ML Problem Solving
Machine learning algorithms implementation from scratch using python
Table of Content
1. Bag of Words (BOW):
Coverts a collection of documents to a matrix, with each document being a row and each word(token) being the column, and the corresponding (row,column) values being the frequency of occurrance of each word or token in that document.
2. Mini-Batch Gradient Descent Implementation for Linear Regression:
An optimization algorithm used for computing the model parameters for algorithms like linear regression, logistic regression, neural networks.
3. Perceptron Algorithm:
Binary classification machine learning algorithm that is the simplest type of neural network model.
4. Perceptron Algorithm as Logical Operations:
Use the perceptroin algorithms as AND,OR and Not logical operator.
5. Naive Bayes:
Bayes Theorem is one of the earliest probabilistic inference algorithms. It was developed by Reverend Bayes (which he used to try and infer the existence of God.
6. Metrics:
Classifiction metrics implementation:
- Accuracy
- Precision
- Recall
- F1 score
- AUC and ROC curve
Regression metrics implementation:
- R-squared
- Mean absolute error
- Mean square error