Machine Learning Lab PCCSL508 Semester 5 KTU CS 2024 Scheme - Dr Binu V P

About Me - Dr Binu V P Syllabus Machine Learning Lab PCCSL508 KTU 2024 Scheme Learn Python well before You start(focus on numpy,pandas,matplotlib )- refer blog Build a strong understanding of the theory before moving on to programming Recommended Tools and Setup for Lab Lab Assignments Lab Assignment-1 Lab Assignment-2 Lab Assignment-3 Lab Assignment-4 Lab Assignment-5 Lab Assignment-6 Lab Assignment-7 Lab Assignment-8 Lab Assignment-9 Lab Assignment-10 Experiments Regression  Explore Californoa Housing Dataset(learn pandas,scikit-learn and matplotlib) Simple Linear Regression using Sample Data ( Single variable, Toy Example) Simpe Linear Regression using Sample Data ( using scikit-learn) Simple Linear regression using California Housing Dataset(CSV input) Simple Linear Regression Using California Housing Dataset (using scikit-learn) Simple Linear Regression using Gradient Descent ( Single variable, Toy Example) Simple Linear regression using Gradient Descent on California Housing ...

  Assignment-5 week 1 Aug Learning Objective: Compare MLE and MAP 1.Problem Statement An instructor conducted an online quiz for a small group of students. Each student's result is recorded as: 1 → Pass 0 → Fail The observed quiz results are 1, 1, 0, 1, 1, 0, 1, 0, 1, 1 Assume that the quiz outcomes follow a Bernoulli distribution , where θ = Probability of passing the quiz The instructor believes that, based on previous years, most students are likely to pass. This prior belief is represented by a Beta distribution with parameters α = 5 β = 2 Using the given data, Part A Estimate the probability of passing using Maximum Likelihood Estimation (MLE) Part B Estimate the probability of passing using Maximum A Posteriori (MAP) Part C Compare the MLE and MAP estimates. Part D Plot the Prior Distribution Likelihood Function Posterior Distribution on the same graph. Part E Comment on the following: Which estimate is affected by prior ...

  California Housing Dataset 🔹 Dataset Characteristics Feature Description Number of Instances                20,640 Number of Attributes                8 numerical predictive attributes + 1 target + 1 categorical Target Variable                Median House Value 🔹 Context This dataset is used in the book “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron . It is widely used as an introductory dataset for machine learning because: Requires basic preprocessing Has clear and interpretable features Is moderate in size (not too small, not too large) 🔹 Dataset Description The dataset contains information about housing in California districts based on the 1990 U.S. Census . Each row represents a census block group , which is: The smallest geographical unit used by the census Typically contains ...

  Assignment-9- week 3 September Learning Objective: Learn SVM 1.Implementation of Linear SVM from Scratch Problem Statement A college wants to classify students into two categories— Pass and Fail —based on their performance in two internal assessment tests. Each student is represented using two features: Internal Test 1 Score Internal Test 2 Score The target class is: −1 → Fail +1 → Pass The following training dataset is provided. Student      Test 1      Test 2      Class S1 2 3      −1 S2 3 2      −1 S3 3 4      −1 S4 4 3      −1 S5 6 7      +1 S6 7 6      +1 S7 7 8           +1 S8 8 7      +1 Task Implement a Linear Support Vector Machine (SVM) classifier from scratch using Python and NumPy , without using Scikit-learn's SVC or any other built-in SVM implementation. Your pro...