R23
ECE · 3-2
✓ VERIFIED VS. PUBLISHED SYLLABUS 2026-07-11
Machine Learning Lab
JNTUK R23 · ECE · semester 3-2 · syllabus
This JNTUK R23 skill-enhancement course is listed for Electronics and Communication Engineering (ECE). It is listed in semester 3-2. It carries 2 credits. Its published Experiments section lists 15 activities or topic groups.
Verified vs. published syllabus
Checked 11 Jul 2026
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Published evidence
✓ Verified
Checked 11 Jul 2026
Record 1 published section
Verification scope Subject name, credits, unit-wise syllabus, and course outcomes are sourced directly from JNTUK's own official R23 course structure and syllabus document, not an autonomous college's adaptation.
Published practical or activity coverage: Experiments. Tick it off as you cover it — your progress stays on this device.
Unit-wise syllabus
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UNIT 01 — Experiments
Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples, reading the training data from a .CSV file · For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples · Write a program to demonstrate the working of the decision tree based ID3 algorithm; use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample · Solve real-world problems using Linear Regression, Logistic Regression and a Binary Classifier · Develop a program for Bias, Variance, removing duplicates, and Cross Validation · Write a program to implement Categorical Encoding and One-hot Encoding · Build an Artificial Neural Network by implementing the Back Propagation algorithm and test it using appropriate data sets · Write a program to implement the k-Nearest Neighbor algorithm to classify the iris data set, printing both correct and wrong predictions · Implement the non-parametric Locally Weighted Regression algorithm to fit data points; select an appropriate data set and draw graphs · Use the naive Bayesian Classifier model to classify a set of documents; calculate accuracy, precision and recall for the data set · Apply the EM algorithm to cluster a Heart Disease Data Set and compare with K-Means clustering on the same data set, commenting on the quality of clustering · Exploratory Data Analysis for classification using Pandas or Matplotlib · Write a Python program to construct a Bayesian network considering medical data, and use it to demonstrate diagnosis of heart patients using a standard Heart Disease Data Set · Write a program to implement Support Vector Machines and Principal Component Analysis · Write a program to implement Principal Component Analysis