Date: 30/09

• Works with Data
• The more data, the better it is to learn from that data
• Machines analyzes large amount of data which humans can't
• ML is about teaching machines to learn the patterns and the machines can generate an algorithm to understand better

ML Model

1. Humans collect data from DBs or Spreadsheets [Db/Sheet \(\to\) Data]
2. Data is used with an ML algorithm and is made into a ML model [Data \(\to\) ML Algorithm \(\to\) ML Model]
3. ML models is used on further data to give desired outputs [ML Model \(\to\) Data \(\to\) Output]

Date 05/10

SuperVised Learning

• Labeled Data + Labels \(\to\) ML Model+Test Data \(\to\) Predictions

Unsupervise Learning

• Unlabeled Data -> ML Model -> Preditcions
• Learns from the Pattern in Data

Reinforcement Learning

• Finds an optimal way to accomplish a particular goal or improve proformance
• Iterative
• Reward based Model
• Eg: Training a Dog

Examples of ML Application

• Basket Analysis(Purchase Analysis)