vatsalparsaniya/ml_knowledge

📙 Machine learning Knowledge Project 📙

Hi there, I'm Vatsal 👋🏼👨🏻‍💻

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Data Science professional with over 2.5 years of expertise in Information Retrieval, NLP, and microservice development. I have hands-on expertise in innovating new products using Data Science and Machine Learning. I have experience collaborating with cross-functional project teams, SMEs, and leading teams with multiple data scientists and engineers. I am passionate about multilingual search systems and uncovering valuable insights from complex data.

• 🔭 I'm into NLP, Machine Learning, Data Science.
• 🌱 I’m currently Data Scientist at Embibe
• ⚡ Check out my portfolio site vatsalparsaniya.com for all my projects and and my works.

Some of my Github Public Stats 💻

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ml_knowledge's Issues

Add Ensembling Knowledge

#1

Algovis Python Library

I can share a script on Algovis python library that is used for visualizing and analyzing algorithms. In that there is visualization for bubble sort and quick sort that is a fun task and a great learning experience.
If allowed I can create a Pull Request for the same.

Ensemble model

Simple way to understand model ensemble in Machine Learning is you can take example of a case where there are more than one models trained for one dataset but with different parameters.

let's say there are 3 models M1,M2,M3.

The predictions for above models are P1,P2,P3.

So, we can take the average or the best prediction of the above model.

ex: Random Forest

Algovis library for Searching Visualisation

I wish to create a PL for searching algorithm viz Linear and Binary search for visualisation using Algovis library.

Hacktoberfest

Collect Machine Learning, Deep Learning, DataScience Concepts or Algorithms

• Ex. I want to Add Concepts of Outlier
  • make a folder named Outlier, if already present in repository add additional information into that.
  • create Readme.md file in Outlier folder add your knowledge there
  • You can add knowledge like:
    • Why this concept
    • How this algorithm works
    • Advantages
    • Disadvantages
    • What Are the Basic Assumption
    • In which problem statement you can your this concept or Algorithm
  • list of concepts: Stacking, Ensembling, Cross-Validation, Stacking VS Blending, standardisation, normalization, standardisation vs normalization, etc...
  • List of Algorithms: Linear Regression, Logistic Regression, Decision Tree, SVM (Support Vector Machine), Naive Bayes, KNN (K- Nearest Neighbors), K-Means, Random Forest, etc...

Add standardisation, normalization, standardisation vs normalization Knowledge

#1

Add Stacking Knowledge

#1

Add random forest algorithm

Add Cross-Validation Knowledge

#1