Machine Learning using Python

Course Code (Credit):

CUTM1019(1-2-1)

Course Objectives:

  • Understand the meaning, purpose, scope, stages, applications, and effects of ML.
  • Explore important packages of python, such as numpy, scipy, OpenCV and scikit-learn.

Learning Outcomes:

  • Students will able to Create and incorporate ML solutions in their respective fields of study.

Course Syllabus:

Module I: Application and Environmental-setup (12 hrs)
  • Applications of Machine Learning In different fields (Medical science, Agriculture, Automobile, mining and many more).
  • Supervised vs Unsupervised Learning based on problem Definition.
  • Understanding the problem and its possible solutions using IRIS datasets.
  • Python libraries suitable for Machine Learning(numpy, scipy, scikit-learn, opencv)
  • Environmental setup and Installation of important libraries.
Module II: Regression (8 hrs)
  • Linear Regression
  • Non-linear Regression
  • Model Evaluation in Regression
  • Evaluation Metrics in Regression Models
  • Multiple Linear Regression
  • Feature Reduction using PCA
  • Implementation of regression model on IRIS datasets.
Module III: Classification (24 hrs)
  • Defining Classification Problem with IRIS datasets.
  • Mathematical formulation of K-Nearest Neighbour Algorithm for binary classification.
  • Implementation of K-Nearest Neighbour Algorithm using sci-kit learn.
  • Classification using Decision tree.
  • Construction of decision trees based on entropy.
  • Implementation of Decision Trees for Iris datasets.
  • Classification using Support Vector Machines.
  • SVM for Binary classification
  • Regulating different functional parameters of SVM using sci-kit learn.
  • SVM for multi class classification.
  • Implementation of SVM using Iris datasets.
  • Implementation of Model Evaluation Metrics using sci-kit learn and IRIS datasets.
Module IV: Unsupervised Learning (12 hrs)
  • Defining clustering and its application in ML .
  • Mathematical formulation of K-Means Clustering.
  • Defining K value and its importance in K-Means Clustering.
  • Finding appropriate K value using elbow technique for a particular problem.
  • Implementation of K-Means clustering for IRIS datasets

Projects:

  1. To be defined based on respective study area of student.

Text Books:

  1. Ethem Alpaydin, Introduction to Machine Learning, Second Edition, http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12012.

Web Resource:

https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51

Session Plan:

Session Topic Reference Link (if any)
Session 1 Applications of Machine Learning YouTube Video
Slideshare
Session 2, 3 Supervised vs Unsupervised Learning based on Problem Definition YouTube Video
Slideshare
Session 4, 5 Understanding the Problem and its Possible Solutions using IRIS Dataset YouTube Video
Session 6, 7 Mathematical Library in Python (NumPy) and its Functions YouTube Video
Slideshare
Session 8, 9 Science Library in Python (SciPy) and its Functions YouTube Video
Slideshare
Session 10, 11 ML Library in Python (scikit-learn) and its Functions YouTube Video
scikit-learn Tutorial
Session 12 Defining Student Specific Project -
Session 13 Linear Regression YouTube Video
Slideshare
Session 14 Non-linear Regression YouTube Video
Session 15 Model Evaluation YouTube Video
Session 16 Evaluation Metrics in Regression Models YouTube Video
Session 17, 18 Multiple Linear Regression YouTube Video
Session 19 Feature Reduction using PCA YouTube Video
Slideshare
Session 20 Implementation of Regression Model on IRIS Dataset YouTube Video
Session-21 Defining Classification Problem with IRIS datasets YouTube
Session-22,23 Create the train/test set using scikit-learn Scikit-learn Docs
Session-24,25 Confusion Matrix, Accuracy, Sensitivity, Specificity Scikit-learn Docs
Session-26 Mathematical formulation of K-NN for binary classification YouTube
Session-27,28 Implementation of K-NN using Scikit-learn YouTube
Session-29,30 Classification using Decision Tree YouTube
Session-31,32 Construction of Decision Trees based on Entropy YouTube
Session-33,34 Implementation of Decision Tree using Scikit-learn YouTube
Session-35,36 Classification using Support Vector Machines YouTube
Session-37,38 SVM for Binary Classification YouTube
Slideshare
Session-39,40 Regulating SVM parameters using Scikit-learn YouTube
Session-41,42 SVM for Multi-class Classification YouTube
Session-43,44 Implementation of Support Vector Machines YouTube
Session-45,46 Defining Clustering and its Application in ML YouTube
Session-47,48 Mathematical formulation of K-Means Clustering YouTube
Slideshare
Session-49,50 Defining K value in K-Means Clustering YouTube
Session-51,52 Implementation of K-Means Clustering in Scikit-learn YouTube
Slideshare
Session-53,54 Finding appropriate K using Elbow Technique YouTube
Session-55,56 Predicting Iris Flower Species with K-Means Medium