This Repository contains Datasets and Jupyter notebooks regarding all my tutorials on Machine Learning.
Topics covered:
REGRESSION:
- Simple Linear Regression
- Decision Tree Regression
- Random Forest Regression
CLASSIFICATION:
- Logistic Regression
- K-Nearest Neighbor
- Support Vector Machines
- Naive Bayes
- Decision Tree
- Random Forest
CLUSTERING:
- K-Means
DEEP LEARNING
- Artificial Neural Networks
- Perceptron Model
- Implementing different logic gates using Perceptron
ML BASED PROJECT
- Spam Classification using Naive Bayes Algorithm