R Programming

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

Over 1 Million students world-wide trust this course.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course can be completed by either doing either the Python tutorials, or R tutorials, or both - Python & R. Pick the programming language that you need for your career.

This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

• Part 1 - Data Preprocessing

• 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