This is repository made to dive into Machine Learning. After going through theoretical definitions of algorithms, every algorithm is implemented using R and then changed in order to make a coding template. Repository contains extensive exercises on Machine Learning algorithms. Organised into logical parts and programmed in R.
The whole project is organised into 10 parts - in order to understand every aspect of Machine Learning:
Part 1 - Data Preprocessing (+ made a template to use in all the other steps of ML),
Part 2 - Regression (Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression),
Part 3 - Classification (Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification),
Part 4 - Clustering (K-Means, Hierarchical Clustering),
Part 5 - Association Rule Learning (Apriori, Eclat),
Part 6 - Reinforcement Learning (Upper Confidence Bound, Thompson Sampling),
Part 7 - Natural Language Processing (Bag-of-words model and algorithms for NLP),
Part 8 - Deep Learning (Artificial Neural Networks, Convolutional Neural Networks),
Part 9 - Dimensionality Reduction (PCA, LDA, Kernel PCA),
Part 10 - Model Selection & Boosting (k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost)