Clustering & Classification With Machine Learning In R (Udemy.com)

( 51 Reviews )

Created by: Minerva Singh

Produced in 2021

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Overall Score : 98 / 100

Course Description

HERE IS WHY YOU SHOULD TAKE THIS COURSE: This course your complete guide to both supervised & unsupervised learning using R... That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic... This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in Data Science! You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R. THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING: - A full introduction to the R Framework for data science - Data Structures and Reading in R, including CSV, Excel and HTML data - How to Pre-Process and - Clean- data by removing NAs/No data,visualization - Machine Learning, Supervised Learning, Unsupervised Learning in R - Model building and selection...& MUCH MORE! By the end of the course, you-'ll have the keys to the entire R Machine Learning Kingdom! NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE REQUIRED: You-'ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you-'ll easily use data science packages like caret to work with real data in R... You-'ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we'll work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW!Who this course is for:

Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data
Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using R

Time: 8h

Instructor Details

Minerva Singh

4.9 Rating
51 Reviews

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I domost of my spatial data analysis work using R and QGIS.Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being ascientist and number cruncher, I am an avid traveler

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Reviews

4.9

51 total reviews

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Write a Review

By Pratheeksha G

yes

By Louettaw Monty

great

By Jo Johnny

Quite a collection of algorithms and their applications

By Yo Shing

One of the best course for machine learning using r. thank you Minerva madam for giving awesome course

By Mon Amar

I enjoyed the course very much. It was my introduction into machine learning, and I am very satisfied with the course. Thanks

By Tilara Varudha

I have no enough experience on this topic. I found Minerva Singh presentation and explanation to be very clear and concise, and was able to learn and absorb the material with ease. Her style and cadence was very valuable in reinforcing the presentation.

By Amaborsha Purnima

Great and a complete course which could be looked upon again and again to refresh concepts. I highly recommend this course.

By Khilary Magdum

This course is a must have for anyone interested in machine learning. very clear introduction, easy to follow, right pace allows me to do the exercises in R as we progress. Madam does an amazing job of turning extremely complex mathematical concepts into easily understandable chunks. She goes under the hood to show us how each of the algorithms work (in detail) and lets us know what kind of data each is best suited for.

By Nirob Ghatok

I am completely new to Machine Learning and R-programming. This course provides a great balance between theory and its application in R for supervised and unsupervised machine learning techniques.

By Viluva Nitai

This was my first course with Minerva. I think this was my best investment of time and money in a long time.

By Reali Lif

Well-organized course that answers all my questions. I hope as soon as possible i can apply all learning topic on my work. waiting for your more courses like this one.

By Kid Caka