Machine-Learning-A-Z

This is a practice Repository consisting of all the notebooks I have practiced for Machine Learning from basics to Advance

Name		Name	Last commit message	Last commit date
Latest commit History 40 Commits
1-FeatureEngineering		1-FeatureEngineering
10-Decision Tree		10-Decision Tree
11-Random Forest		11-Random Forest
12-Adaboost		12-Adaboost
13-Gradient Boosting		13-Gradient Boosting
14-XgBoost		14-XgBoost
15-Unsupervised Machine Learning		15-Unsupervised Machine Learning
16-PCA		16-PCA
17-K Means Clutering		17-K Means Clutering
18-Hierarichal Clustering		18-Hierarichal Clustering
19-DBSCAN Clustering		19-DBSCAN Clustering
2-Introduction to Machine Learning		2-Introduction to Machine Learning
20-Silhoute Clustering		20-Silhoute Clustering
21-Anomaly Detection		21-Anomaly Detection
22-Dockers		22-Dockers
3-Exploratory Data Analysis		3-Exploratory Data Analysis
4-Linear-Regression		4-Linear-Regression
5-Ridge Lasso And Elasticnet Regression		5-Ridge Lasso And Elasticnet Regression
6-Logistic Regression		6-Logistic Regression
7-Support Vector Machine		7-Support Vector Machine
8-Naive Bayes		8-Naive Bayes
9-K Nearest Neighbors		9-K Nearest Neighbors
Final Assignment.ipynb		Final Assignment.ipynb
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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