Introduction to Custom Transformers. A Walk-through in Scikit-learn Python

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Data Transformers


Probing the the basics of Confusion Matrix, ROC-AUC curve, and Cost Functions for Classification in Machine Learning.

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Confusion Matrix

Terminologies of Confusion Matrix


Hands-on Clustering Algorithms: A Walkthrough in Python!

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Clustering

How does Clustering differ from Classification?


Probing deep into the fundamental concepts of Machine Learning

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Machine Learning

“Field of study that gives computers the ability to learn without being explicitly programmed”.


Probing deep into cost functions of Regression and its Optimization Techniques: A Walkthrough in Python

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Cost Function

Loss function vs. Cost function

  • A function that is defined on a single data instance is called Loss function.


Boost your data processing performance with Apache Pyspark!

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Apache Spark

Features of Spark

  • Spark is polyglot which means you can utilize Spark using one or more programming languages. Spark provides you with high-level APIs in Java, Python, R, SQL, and Scala. Apache Spark package written in Python is called Pyspark.
  • Spark supports multiple data…


Skyrocket your model performance with Artificial Neural Networks. A Walkthrough in Tensorflow!

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Artificial Neural Network (ANN)

Biological neurons vs Artificial neurons

Structure of Biological neurons and their functions


Categorical Feature Encoding: A Walkthrough in python!

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Categorical Feature Encoding

  • Ordinal Features
  • Nominal Features

Ordinal Features:

  • Ordinal features are the features that have inherent ordering.
  • Eg: Ratings such as Good, Bad.

Nominal Features:

  • Nominal features are the features that don’t have any inherent ordering as opposed to Ordinal features.
  • Eg: Names of persons, gender, yes, or no.

Need for categorical feature encoding

  • Categorical features must be encoded before…


Facing issues with Overfitting and Low accuracy? Feature Selection comes to the rescue

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Dimensionality Reduction

  • Dimensionality reduction is the process of reducing the set of available features in the dataset.
  • The model could not be applied to the entire set of features directly which may lead to spurious predictions and generalization issues which in turn makes the model unreliable.
  • In order to prevent these issues dimensionality reduction is applied.

Need for dimensionality reduction

  • Overfitting is when the model memorizes the data and fails to generalize. Overfitting can be caused by flexible models (like decision tree) and high dimensional data as well.
  • The Overfitted model could not be applied to real-world problems due to the problem…


Techniques for handling the Missing data in Machine Learning: A Walkthrough in Python

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Why handling missing data is important?

  • First, the missingness of data reduces the power of statistical methods.
  • Second, the missing data can cause bias in the model.
  • Third, many machine learning packages in python does not accept missing data. It needs the missing data to be treated first.

Missing data mechanisms

  • Missing Completely At Random (MCAR): The values are Missing Completely At Random (MCAR) if the missing data is completely not related to both observed…

Srivignesh Rajan

Aspiring Machine Learning Practitioner 👨🏻‍💻 💻

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