Lifelong LearningComputational and Data-Enabled
Science and Engineering Coding Tutorials and Examples
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
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