Further-Reading.md

Links for Further Reading

Basic Algorithms

• https://github.com/Avik-Jain/100-Days-Of-ML-Code

Simple theories

• https://towardsdatascience.com/a-tour-of-the-top-10-algorithms-for-machinelearning-newbies-dde4edffae11

Logistic Regression

• https://towardsdatascience.com/building-a-logistic-regression-in-python-stepby-step-becd4d56c9c8

Genetic Algorithm

https://towardsdatascience.com/evolution-of-a-salesman-a-complete-geneticalgorithm-tutorial-for-python-6fe5d2b3ca35

Reinforcement Learning

• https://medium.com/emergent-future/simple-reinforcement-learning-withtensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
• https://medium.freecodecamp.org/an-introduction-to-reinforcement-learning4339519de419
• https://towardsdatascience.com/introduction-to-various-reinforcementlearning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287

Naïve Bayes Algorithm

• https://www.hackerearth.com/blog/machine-learning/introduction-naivebayes-algorithm-codes-python-r/
• https://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
• https://appliedmachinelearning.blog/2017/05/23/understanding-naive-bayesclassifier-from-scratch-python-code/

Decision Tree

• https://heartbeat.fritz.ai/introduction-to-decision-tree-learning-cd604f85e236
• https://medium.com/open-machine-learning-course/open-machine-learningcourse-topic-3-classification-decision-trees-and-k-nearest-neighbors8613c6b6d2cd
• https://www.kaggle.com/creepykoala/study-of-tree-and-forestalgorithms/notebook