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.


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