The Basics of Machine Language

 Machine Learning algorithms enables the computer to learn from data, and even improve themselves without being explicitly programmed.

Arthur Samuel, in 1950 developed a computer programme to play checkers. Features of the programme were:

• Scoring functions using positions of the pieces on the board, and measuring chances of winning (alpha-beta pruning)
• Designed a number of  mechanisms allowing his program to become better. In what Samuel called rote learning, his program recorded/remembered all positions it had already seen and combined this with the value of the reward function.
• Popularized the term "Machine Learning" in 1952

The need: Using data to answer questions and perform actions like:

• Is there traffic on the way home
• Which Netflix movie would I like?
• Will it rain in the next hour?
• How long will my food take to be delivered?

How does ML work?

Where does the data come from? - It can be from the present storage or data base or it can be scientifically collected.

Machine learning can be basically of three types:

How do ML work in our everyday life?

Recommendation’s and personalization’s – and we have got addicted to it. 

Machine learning in Marketing:

• Dynamic pricing
• Content creation
• Insights
• Sales Forecasting

Machine learning across Industry:

Manufacturing:

• Predictive maintenance or condition monitoring 
• Warranty reserve estimation
• Propensity to buy
• Demand Forecasting
• Process Optimization
• Telematics

Retail:

• Predictive inventory planning
• Recommendation engines
• Upsell and cross channel marketing
• Market segmentation and targeting.
• Customer ROI and lifetime value

Health care and life science:

• Alerts and diagnostics from real time patient data
• Diseases identification and risk stratification
• Patient triage Optimization
• Proactive health management
• Health care provider sentiment analysis

Travel and Hospitality:

• Power usage analytics
• Seismic data processing
• Carbon Emission and Trading
• Customer-specific pricing
• Smart grid managment
• Energy demand and supply optimization

Financial Services:

• Risk analytics and regulation
• Customer Segmentation
• Cross-selling and Up-selling
• Sales and Marketing campaign management
• Credit worthiness evaluation

Energy Feedstock and Utilities:

• Aircraft scheduling
• Dynamic pricing
• Social -media - customer feedback and interaction analysis
• Customer complaint resolution
• Traffic pattern and congestion management

Emerging uses of machine learning are:

Find skin cancer, Car automation, Personalization in stores, google and apple glasses – understand the interest, and help you see that. Data Model, can help clean up old family photos in seconds and make them look like new. We are not aware how much this is effecting our daily lives now, if they stop we will be bored and will struggle, you have your favorites popping up in you tube, netflix, amazon, the problem of choosing is minimized. 

• ML is becoming more engaging in everyday technology and services.
• We are becoming more dependent on ML
• ML is not perfect but is improving at a rapid pace

Computers are able to see, learn and hear. Welcome to the future. - Dave Waters.

Thanks to Jas Dhaliwal for the wonderful insight.