Course Information
Course Code
BCSL606
Credits
01
Total Hours
40 hours
Examination
Practicle (3 hours)
Course Objectives
- To become familiar with data and visualize univariate, bivariate, and multivariate data using statistical techniques and dimensionality reduction.
- To understand various machine learning algorithms such as similarity-based learning, regression, decision trees, and clustering.
- To familiarize with learning theories, probability-based models and developing.
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Course Modules
Full Machine Learning Lab Manual
Experiment 1: Histograms and Boxplots Analysis (California Housing)
Topics: Histograms and Boxplots Analysis (California Housing).
Experiment 2: Correlation Matrix and Pair Plot (California Housing)
Topics: Correlation Matrix and Pair Plot (California Housing).
Experiment 3: PCA Dimensionality Reduction (Iris Dataset)
Topics: PCA Dimensionality Reduction (Iris Dataset).
Experiment 4: Find-S Algorithm for Hypothesis Generation
Topics: Find-S Algorithm for Hypothesis Generation.
Experiment 5: k-Nearest Neighbors Classification (Generated Data)
Topics: k-Nearest Neighbors Classification (Generated Data).
Experiment 6: Locally Weighted Regression Algorithm
Topics: Locally Weighted Regression Algorithm.
Experiment 7: Linear and Polynomial Regression (Boston Housing & Auto MPG)
Topics: Linear and Polynomial Regression (Boston Housing & Auto MPG).
Experiment 8: Decision Tree Classifier (Breast Cancer Dataset)
Topics: Decision Tree Classifier (Breast Cancer Dataset).
Experiment 09: Naive Bayes Classifier (Olivetti Face Dataset)
Topics: Naive Bayes Classifier (Olivetti Face Dataset).
Experiment 10: K-Means Clustering (Breast Cancer Dataset)
Topics: K-Means Clustering (Breast Cancer Dataset).