Python Machine Learning with SciKit-Learn

SkillsFuture Credit Approved Course

Introduction

Learn simple and efficient tools for data mining and data analysis, using Python’s Machine Learning framework Scikit-learn. Master concepts such as regression, clustering, dimensionality reduction, preprocessing, decision trees and neural networks and more.

Course Outline

Course Outline

  1. Getting Started on Scikit-Learn
    • What is Machine Learning
    • Machine Learning Steps
    • What is Scikit-Learn
    • Installing Scikit-Learn
  2. Datasets
    • What is Dataset 
    • Iris Dataset
    • Handwritten Digits Dataset
    • Boston Housing Price Dataset
    • Splitting Datasets for Training/Testing
  3. Supervised Learning
    • What is Supervised Learning
    • Key Classifiers Algorithms – KNN, SVM, GNB, DT, Ensemble
    • Performance Metric and Errors
    • Model Persistence
    • Regression
  4. Unsupervised Learning
    • What is Unsupervised Learning
    • Key Clustering Algorithms – K-Means, Mean Shift, Agglomerative
    • Dimensionality Reduction – PCA
  5. Neural Network
    • What is Neural Network?
    • Multi Layer Perceptron Classifier
    • Hidden Layers
    • Activation Function
    • Solver
    • Learning Rate
    • Momentum