Link to Content:

Essentials of Machine Learning Algorithms

Created/Published/Taught by:

Sunil Ray

Content Found Via:

Analytics Vidhya

Free? Yes

Tags: algorithms / decision trees / dimensionality reduction / k nearest neighbors (k-NN) / linear regression / logistic regression / machine learning / naive bayes / python / R / random forests / Support Vector Machines (SVMs)

Difficulty Rating:

From Analytics Vidhya:

“Today, as a data scientist, I can build data crunching machines with complex algorithms for a few dollors per hour. But, reaching here wasn’t easy! I had my dark days and nights….

The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world. Through this guide, I will enable you to work on machine learning problems and gain from experience. **I am providing a high level understanding about various machine learning algorithms along with R & Python codes to run them. These should be sufficient to get your hands dirty.”**

This tutorial covers the following topics:

- Linear Regression
- Logistic Regression
- Decision Tree
- SVM (Support Vector Machine)
- Naive Bayes
- KNN (K-Nearest Neighbors)
- K-Means
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting Algorithms
- XGBoost
- LightGBM
- Catboost

Recommended Prerequisites: none specified

Go to Content: Essentials of Machine Learning Algorithms (with Python and R Codes)

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