Pathway to ML:

In real world we have data problems that we need to solve

Or we have data questions that we have to answer

Here in the pathway, we have real world.

  1. From where we get the raw data it can be from anything like sensors, survey etc.
  2. Then we just store that data into some files like csv, cloud etc.

The above two points terms as the Data Engineering (collecting and storing the data)

  1. After collecting the data we just have to clean it like finding the missing values, recognising the data, restructure data etc.
  2. Then we have Exploratory data analysis which includes data visualization

The above two points terms as the Data Analysis or Scientist (cleaning and visualizing the data)

  1. After the analysis we went towards the ML models
    1. Supervised Learning: predict on outcome
    2. Unsupervised Learning: discover patterns in data

The above 3 points are together consider as Data Scientist or Machine Learning Engineer

  1. After all the above steps we create a data product which include (service, dashboard, application) by this data product we use to predict the future outcomes and we can gain insights on the data

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NumPy:

All the Data Science libraries like skit learn, seaborn, etc. are built using NumPy

It is an N-dimensional Array

It looks similar to the python list but it has much more efficiency

Broadcasting capabilities are useful for quickly applying functions to the datasets

Creation: