/ML_Algorithms_Course

Public repo for 365 Data Science ML Algorithms Course

Primary LanguageJupyter Notebook

MLAlgorithmsCourse

This is the public repository for the 365 Data Science ML Algorithms Course by Ken Jee and Jeff Li. In this course, we walk you through the ins and outs of each ML Algorithm. We did not build this course ourselves. We stood on the shoulders of giants. We think its only fair to credit all the resources we used to build this course, as we could not have created this course without the help of the ML community. This course includes the following:

  • Detailed explanations of each ML algorithm (listed below) with specifics on how they work, pros and cons, when to use them, and data preprocessing needed for each one.
  • Two projects using all of the classification and regression algorithms with detailed instructions on parameter tuning
  • Resources that we used to build the course so you have additional details on each topic

Use the discount link for our 3 course bundle (limited time 68% off!) --> The Machine Learning A-Z Bundle

Coding Project Examples

Flashcards

Please go to Ankiweb.net to download Anki and to sign up for account. Please go here to download the flashcards for this course.

1. Linear Regression

2. Regularization

3. Logistic Regression

4. Gradient Descent

5. Decision Tree

6. Random Forest

7. Gradient Boosted Trees

8. XGBoost

9. K-Nearest Neighbors(KNN)

10. K-Means Clustering

11. Hierarchical Clustering

12. Support Vector Machine

13. Artificial Neural Nets

14. Collaborative Filtering