Python Scikit-Learn for Machine Learning

Learn different concepts and techniques in machine learning using Scikit-learn library in Python.

Course overview

This course is for people who have basic knowledge of python and want to get started with Machine Learning using Scikit-learn, which is an open source library for Machine Learning Algorithms.

In the first part of this course we will get started by setting up our computer for python and scikit-learn, get an overview of; Machine Learning; different datasets used in the course and Skeleton for building a machine learning model.

In the second part of this course we will learn about implementing different algorithms from Scikit-learn in python and improve the accuracy of our model by doing hyper-parameter tuning.

In the final article we will build a Machine Learning model ground up with categorical data, during the course we will learn about, label filter, feature mapping, imputing, standardization and dimension reduction technique.

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Course content

1. Setting up python and Scikit learn in our computer + Course Code.


2. An overview of Machine Learning.


3. Datasets and importing datasets.


4. Typical Skeleton of Machine Learning model.


5. Perceptron from Scikit-learn on Iris dataset.


6. Logistic Regression from Scikit-learn on Iris dataset


7. Support Vector Machine, linear and kernal, from Scikit-learn on Iris dataset.


8. Decision Tree Classifier from Scikit-learn on Iris dataset


9. Random Forest classifier from Scikit-learn on Iris dataset


10. Data Shaping and best practices