Machine Learning using Python
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Machine Learning using Python
Teacher
Dr. Sujata Chakravarty
Category
Core Courses
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Course Name : Machine Learning using Python
Code(Credit) : CUTM1019(1-2-1)
Course Objectives
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Understand the meaning, purpose, scope, stages, applications, and effects of ML.
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Explore important packages of python, such as numpy, scipy, OpenCV and scikit-learn.
Course Outcomes
|
COs |
Course outcomes |
Mapping COs with POs (High-3, Medium-2, Low- 1) |
|
CO1 |
Able to gain knowledge on ML solutions in their respective fields of study |
PO1 (3) |
|
CO2 |
Able to analyze several problems and apply ML techniques to solve it |
PO2(3) |
|
CO3 |
Ability to design prediction and classification models |
PO3 (3) |
Course outcome
CO1: Able to gain knowledge on ML solutions in their respective fields of study: PO1
CO2: Able to analyze several problems and apply ML techniques to solve it: PO2
CO3: Ability to design prediction and classification models: PO3
PO1: Engineering knowledge: Apply knowledge of mathematics, science, Engineering fundamentals, and electronics engineering to the solution of engineering problems.
PO2: Problem analysis: Identify, formulate, review literature and analyze Computer Science and Engineering problems to design, conduct experiments, analyze data and interpret data.
PO3: Design /development of solutions: Design solution for Computer Science and Engineering problems and design system component of processes that meet the desired needs with appropriate consideration for the public health and safety, and the cultural, societal and the environmental considerations.
Course Syllabus
Module 1 – Application and Environmental-setup (12 hrs)
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Applications of Machine Learning In different fields (Medical science, Agriculture, Automobile, mining and many more).
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Supervised vs Unsupervised Learning based on problem Definition.
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Understanding the problem and its possible solutions using IRIS datasets.
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Python libraries suitable for Machine Learning(numpy, scipy, scikit-learn, opencv)
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Environmental setup and Installation of important libraries.
Module 2 - Regression (8 hrs)
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Linear Regression
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Non-linear Regression
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Model Evaluation in Regression
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Evaluation Metrics in Regression Models
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Multiple Linear Regression
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Feature Reduction using PCA
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Implementation of regression model on IRIS datasets.
Module 3 - Classification (24 hrs)
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Defining Classification Problem with IRIS datasets.
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Mathematical formulation of K-Nearest Neighbour Algorithm for binary classification.
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Implementation of K-Nearest Neighbour Algorithm using sci-kit learn.
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Classification using Decision tree.
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Construction of decision trees based on entropy.
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Implementation of Decision Trees for Iris datasets .
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Classification using Support Vector Machines.
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SVM for Binary classification
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Regulating different functional parameters of SVM using sci-kit learn.
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SVM for multi class classification.
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Implementation of SVM using Iris datasets .
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Implementation of Model Evaluation Metrics using sci-kit learn and IRIS datasets.
Module 4 - Unsupervised Learning (12 hrs)
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Defining clustering and its application in ML .
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Mathematical formulation of K-Means Clustering.
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Defining K value and its importance in K-Means Clustering.
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Finding appropriate K value using elbow technique for a particular problem.
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Implementation of K-Means clustering for IRIS datasets
Projects
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To be defined based on respective study area of student.
References:
Text Book:
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Ethem Alpaydin, Introduction to Machine Learning, Second Edition, http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12012.
Web Resource:
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https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51
practical
Practical 1: Introduction to Numpy module
Practical 2: Introduction to Pandas module
Practical 3: Simple Linear regression: Predict the sepal length (cm) of the iris flowers
Practical 4: Implementation of Non Linear regression using IRIS
Practical 5: Implementation of Multiple linear regression using IRIS
Practical 6: Comparison between Linear and Non linear regression
Practical 7: Implementation of PCA for feature reduction
Practical 8: Implementation of k-Nearest Neighbour algorithm using IRIS
Practical 9: Implementation of Tree construction using Decision tree Classifier using IRIS
Practical 10: Implementation of Finding Accuracy using Decision tree using IRIS
Practical 11: Implementation of SVM Classification using Binary class
Practical 12: Implementation of SVM Classification using multiclass using IRIS
Practical 13: Implementation of Evaluation metrics
Practical 14: Implementation of KMeans
Session Plan
Session-1
Applications of Machine Learning
https://www.youtube.com/watch?v=ahRcGObyEZo
https://www.slideshare.net/makabee/applications-of-machine-learning
Session-2,3
Supervised vs Unsupervised Learning based on problem Definition
Session-4,5
Understanding the problem and its possible solutions using IRIS datasets.
Session-6,7
Mathmatical library in Python numpy and its functions
Session-8,9
Science library in Python scipy and its functions
session-10,11
ML library in Python scikit-learn and its functions.
https://www.youtube.com/watch?v=bwZ3Qiuj3i8
https://scikit-learn.org/stable/tutorial/basic/tutorial.html
Session-12
Defining student specific Project
Session-20
Implementation of regression model on IRIS datasets.
Session-21
Defining Classification Problem with IRIS datasets.
Session-22,23
Create the train/test set using scikit-learn using scikit-learn
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
Session-24,25
Confussion Matrix, Accuraccy, Sensitivity, specificity
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
Session-26
Mathematical formulation of K-Nearest Neighbour Algorithm for binary classification.
Session-27,28
Implementation of K-Nearest Neighbour Algorithm using sci-kit learn.
Session-31,32
Construction of decision trees based on entropy.
Session-33,34
Implementation of Decision Tree using sci-kit learn
Session-35,36
Classification using Support Vector Machines.
Session-39,40
Regulating different functional parameters of SVM using sci-kit learn.
Session-43,44
Implementation of Support Vector Machines.
Session-45,46
Defining clustering and its application in ML
Session-47,48
Mathematical formulation of K-Means Clustering.
Session-49,50
Defining K value and its importance in K-Means Clustering.
Session-51,52
Implementation of K-Means Clustering in Scikit-learn
Session-53,54
Finding appropriate K value using elbow technique for a particular problem.
Session-55,56
predicting iris flower species with k-means clustering.
https://medium.com/@belen.sanchez27/predicting-iris-flower-species-with-k-means-clustering-in-python-f6e46806aaee
Our Main Teachers
Dr. Sujata Chakravarty is a Senior Member of IEEE. Her research area includes multidisciplinary fields like Application of Computational Intelligence and Evolutionary Computing Techniques in the field of Financial Engineering, Bio-medical data classification, Smart Agriculture, Intrusion Detection System in Computer-Network, Analysis and prediction of different financial time series data. She is a reviewer of many […]
G RAMA DEVI ,MCA,M.TECH(CSE),(Ph.d) working as Assistant professor, Department of CSE, Centurion University of Technology and Management, Andhra Pradesh. Interested to work on Data Structures, Python, Machine Learning, Database Management Systems and Web Development, Problem Solving Methodologies. Programming Skill: Data structures, C programming, python, Web development (Html, CSS,JS), Database Management Systems.
