/Machine-Learning-A-Z

This is the code which I made during the Machine Learning Course on Udemy

Primary LanguageJupyter Notebook

Udemy - Machine Learning A-Z

By Parth Mistry

From: Kirill Eremenko, Hadelin de Ponteves, and SuperDataScience Team

Code and Resources Used

Python Version: 3.7
Packages: pandas, numpy, sklearn, matplotlib, seaborn, selenium, flask, json, pickle
For Web Framework Requirements: pip install -r requirements.txt
Scraper Github: https://github.com/arapfaik/scraping-glassdoor-selenium
Scraper Article: https://towardsdatascience.com/selenium-tutorial-scraping-glassdoor-com-in-10-minutes-3d0915c6d905
Flask Productionization: https://towardsdatascience.com/productionize-a-machine-learning-model-with-flask-and-heroku-8201260503d2

Content

Part 1:Data Preprocessing

Part 2:Regression

•Simple Linear Regression
•Multiple Linear Regression
•Polynomial Regression
•Support Vector Regression(SVR)
•Decision Tree Regression
•Random Forest Regression
•Evaluating Regression Models Performance

Part 3:Classification

•Logistic Regression
•K-Nearest Neighbors (K-NN)
•Support Vector Machine (SVM)
•Kernel SVM
•Naive Bayes
•Decision Tree Classification
•Random Forest Classification
•Evaluating Classification Models Performance

Part 4:Clustering

•K-Means Clustering
•Hierarchical Clustering

Part 5:Association Rule Learning

•Apriori

Part 6:Reinforcement Learning

•Upper Confidence Bound (UCB)
•Thompson Sampling

Part 7:Natural Language Processing

Part 8:Deep Learning

•Artificial Neural Networks
•Convolutional Neural Networks
•Transfer Learning

Part 9:Dimensionality Reduction

•Principal Component Analysis (PCA)
•Linear Discriminant Analysis (LDA)
•Kernel PCA

Part 10:Model Selection & Boosting

•Model Selection
•XGBoost