ashoknp-git/machine_learning_a-z_all_codes_and_templates

All codes, both created and optimized for best results from the SuperDataScience Course

Machine Learning A-Z by SuperDataScience Team

This repository contains all the codes and datasets for Machine Learning A-Z Course, by Kirill Eremenko and Hadelin de Ponteves.

Verdict for using R & Python for the tasks

Objective	R	Python	Verdict
Data Preprocessing	Requires fewer packages	Easy to impute missing values and splitting into training and test sets, has LabelEncoder and OneHotEncoder	Python
Linear Regression	Can be done in a single line	Requires multiple lines to code	R
Polynomial Regression	Unless you use the poly function the process is cumbersome	Easier to define degrees with PolynomialFeatures from sklearn.preprocessing	Python
Support Vector Regression	e1071 makes it very easy	Requires feature scaling, therefore lengthy	R
Decision Tree Regression	Process requires to define control variable	Easy going process with DecisionTreeRegresson from scikitlearn	Python
Random Forest Regression	Process requires to define control variable	Same code as Decision Tree, easy process with sklearn.ensemble - RandomForestRegressor	Python
Logistic Regression	Requires multiple lines of code	Much more intuitive	Python
k-NN Classification	Careful while plotting training and test sets	Intuitive	Python
SVM	Ease of use	Same ease of use	R
Kernel-SVM	Ease of use	Same ease of use	R
Naive-Bayes Classifier	Careful during the call of naiveBayes function for x & y values	Ease of use	Python
Decision Trees	Helpful with the plot and text function for classifier object	Intuitive	R
Random Forest Classifier	Careful during the call of randomForest function for x & y values	Ease of use with RandomForestClassifier	Python
Clustering	k-means is built-in in R and Hierarchical Clustering is easier	Better for visualization	R
Association Rule Learning	Extremely easy and intuitive with built in functions for both Apriori & Eclat using the arules library	Apriori is still intuitive but Eclat is a lenghty algorithm and complicated	R
Reinforcement Learning using UCB & Thompson Sampling	Complex algorithm and lenghty	Relatively easier to implement	Python
Natural Language Processing	Extremely easy and short procedure with tm	Fairly intuitive	R
Artificial Neural Networks	Fast process as it makes use of all the cores available. Computational difference through external server is phenomenal through h2o	Takes time to process hidden layers	R
Convlutional Neural Networks	DeepWater thorugh h2o is in beta stage	Step by step process	Python
Dimensionality Reduction	Long process, caret library is tricky to operate with	In-built libraries make the work far more simpler	Python
Model Selection and Boosting	Code is complicated and reqiures multiple functions	Relatively easier with built in functions from cross_val_score	Python

Course Structure

1. Data Preprocessing and Classification

2. Simple Linear Regression

3. Multiple Linear Regression

4. Polynomial Regression (Contains Random Forest and SVR)

5. Classification (Contains Logistic Regression, Decision Trees, k-NN, Naive Bayes, Random Forest, SVC, SVM, Kernel-SVM)

6. Clustering (k-means and Hierarchical Clustering along with Elbow Representations)

7. Association Rule Learning (using Apriori and Elclat Algorithms)

8. Reinforcement Learning (using UCB and Thompson Sampling)

9. Natural Language Processing (using nltk in Python and tm in R)

10. Neural Networks (through ANN and CNN - Note, CNN through DeepWater is in beta stage and has not been implemented in R)

11. Dimensionality Reduction (using Principal Component Analysis, Linear Discriminant Analysis & Kernel PCA)

12. Model Selection and Boosting

Annex. 1 Containg various templates for Regression, Plotting, Classification, Cross Validation and Plotting along with PDFs from the lecture