Complete Machine Learning With R Studio – ML For 2021

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 of machine learning, R, and predictive modelling. Each section contains a practice assignment for you to practically implement your learning on machine learning, R, and predictive modelling.

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 that 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 are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 parts:

Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. The second section of the course covers this part.

Understanding of Machine learning – The fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in recent days. The third section will help you set up the Python environment and teach you some basic operations. In later sections, there is a video on how to implement each concept taught in the theory lecture in Python

Understanding of models – Fifth and sixth sections cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R than Python. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. As compared to Python, R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, usage of R and Python has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Like Python, adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

What are the major advantages of using R over Python?

• As compared to Python, R has a higher user base and the biggest number of statistical packages and libraries available. Although, Python has almost all the features that analysts need, R triumphs over Python.
• R is a function-based language, whereas Python is object-oriented. If you are coming from a purely statistical background and are not looking to take over major software engineering tasks when productizing your models, R is an easier option, than Python.
• R has more data analysis functionality built-in than Python, whereas Python relies on Packages
• Python has main packages for data analysis tasks, R has a larger ecosystem of small packages
• Graphics capabilities are generally considered better in R than in Python
• R has more statistical support in general than Python

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.