I am teaching a Learning ML workshop for some people @ Belong.co. Creating this repo to organise the course material.

I have implemented some basic machine learning and neural network Models from scratch in python and compared it with sklearn's implementation:

• Simple Linear Regression Notebook

• Simple Linear Regression with Stochastic Gradient Descent Notebook

• Multiple Linear Regression Notebook

• Multiple Linear Regression with Stochastic Gradient Descent Notebook

• Logistic Regression Notebook

• Logistic Regression with Stochastic Gradient Descent Notebook

• Naive Bayes Classifier Bernoulli Notebook

• Naive Bayes Classifier Multinomial Notebook

• Hidden Markov model Notebook

• Fizz Buzz in keras with Relu Notebook

• Fizz Buzz in keras with LeakyReLU and Dropout Notebook

• Fizz Buzz in Tensorflow Notebook

• Text classficiation model in keras Notebook

Working on more models to be put here, Reinforcement learning and Encoder decoder models.

• AUC and AUC_ROC Notebook

• Covariance and Correlation Notebook

• Categorical Cross Entropy and LogLoss Notebook