Complete Machine Learning with R Studio – ML for 2020
Salepage : Complete Machine Learning with R Studio – ML for 2020
You’re seeking for a comprehensive Machine Learning course that can help you establish a successful career in Data Science and Machine Learning, right?
You’ve discovered the ideal Machine Learning course!
Following completion of this course, you will be able to:
Build predictive Machine Learning models with confidence to solve business challenges and define company strategy.
Answer interview questions on Machine Learning.
Participate in and perform in online Data Analytics contests such as those hosted by Kaggle.
Check out the table of contents to discover what Machine Learning models you’ll be learning.
How will this course benefit you?
All students who complete this Machine learning fundamentals course receive a verifiable Certificate of Completion.
If you are a company manager, executive, or student who wants to understand and use machine learning in real-world business challenges, this course will provide you with a strong foundation by teaching you the most popular machine learning approaches.
Why should you take this class?
This course covers all of the procedures involved in addressing a business problem using linear regression.
Most courses simply teach how to conduct the analysis, but we feel that what occurs before and after running analysis is much more significant, i.e. before running analysis, you must obtain the correct data and perform some pre-processing on it. And, after conducting the analysis, you should be able to determine how good your model is and understand the data in order to truly aid your business.
What qualifies us to educate you?
Abhishek and Pukhraj teach the course. As managers at the Global Analytics Consulting firm, we have assisted businesses in solving business problems via the use of machine learning techniques, and we have leveraged our knowledge to include practical parts of data analysis in this course.
We also developed some of the most popular online courses, with over 150,000 enrollments and thousands of 5-star ratings like these:
This is excellent; I appreciate how all of the explanations can be comprehended by a layperson. – Jonathan
Thank you very much, Author, for this fantastic course. You are the greatest, and this training is priceless. – Daisy
Our duty is to teach our pupils, and we are dedicated to it. If you have any issues regarding the course material, practice sheet, or anything else, please post them in the course or email us a private message.
Download practice files, take quizzes, and do assignments.
There are class notes connected to each lecture so you may follow along. You may also take tests to test your comprehension of ideas. Each lesson includes a practice assignment to help you put your knowledge into practice.
The following is a collection of frequently asked questions by students looking to begin their Machine Learning journey:
What exactly is Machine Learning?
Machine Learning is a branch of computer science that allows computers to learn without being explicitly programmed. It is a subfield of artificial intelligence that is predicated on the premise that systems can learn from data, spot patterns, and make choices with little or no human interaction.
What are the steps I should take to create a Machine Learning model?
Your learning process may be divided into three stages:
Statistics and Probability – Implementing machine learning techniques necessitates a fundamental understanding of statistics and probability principles. This is covered in the second segment of the course.
Understanding Machine Learning – The fourth section explains the words and ideas connected with Machine Learning and walks you through the methods required to develop a machine learning model.
Programming Knowledge – Programming is an important aspect of machine learning. Python and R have definitely emerged as the leaders in recent days. The third section will assist you in configuring the Python environment and teaching you some fundamental procedures. There is a video in the following parts that shows how to apply each subject given in the theoretical lecture in Python.
Understanding of models – The fifth and sixth sections address classification models, and each theoretical lecture is followed by a practical session in which we execute each question with you.
Why should you use R for Machine Learning?
Understanding R is one of the most important abilities for a career in Machine Learning. The following are some of the reasons why you should study Machine Learning in R.
It’s a popular language for Machine Learning at leading IT companies. Almost all of them recruit R data scientists. For example, Facebook used R to do behavioral analysis on user post data. R is used by Google to evaluate ad performance and produce economic projections. R is used in analytic and consulting organizations, banks and other financial institutions, academic institutions and research laboratories, and pretty much wherever else data requires analyzing and visualizing.
Learning the fundamentals of data science is perhaps simpler with R. R has a significant advantage in that it was created primarily for data manipulation and analysis.
Fantastic packages that will make your life simpler. R offers a superb ecosystem of packages and other tools for data science since it was created with statistical analysis in mind.
A strong and expanding community of data scientists and statisticians. R has grown in popularity with the area of data analysis, becoming one of the world’s fastest-growing programming languages (as measured by StackOverflow). That means it’s simple to discover solutions to problems and community advice as you progress through R projects.
Add another tool to your arsenal. No single language will be the best tool for every job. Adding R to your toolbox will make some projects easier, and it will also make you a more flexible and marketable employee when looking for data science jobs.
What distinguishes Data Mining, Machine Learning, and Deep Learning?
Simply put, machine learning and data mining use the same algorithms and techniques as data mining, with the exception that the types of predictions differ. While data mining uncovers previously unknown patterns and knowledge, machine learning replicates known patterns and knowledge—and then applies that information automatically to data, decision-making, and actions.
Deep learning, on the other hand, employs advanced computing power and special types of neural networks to learn, understand, and identify complex patterns in large amounts of data. Deep learning applications include automatic language translation and medical diagnosis.