Clustering & Classification With Machine Learning In R
What you'll learn
- Be Able To Harness The Power Of R For Practical Data Science
- Read In Data Into The R Environment From Different Sources
- Carry Out Basic Data Pre-processing & Wrangling In R Studio
- Implement Unsupervised/Clustering Techniques Such As k-means Clustering
- Implement Dimensional Reduction Techniques (PCA) & Feature Selection
- Implement Supervised Learning Techniques/Classification Such As Random Forests
- Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy
- Should Be Able To Operate & Install Software On A Computer
- Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning
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
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).