Machine_Learning_AZ - Udemy Certification

The following are some of the notes and projects that I worked on in this certification and most of the code that I share in GitHub can be used as templates or reference when working in tasks that involve machine learning.

Table of contents

No	Folder name	Content description
1	Data Preprocessing	Files in Python and R that handles missing data, transforms categorical data into numerical data, applies simple feature scaling techniques and splits data into training and test sets.
2	Regression	Files in Python and R that shows how to perform the different types of regression techniques in ML: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression.
3	Classification	Files in Python and R that show how to perform the different types of classification techniques in ML: Logistic Regression, KNN-algorithm, Support Vector Machines, Kernel SVM (non linear problems), Naive Bayes, Decision Tree Classification, Random Forest Classification.
4	Clustering	Files in Python and R that show how to perform the different types of clustering techniques in ML: hierarchical clustering and k_means.
5	Association Rule	File in Python and 2 files R that show how to perform the Eclat and Apriori techniques.
6	Reinforcement Learning	Files in Python and R that show how to perform the different types of reinforcement learning techniques in ML: Thompson learning and Upper Confidence Bound.
7	Natural Language Processing	Files in Python and R that guide you to perform basic natural language processing techniques.
8	Deep Learning	Files in Python and R that show how to create an Artificial Neural Network and a file in Python that guide you to create a convolutionary neural network. The files for the convolutionary neural network were not uploaded because it weights too much.
9	Dimensionality Reduction	Files in Python and R that show how to apply the Principal Component Analysis and Linear Discriminant Analysis techniques.
10	Model Selection and Boosting	Files in Python that show how to apply the tuning techniques of grid search and k_fold_cross_validation to find the best parameters in regression or classification methods.