Choosing the Right Machine Learning Algorithm
- 3.6
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Brief Introduction
Overview of different machine learning algorithms, their pros & cons, and use cases where different algorithms fitDescription
This course covers the basics of the following algorithms:
Linear Regression
Logistics Regression
Decision Trees
K-Means
PCA
Support Vector Machines
Random Forest
Apriori
Adaptive Boosting
Naïve Bayes
Neural Networks
For each of these, the course dives into the underlying concept, pros & cons, and the different practical business use cases where each of these algorithms work well. For those interested in getting their hands dirty, there are also sample implementations of the algorithms in Python
Requirements
- Requirements
- Although there is no background required as such for most of the course, however having a little understanding of statistics and programming fundamentals will be helpful.
- The course examples have source code in Python which are there for developers who want to try out different algorithms and not necessary to understand to complete the course.
TechTales Inc.
3.6 Raiting
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