Machine Learning with R Studio
Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language with R Studio.
What you’ll learn
- Learn how to solve real life problem using the Machine learning techniques
- Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
- Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
- Understanding of basics of statistics and concepts of Machine Learning
- How to do basic statistical operations and run ML models in R
- Indepth knowledge of data collection and data preprocessing for Machine Learning problem
- How to convert business problem into a Machine learning problem
- Students will need to install R and R studio software but we have a separate lecture to help you install the same
You’re looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?
You’ve found the right Machine Learning course!
After completing this course you will be able to:
· Confidently build predictive Machine Learning models to solve business problems and create business strategy
· Answer Machine Learning related interview questions
· Participate and perform in online Data Analytics competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning models you are going to learn.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
Below is a list of popular FAQs of students who want to start their Machine learning journey-
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Who this course is for
- People pursuing a career in data science
- Working Professionals beginning their Data journey
- Statisticians needing more practical experience