Coursera: Machine Learning Course: Notes #1

Coursera: Machine Learning Course: Notes


Sections:


Why you should learn machine learning with us:

  - Learn about: Applications of machine learning, how to build machine learning systems, how the algorithms behind machine learning work and how to build these algorithms.
  - Old view of ML (Machine Learning):
    - Start with data sets
    - Feed data to ML algorithms
    - Show data relationships
    - Publish results
  - Many intelligent applications are using ML: successful companies such as Google, Amazon, Netflix all use machine learning for at least one aspect of their functions. These are disruptive companies: These companies change the established market.
    - Examples:
    - Product recommendations used by Amazon. This disrupts the retail market.
    - Movie recommendations used by Netflix. This disrupts movie theater business.
    - Smart advertisement choice used by Google.
  - ML helps disrupt the markets: This disruption is positive for the consumer as it helps the consumer acquire the product more efficiently.
  - ML pipeline:
    - Data set   --->   Algorithm ----> Intelligent output

 1 - Predicting house pricing:
    - Try to find the value of house with an unknown value (off the market).
    - House value derived from data: Look at other house sales and the associated data and apply this information to the house of unknown value.
    - Data sets based on different features of the house.
    - ML Method: Create a relationship, such as a linear regression: Relate the house attributes to sales price. Then use relationship to predict price for house of unknown value.


     - ML Method = Regression


 2 - Sentiment analysis: Restaurant review:
    - Acquire review with both positive and negative feedback.
    - Use previous data sets of reviews that have been categorized based on positive and negative feedback.
    - ML Method: Compare positive and negative feedback to get an overall conclusion on the unknown review.


    - ML Method = Classification


 3 - Document retrieval:
    - Select an article or book that would be of interest to the reader or consumer.
    - Data set is large collection of all possible books that could be recommended for reader.
    - ML Method: Distinguish data by finding structure in data: Divide data into groups of related articles: genres. Infer genre of article or book that is to be recommended.


    - ML Method = Clustering


  4 - Product recommendation:
    - Select a product that is to be recommended to the consumer or buyer.
    - Data set includes past purchases. Use past purchases to infer future purchases.
    - ML Method: Find relationship between what consumer bought before and what consumer is likely to buy in the future. Use other consumer's purchase histories to infer another buyer's recommended products.
    - Customers compared to products matrix created. Shows which products were actually purchased from previous recommendations: Learn features of the consumer and features of the product: Compare interests.


    - ML Method = Matrix Factorization


 5 - Visual product recommender
    - Data set: inputted images to search from. From original images, new images are found with visual similarities.
    - To find new images, distinguishable features must be found in the images so they can separated.
    - ML Method: Look at neural-networks to find more and more features with increasing precision in differences.


    - ML Method = Deep Learning