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Machine Learning

 

As part of my program, I recently took a course that leaned heavily on machine learning.  As part of preparing for the exams and completing assignments, I collected a set of online resources. I'd done a little bit in another class as well.  Most the resources below fall into two major categories: 1) knowledge (how do the machine learning algorithms work mathematically), 2) executing them in Python. These algorithms can certainly be carried out in other languages, but this was what we used for the courses I took. Some of these refer to pyspark, which you can use in Python without Spark.

 

Links

Unsupervised Machine Learning: Flat Clustering

scikit-learn: Machine Learning in Python

PCA using Python

Keras

Unsupervised Learning | Clustering and Association Algorithms in Machine Learning (video)

Clustering Introduction - Practical Machine Learning Tutorial with Python (video)

KNN Algorithm - How KNN Algorithm Works With Example (video)

Logistic Regression in Python (video)

Decision Tree Algorithm With Example  (video)

Logistic Regression in Machine Learning using Python

Classification in Machine Learning (video)

Naive Bayes Classifier in Python (video)

K Means Clustering Algorithm (video)

Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function (video)

Random Forest Algorithm - Random Forest Explained (video)

Support Vector Machine - How Support Vector Machine Works (video)

Bagging and Boosting (video)

Boosting Machine Learning Tutorial (video)

AdaBoost, Clearly Explained (video)

StatQuest: Principal Component Analysis (PCA), Step-by-Step (video)

Machine Learning #86 Multiclass Classification (video)

Machine Learning: Multiclass Classification (video)

PySpark MLlib Tutorial (video)

Classification and regression

Kaggle

Natural Language Processing

 

 

 

 

 

 

Coding Tutorials and Examples

 

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Last updated 2019 September 17