Some Top Techniques and Tools for a Data Scientist

  1. How to find the optimum parameter values for a curve fitting problem. Stochastic Gradient Descent Method for Finding Local Optimum.

This is a commonly used numerical optimization technique. Some times, this is also called batch gradient descent algorithm.

2. How to avoid over-fitting problem? Use Regularization.

3. Sparse Matrix Based Prediction – GLMNET in R


4. Naive Bayes Modeling when there are many many conditioning variables

5. Out of Necessity, Real Time Application of Naive Bayes Application – Spam Deduction

6. Fisher LDA and Bayesian Classification

7.Lagrange Multipliers – A Simple Intro

8. Density Based Spacial Clutering Algorithm With Noise – DBSCAN

9. Meanshift Clustering with Scikit-learn and Python

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