rishi-wqd190004 / DS_ML_Lectures

Here you will find jupyter notebooks and python scripts talking about Data Science and Machine learning sessions

DS_ML_Lectures

Here you will find jupyter notebooks and python scripts talking about Data Science and Machine learning sessions

Here one can refer to Weekly Lecture with the serial order for understanding basic of Python, data analysis, visualization and machine learning.

Below are the links for reference:

• SQL Joins
• Machine Learning Flow Chart
• Linear Regression
• Logistic Regression
• Statistical Modelling examples

Some algorithms for Statistical models for classification:

• Naive Bayes (NB)
• Stochastic Grdient Dissent (SGD)
• K-Nearest Neighbors
• Decsision Trees
• Random Forest
• Support Vector Machines(SVM)

Feature Selection

Reference

• Supervised:
  • Intrinsic:
    • Combining below two together
  • Wrapper Method:
    • Split dataset into subsets and train a model using this, based on the output of the model then add or subtract features and train the model again
    • Eg: Forward Selection
  • Filter Method:
    • Features dropped based on their relation to output or how they are correlating to output.
    • Eg: Information Gain, Chi-Square Test, Fisher's score
• Unsupervised