Machine Learning (CO402)

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• Select section Announcements

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  Announcements

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  Dear Learners,
  Wel-Come to the Course on Machine Learning.It has Following Course Objectives & Course Outcomes:

  Prerequisites: Data Mining, Discrete Mathematics, Database

  Course Objectives:

  1.                  To understand the need for machine learning for various problem solving

  2.                  To understand the nature of the problem and apply machine learning algorithms.

  3.                   To study the various supervised, semi-supervised and unsupervised learning algorithms in

                 machine learning

  4.                  To understand the latest trends in machine learning

  5.                  To design appropriate machine learning algorithms for problem solving

  Course Outcomes (COs): At the end of this course, students will be able to,

  CO No.	Statement of Course Outcome	Bloom’s Taxonomy
  		Level	Descriptor
  CO1	Understand different learning based applications.	2	Understand
  CO2	Apply different pre-processing methods to prepare training data set for machine learning	3	Apply
  CO3	Apply the  Regression Techniques to various problems	3	Apply
  CO4	Apply the  Bayesian algorithm to various problems	3	Apply
  CO5	Apply  the classification &  ensemble techniques.	3	Apply
  CO6	Ability to apply Clustering techniques for data.	3	Apply

  Text Books:

   

  Sr. No.	Authors	Title	Edition	Year	Publication
  1	Giuseppe Bonaccorso	Machine Learning Algorithms			Packt Publishing Limited
  2	Tom M. Mitchell	Machine Learning		2013	McGraw-Hill Education (India) Private Limited,
  3	Josh Patterson, Adam Gibson	Deep Learning: A Practitioners Approach		2017	O‟REILLY

  References Books:

  Sr. No.	Authors	Title	Edition	Year	Publication
  1	Ethem Alpaydin	Introduction to Machine Learning			The MIT Press
  2	Stephen Marsland	Machine Learning: An Algorithmic Perspective			CRC Press
  3	Ethem Alpaydin	Introduction to Machine Learning	2nd		PHI
  4	Peter Flach	Machine Learning: The Art and Science of Algorithms that Make Sense of Data			Cambridge University Press
  5	Nikhil Buduma	Fundamentals of Deep Learning			O‟REILLY publication

   

  E-Resources:

   

  Sr. No.	Link
  1	https://nptel.ac.in/courses/106105152
  2	https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/

   

  Thanks & Regards,
  Dr. T.Bhaskar
  Associate Professor(Computer Engg),Sanjivani College of Engineering,Kopragon
  Google-Site: https://sites.google.com/view/bhaskart/ug-notes/datamining-warehousing
  Moodle-Site: https://proftbhaskar.gnomio.com/course/view.php?id=3 (Log in as Guest)
  DMW YouTube Playlist: https://tinyurl.com/DMW-Bhaskar

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    Announcements Forum
• Select section INTRODUCTION TO MACHINE LEARNING

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  INTRODUCTION TO MACHINE LEARNING

  Classic and adaptive machines, Machine learning matters, beyond machine learning-deep learning and bio inspired adaptive systems, Machine learning and Big data. Important Elements of Machine Learning- Data formats, Learn ability, Statistical learning approaches, Elements of information theory.

  • Select activity ML UNIT-I Notes

    

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    Introduction to Course & COs POs & CIA Rubrics

    This Link Has ppt of Unit-I :Introduction to Machine learning

    ML Videos Playlist: https://tinyurl.com/ML-DrBhaskarT

• Select section FEATURE SELECTION

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  FEATURE SELECTION

  Scikit- learn  Dataset, Creating training and test sets, managing categorical data,. Managing missing features, Data scaling and normalization, Feature selection and Filtering, Principle Component Analysis(PCA)-non negative matrix factorization, Sparse PCA, Kernel PCA. Atom Extraction and Dictionary Learning

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    This Link has ppt of Unit-II:Feature Selection

    ML Videos Playlist: https://tinyurl.com/ML-DrBhaskarT

• Select section REGRESSION TECHNIQUES

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  REGRESSION TECHNIQUES

  Linear regression- Linear models, A bi-dimensional example, Linear Regression and higher dimensionality, Ridge, Lasso and Elastic Net, Robust regression with random sample consensus, Polynomial regression, Isotonic regression,

  Logistic regression-Linear classification, Logistic regression, Implementation and Optimizations, Stochastic gradient descendent algorithms, Finding the optimal hyper-parameters through grid search, Classification metric, ROC Curve.

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    This Link has ppt of Unit-III:Regression Techniques

    ML Videos Playlist: https://tinyurl.com/ML-DrBhaskarT

• Select section BAYESIAN AND SVM TECHNIQUES

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  BAYESIAN AND SVM TECHNIQUES

  Bayes‟ Theorom, Naïve Bayes‟ Classifiers, Naïve Bayes in Scikit- learn- Bernoulli Naïve Bayes,Multinomial Naïve Bayes, and Gaussian Naïve Bayes.

  Support Vector Machine(SVM)- Linear Support Vector Machines, Scikit- learn implementation Linear Classification, Kernel based classification, Non- linear Examples. Controlled Support Vector Machines, Support Vector Regression.

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    This Link has ppt of Unit-IV:Naïve bayes & SVM

    ML Videos Playlist: https://tinyurl.com/ML-DrBhaskarT

• Select section CLASSIFICATION AND ENSEMBLE LEARNING

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  CLASSIFICATION AND ENSEMBLE LEARNING

  Decision Trees- Impurity measures, Feature Importance. Decision Tree Classification with Scikitlearn, Ensemble Learning-Random Forest, AdaBoost, Gradient Tree Boosting, Voting Classifier. Introduction to Meta Classifier: Concepts of Weak and eager learner, Ensemble methods, Bagging, Boosting, Random Forests.K-NN Algorithms

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    This Link has ppt of Unit-V:Classification & Ensemble Learning

    ML Videos Playlist: https://tinyurl.com/ML-DrBhaskarT

• Select section CLUSTERING TECHNIQUES

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  CLUSTERING TECHNIQUES

  Clustering Fundamentals- Basics, K-means: Finding optimal number of clusters, DBSCAN, Spectral Clustering. Evaluation methods based on Ground Truth- Homogeneity, Completeness, Adjusted Rand Index. Hierarchical Clustering, Expectation maximization clustering, Agglomerative Clustering- Dendrograms, Agglomerative clustering in Scikit- learn, Connectivity Constraints

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    This Link has ppt of Unit-VI:Clustering

    ML Videos Playlist: https://tinyurl.com/ML-DrBhaskarT

• Select section Rubrics for Assessment of ML Lab(CO407)

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  Rubrics for Assessment of ML Lab(CO407)

  Evaluation of practical assignment is based on the following criteria’s. Each Assignment is evaluated out of 10 Marks

  Criteria	Excellent	Good	Average	Poor
  Write Ups (2)	Timely submission within deadline in all respects.  (2)	Timely submission but needs some improvement. (1)	Submission with maximum one-week delay.  (1)	Delayed in submission or found copied. (0)
  Understanding (4)	Understand all the concepts, algorithm, or logic. (4)	Understand the concepts, algorithm, or logic but need improvement.  (3)	Limited understanding  of the concepts or algorithm or logic but need more improvement  (2)	Failed to understand the concepts, algorithm, or logic.  (0)
  Performance (4)	Implemented the concepts, algorithm, or logic with correct expected output considering test cases. ( 4)	Implemented the concepts, algorithm, or logic with expected results. (3)	Implemented the concepts, algorithm, or logic with partial results and needs improvement. (2-1)	Not implemented and no output. (0)
• Select section Machine Learning Lab Software

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  Machine Learning Lab Software

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    Machine Learning Lab Software URL
• Select section Assignment on Linear Regression

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  Assignment on Linear Regression

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    Implemenation Details:

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT

• Select section Assignment on PCA

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  Assignment on PCA

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    Implementation of PCA URL

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    Implemenation Details:

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT

• Select section Assignment on Decision Tree

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  Assignment on Decision Tree

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    Implementation of Decision Tree URL

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    Implementation of Decision Tree:

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT

• Select section Assignment on Naive Bayes

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  Assignment on Naive Bayes

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    Implementation of Naive Bayes

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT

• Select section Assignment on SVM

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  Assignment on SVM

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    Implementation of SVM URL

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    Implementation of SVM:

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT

• Select section Assignment on KMeans

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  Assignment on KMeans

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    Implementation of KMeans

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT

• Select section Assignment on Gradient Boost Classifier

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  Assignment on Gradient Boost Classifier

  • Select activity Implementation of Gradient Boost Classifier

    

    Implementation of Gradient Boost Classifier URL

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    Implementation of Gradient Boost Classifier

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT

• Select section ML Sample Mini Projects

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  ML Sample Mini Projects

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    ML Sample Mini Projects

• Select section ML Lab Manual

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  ML Lab Manual

  • Select activity ML Lab Manual

    

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    Machine Learning Lab Manual Link:

    Machine Learning Virtual Lab:https://tinyurl.com/MLLab-DrBhaskarT