Data Science | Statistics | Machine Learning

Biases in data that we should all be aware of to build a reliable and fair machine learning model

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Data Science | Machine Learning | Lessons Learned

What have I learned working in the data science field?

1. You will spend 80% of your time in data preparation


Data Science | Statistics | Machine Learning

Important statistical concepts for data scientists and how to use them


Machine Learning|Neural Networks|Activation functions|

Popular activation functions and their use


Polynomial Regression | Data Science | Machine Learning

Determining the best degree of polynomial to choose in a polynomial regression.


Checklist pdf | Data Preparation | Exploratory Data Analysis | Data Mining

A reference checklist for Data Analytics professionals


HYPER-PARAMETER TUNING | L1 VS L2 REGULARIZATION | SUPERVISED LEARNING

Choosing the right hyperparameter values using Cross-Validation

  1. It’s prone to overfitting with many input features and,
  2. It cannot easily express non-linear/curvy relationships.


AI replacing jobs | Jobs AI will replace | Jobs and AI

How to augment machine intelligence and what the future will look like for humans in terms of jobs

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Statistics | z score table | z score formula

Understanding how z-scores were invented are how they are used

  1. What is a Z-score — Formula and definition.
  2. How to use Z-score using a toy example.
  • z < 1.81 - Distress “Zone”
  • 1.81 < z< 2.99 - Grey “Zone”
  • z > 2.99 - Safe “Zone”


Data preparation | Machine Learning | Data Science

Feature scaling using python. Understanding why feature scaling is required and the two common types of feature scaling methods

  1. What is feature scaling and why it is required in Machine Learning (ML)?
  2. Normalization — pros and cons.
  3. Standardization — pros and cons.
  4. Normalization or Standardization. Which one is better.
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Swapnil Kangralkar

Data Scientist and Project Management Professional at Government of Canada. Visit https://swapnilklkar.github.io for more.

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