machine Learning Overview By Mukesh Sinha
- 2. AGENDA ARTIFICIAL INTELLIGENCE – INDUSTRY OVERVIEW MACHINE LEARNING – OVERVIEW TYPES OF MACHINE LEARNING MACHINE LEARNING – PROBLEM CLASSIFICATION CASE STUDIES & DEMO
- 3. ARTIFICIAL INTELLIGENCE – INDUSTRY OVERVIEW Investment in AI is growing at high rate In 2016 , Companies invested $26B to $39B in artificial intelligence TECH GIANTS $20B to $30B STARTUP $6B to $9B 3x External Investment growth since 2013 AI Adoptions – Top 3 industries spending ( % of Spend) Banking 20% of Spend Intelligent Financial health assessment Credit scoring worthiness Healthcare 18% of Spend Rapid diagnostic with Medical images X-Ray, MRI etc Retail 17% of Spend Interactive personalized experience analyzes Shoppers History , mood , expressions etc Data around the World Top 5 AI Companies Face Recommendation Navigation Product Recommendation Speech Recognition Medical System Benefits Digitially Mature Larger Business • JP Morgan has been able to save 360,000 work hours by automating part of their lawyer work by leveraging an operation efficacy applications • UBS implemented AI based platform for post-trade allocation requests . Investment banker took about 45 minutes, to complete the end to end process , AI implementation helped banker to do task in 2 minutes only • Korean airlines built AI based expert system to redcue the number of flight dealy and cancellations and shortened its mainteanance lead times by 90% • NetFlix leaverages AI to tag its movie content and built recommender system for improving customer experience by offering personalized product recommendations and helping customers to find desired product faster Increased Automation Productivity Uncovering new insights Increased Employee Productivity • We are pursuing AI to empower every person and every institution ..so that they can go on to solve the most pressing problems of our society and economy - Satya Nadela , Microsoft • Building genral artificial intelligence (AI) in a way that helps people meaningfully – I think the word moonshot is an understatement for that . - Sunder Pichai , Google • We are living in the golden age of AI , We are now solving problems with machine learning and artificial intelligence - Jeff Bezos Amazon • Artificial Intelligence is the new electricity. - Andrew Ng , Baidu Top Industry Leader Statement on AIAI Use Cases 90 % of World‘s Data created in past 2 years 3 Billion Online In 2000, only 738 million used internet, In 2015 number grew to 3.2 Billion According to World Bank, 75% of people own a cell phone 204 Million emails are sent each minute
- 4. MACHINE LEARNING OVERVIEW Machine Learning Used Machine Learning Tools and Services Trend Forecasting Fraud Detection Price Prediction Product Recommendation Interesting Case Studies Large US based Retail Grocery Store Observation : Men between 30-40 years in age shopping between 5 PM and 7 PM on Fridays, who purchased diapers along with beer in their shopping cart Analysis : Data Analysis revealed that new fathers tend to buy more beer , because they are spending less time at the pub. Solution: Used Data Mining and Anaytics approach Retailer shop move the beer isle closer to diaper isle Benefits :35 % increase in sales of both Large Global Insurance company - AXA Use Case : Approximately 7-10% of AXA’s customers cause a car accident every year, about 1% are so-called large-loss cases that require payouts over $10,000. Objective : AXA adjusters to understand which clients are at higher risk in order to optimize the pricing of its policies. Solution : AXA’s R&D team analyzed the historical data ( Age of Driver , Address , Age of car etc) and using Machine learning algorithm to optimizing price by large-loss traffic accident Benefits : Team achieved 78% accuracy in its predictions Significant advantage for optimizing insurance cost and pricing, Machine Learning - How it works Learn from Experience Learn from Data Follow Instructions What is Machine Learning ? Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Human ComputerMachine Learning
- 5. TYPES OF MACHINE LEARNING • Make machine learn explicitly • Data with clearly defined output is given • Direct feedback is given • Predicts outcome or future Supervised Learning Teacher teaches kids and providing the solution to them Unsupervised Learning • Machine understand the data ( identifies pattern and data • Evaluation is qualitative or indirect • Does not predict or find anything specific Predicting the weather forecast Kids are taking decisions out of their own understanding Reinforcement Learning • Reward based learning • Learning from +ve and –ve reinforcement • Machine learns how to act in certain environment • To Maximize rewards Kids will take actions on his own from past exp, and parent providing the feedback Decision Tree Random Forest Naïve Bayes Support Vector machineK-Means ClusteringTensorFlow MACHINE LEARNING ALGORITHM You Tube Recommendation videos System which play chess Linear Regression Logistic Regression
- 6. MACHINE LEARNING – PROBLEM CLASSIFICATION Classification Algorithm Anomaly Detection Algorithm Regression Algorithm Clustering Algorithm Reinforcement Algorithm • Used to classify record • Limited number of answers • Analyze the pattern • Alerts in case of change in pattern • Used to calculate numeric value • Predicts outcome • Sepeartes the data into groups or clusters • Ease out the interpreation of data • Design how brains of humans respond • Learn from outcome , reward , punishment Email Spam for Gmail and Yahoo Prediction of house price QUESTIONS ML ALGORITHM ALGORITHM DESCRIPTION EXAMPLE Fraud Detection for Credit card Product Recommendation Google self drive car How much or how many ?3 Is this weird ?2 Is this A or B?1 How is this organized ?4 What should I do next?5
- 7. Business Problem ML DEMO - PORTUGUESE BANK age job marital education default balance Housing loan contact day month duration campaign pdays previous poutcome Y 30 unemployed married primary no 1787 no no cellular 19 oct 79 1 -1 0 unknown No 33 services married secondary no 4789 yes yes cellular 11 may 220 1 339 4 failure no 30 management married tertiary no 1221 yes no telephone 25 jul 279 4 -1 0 unknown yes • There has been a revenue decline for the Portuguese bank and they would like to know what actions to take. • After investigation, they found out that the root cause is that their clients are not depositing as frequently as before. • Knowing that term deposits allow banks to hold onto a deposit for a specific amount of time, so banks can invest in higher gain financial products to make a profit. • In addition, banks also hold better chance to persuade term deposit clients into buying other products such as funds or insurance to further increase their revenues. • As a result, the Portuguese bank would like to identify existing clients that have higher chance to subscribe for a term deposit and focus marketing effort on such clients. To predict which clients are more likely to subscribe for term deposits Data Set Description Name Description and Values Personal Client Information Age Age at the contact date (Numeric ≥18) Marital Status Married, Single , Divorced, Widowed etc Sex Male or Female Job Unemployed , 'entrepreneur, technician Name Description and Values Bank Client Information Annual Balance in Euro currency (Numeric ) Default ( Credit) Yes , No , Unknown Housing Loan Yes , No , Unknown Pesonal Loan Yes , No , Unknown Name Description and Values Last contact information Contact Type contact communication type Date When the contact was made Duration Duration of the contact Name Description and Values Other Attributes campaign No of contacts performed during this campaign pdays No of contacts performed before this campaign poutcome Outcome of the previous compaign Output variable (desired target): y - has the client subscribed a term deposit? (binary: 'yes','no') Data Set Business Goal
- 8. PYTHON DEMO STEPS FOR BANKING SCENARIO Import Python Library Packages Pandas , numpy, matplotlib Loading the Banking data and reading the file Drop the variables which are not required for prediction Spliting the data into training set and testing set Applied logistic regression model using training data set Checking the score or accuracy of the model Library File Data Set Splitting the Data Set Data Filter ML Model Accuracy DESCRIPTION PYTHON CODESTEPS
- 9. REFERENCES Machine Learning book - Tom Mitchel The Elements of Statistical Learning book -Trevor Hastie Video Lecture - Andrew NG Video Lecture - Yaser Abu Mostafa Artificial Intellignece Reports from IDC , PWC , DB and McKinsey