Machine Learning Made Easy: Beginner to Advanced using R Download

Learn Machine Learning Algorithms using R from experts with hands-on examples and practice sessions. With 5 different pr

What you’ll learn

R Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning.
Write your own R scripts and work in R environment.
Import, manipulate, clean up, sanitize and export datasets.
Understand basic statistics and implement using R.
Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.
Do powerful analysis on data, find insights and present them in visual manner.
Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
Know how each machine learning algorithm works and which one to choose according to the type of problem.
Build more than one powerful machine learning model and be able to select the best one and improve it further.

Requirements

Familiarity with high school mathematics.

Description

Want to know how Machine Learning algorithms work and how people apply it to solve data science problems? You are looking at right course!

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.

Here is how the course is going to work:

Part 1 – Introduction to R Programming.
- This is the part where you will learn basic of R programming and familiarize yourself with R environment.
- Be able to import, export, explore, clean and prepare the data for advance modelling.
- Understand the underlying statistics of data and how to report/document the insights.
Part 2 – Machine Learning using R
- Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
- Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it.
- Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

Features:

Fully packed with LAB Sessions. One to learn from and one for you to do it yourself.
Course includes R code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.
Quiz after each section to test your learning.

Bonus:

This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.
These projects will serve as your step by step guide to solve different business and data science problems.

Who this course is for:

Anyone interested in Data Science and Machine Learning.
Students who want a head start in Data Science field.
Data analysts who want to upgrade their skills in Machine Learning.
People who want to add value to their work and business by using Machine Learning.
People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.
People interested in understanding the application of machine learning algorithms on real business problems.
People interested in understanding how a machine learning algorithm works and what’s the math behind it.