Syllabus
Course Code: MS-20-42 Course Name: Machine Learning using Python |
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MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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1 | Python Programming: Strings - String slices, immutability, string functions and methods, string module; Lists, Tuples,
Dictionaries: Lists - Lists as arrays Traversing a List, list operations, list slices, list methods, Map, Filter and Reduce, list
loop, mutability, aliasing, cloning lists, list parameters; Dictionaries - operations and methods; advanced list processing -
list comprehension; Tuples - tuple assignment, tuple as return value. Files and Modules: Files and exception - text files, reading and writing files, format operator; command line arguments, errors and exceptions, handling exceptions, modules. |
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2 | Packages in Python: PANDAS, NUMPY, SCIKIT-LEARN, MATPLOTLIB. NumPy - Introduction,Ndarray Object ,Data types, Array Attributes,Array Creation Routines, Indexing & Slicing, Advanced Indexing,Broadcasting,Iterating Over Array,Array Manipulation,Binary Operators,String Functions,Mathematical Functions,Mathematical Functions,Arithmetic Operations,Statistical Functions,Linear Algebra,Matplotlib(Used for data visualization),Histogram Using Matplotlib. Pandas: Performing data cleaning and analysis, Loading data with Pandas (data manipulation and analysis), Working with and Saving data with Pandas. Using Scikit-Learn for Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, KNN, SVN, k Mean Clustering, Random Forest. |
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3 | Introduction to Machine Learning – Well defined learning problems, Designing a Learning System, Issues in Machine
Learning. The Concept Learning Task - General-to-specific ordering of hypotheses, Find-S, List then eliminate algorithm, Candidate elimination algorithm, Inductive bias Decision Tree Learning - Decision tree learning algorithm-Inductive bias- Issues in Decision tree learning. |
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4 | Bayesian Learning: Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief
networks, EM algorithm. Computational Learning Theory: Sample Complexity for Finite Hypothesis spaces, Sample Complexity for Infinite Hypothesis spaces, The Mistake Bound Model of Learning. Instance-Based Learning – k-Nearest Neighbour Learning, Locally Weighted Regression, Radial basis function networks, Case-based learning. |