The Beginners Guide to Machine Learning Algorithms

Link to my ML blogs

Photo by Daria Shevtsova from Pexels

In this article, I have updated links to all my blogs in machine learning.

Statistics

  1. Exploring Descriptive Statistics Using Pandas and Seaborn
  2. Inferential Statistics for Data Science
  3. Hypothesis Testing- Test of Mean, Variance, Proportion
  4. Central Limit Theorem — Clearly Explained
  5. Important Terms in Statistics- Machine Learning

Linear Algebra

  1. Essential Math for DataScience — Linear Algebra

Data Preprocessing

  1. Data Cleaning — How to Handle Missing Values in Pandas
  2. String Operations on Pandas DataFrame

Data Visualization

  1. Data Visualization Using Seaborn

Machine Learning Algorithms

  1. Linear Regression in Python
  2. Line of Best Fit in Linear Regression
  3. The Concepts Behind Logistic Regression
  4. Logistic Regression in Python
  5. An Introduction to Support Vector Machine
  6. An Introduction to K-Nearest Neighbours Algorithm
  7. Naive Bayes Classifier in Machine Learning
  8. Understanding Decision Trees in Machine Learning
  9. Bagging Decision Trees — Clearly Explained

Important terms in Machine Learning

  1. Bias vs Variance Trade-off — Clearly Explained
  2. Confusion Matrix — Clearly Explained
  3. Correlation Coefficient — Clearly Explained
  4. Everything to Know About Residuals in Linear Regression

If you like to read more of my tutorials on Python and Data Science,
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https://indhumathychelliah.medium.com/membership

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