marilynyi/machine-learning-a-z

Guided projects in Jupyter Notebooks for the Machine Learning A-Z course on Udemy

Overview

This repository houses all guided projects I have written during the Machine Learning A-Z course on Udemy.

Course Details

The course aims to fulfill the following learning objectives:

• Master Machine Learning on Python & R
• Have a great intuition of many Machine Learning models
• Make accurate predictions
• Make powerful analysis
• Make robust Machine Learning models
• Create strong added value to your business
• Use Machine Learning for personal purpose
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning
• Handle advanced techniques like Dimensionality Reduction
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Sections

Jupyter Notebooks and Python files are personally written while .csv data files are provided by the course.

• Part 1 - Data Preprocessing
  • Data Preprocessing Tools
• Part 2 - Regression
  • Simple Linear Regression: Salary Prediction
  • Multiple Linear Regression: Startup Profit Prediction
  • Polynomial Regression: Position Salary Prediction
  • SVR: Position Salary Prediction
  • Decision Tree Regression: Position Salary Prediction
  • Random Forest Regression: Position Salary Prediction
• Part 3 - Classification
  • Logistic Regression: Car Purchase Prediction
  • K-NN: Car Purchase Prediction
  • SVM: Car Purchase Prediction
  • Kernel SVM: Car Purchase Prediction
  • Naive Bayes: Car Purchase Prediction
  • Decision Tree Classification: Car Purchase Prediction
  • Random Forest Classification: Car Purchase Prediction
• Part 4 - Clustering
  • K-Means: Mall Customer Patterns
  • Hierarchical Clustering: Mall Customer Patterns
• Part 5 - Association Rule Learning
  • Apriori: Market Basket Optimization
  • Eclat: Market Basket Optimization
• Part 6 - Reinforcement Learning
  • Upper Confidence Bound: Ad Clickthrough Rate Optimization
  • Thompson Sampling: Ad Clickthrough Rate Optimization
• Part 7 - Natural Language Processing
  • Bag-of-words model and NLP: Sentiment Analysis of Restaurant Reviews
• Part 8 - Deep Learning
  • Artificial Neural Networks: Bank Customer Churn Analysis
  • Convolutional Neural Networks: Dog/Cat Classifier
• Part 9 - Dimensionality Reduction
  • PCA: Wine Customer Segment Classification
  • LDA: Wine Customer Segment Classification
  • Kernel PCA: Wine Customer Segment Classification
• Part 10 - Model Selection & Boosting
  • k-fold Cross Validation: Car Purchase Prediction
  • Parameter Tuning and Grid Search: Car Purchase Prediction
  • XGBoost: Breast Cancer Detection
  • CatBoost: Breast Cancer Detection
  • All Classification Models: Breast Cancer Detection