FML

Fundamentals of Machine Learning Course for II B.Tech. CSE

Cycle - 0 (Prerequisite Tasks)

practice the basic librairies required to do ML
- NumPy
- Pandas
- Matplotlib, Seaborn
Perform Exploratory Data Analysis on
- Iris Dataset
- Titanic Dataset
- Boston House prices dataset

Build a simple linear regression model and perform predictions on the test dataset. Consider a company’s data, where there is the amount spent on different types of advertisements and its subsequent sales. Dataset
Build a Multiple Linear Regression model on a dataset (eg: 50_startups) Dataset
Apply L1 regularization on the created simple and multiple linear regression. What is your observation?
Apply L2 regularization on the created simple and multiple linear regression. What is your observation?

Fill the following tables with your results

Name		Name	Last commit message	Last commit date
Latest commit History 32 Commits
Datasets		Datasets
NLP		NLP
NVIDIA		NVIDIA
images		images
ARIMA.ipynb		ARIMA.ipynb
EDA_on_boston_dataset.ipynb		EDA_on_boston_dataset.ipynb
FML Record.pdf		FML Record.pdf
Feature selection using lasso.ipynb		Feature selection using lasso.ipynb
KMeans_Clustering.ipynb		KMeans_Clustering.ipynb
Lasso regression-Boston.ipynb		Lasso regression-Boston.ipynb
Logistic_Regression1ipynb.ipynb		Logistic_Regression1ipynb.ipynb
Logistic_Regression_Handwriting_recognition.ipynb		Logistic_Regression_Handwriting_recognition.ipynb
Model_Ravana_on_custom_data.ipynb		Model_Ravana_on_custom_data.ipynb
Naive_Bayes.ipynb		Naive_Bayes.ipynb
PCA.ipynb		PCA.ipynb
README.md		README.md
Ridge regression-Boston.ipynb		Ridge regression-Boston.ipynb
Univariate inear regression-Boston.ipynb		Univariate inear regression-Boston.ipynb
hierarchical_clustering.ipynb		hierarchical_clustering.ipynb
knn_on_iris.ipynb		knn_on_iris.ipynb
multivariate Linear regression-Boston.ipynb		multivariate Linear regression-Boston.ipynb
multivariate-train-test-split-Boston.ipynb		multivariate-train-test-split-Boston.ipynb