Summary, Description
You are looking for a full course on the Artificial Neural Network (ANN) that teaches you everything you need to build a model of the Neural Network in R, right?
The right Neural Networks course you’ve got!
You will be able to: After finishing this course:
Identify the market dilemma with Neural Network Models that can be solved.
Have a good grasp of the principles of the Advanced Neural Network, such as Gradient Descent, forward and backward propagation, etc.
Using Keras and Tensorflow libraries to build Neural network models in R and evaluate their performance.
Practise, explore and grasp Deep Learning principles confidently,
How’s this course going to help?
All students completing this Neural Networks course are presented with a Verifiable Certificate of Completion.
If you are a business analyst or an entrepreneur, or a beginner who wants to understand and apply Deep Learning to business issues in the real world, this course will provide you with a strong basis by showing you some of the most advanced neural network principles and their application in R Studio without going too mathematical.
Why should this course be selected for you?
This course outlines all the steps one can take to use Neural Networks to construct a predictive model.
Most courses concentrate mainly on teaching how to do the research, but we agree that a deep theoretical grasp of the principles helps one to construct a good model. And one should be able to judge how effective the model is after running the research and to analyse the findings in order to really be able to support the enterprise.
For Deep Learning, why use R?
One of the important skills needed for a career in Machine Learning is understanding R. Any explanations why you should study Deep Learning in R are below.
1. It’s a common language at top tech firms for machine learning. Nearly all of them employ data scientists who use R. For example, Facebook uses R to carry out behavioural research of user post info. To analyse ad efficacy and make economic predictions, Google uses R. And, by the way, it’s not just hardware companies: R is used by analytics and consultancy agencies, banks and other financial organisations, academic institutions and research laboratories, and data has to be processed and visualised nearly everywhere else.
2. In R, it is arguably better to learn the data science fundamentals. R has a major advantage: it was built with data manipulation and interpretation in mind in particular.
3. Incredible bundles that make it simpler for you. It has a wonderful ecosystem of packages and other tools that are perfect for data science, since R was built with statistical analysis in mind.
4. Robust, growing data scientist and statistician culture. As the data science market has grown, R has exploded with it, becoming one of the world’s fastest rising languages (as measured by StackOverflow). This ensures that solutions to questions and group guidance are easy to find when you navigate your way through R ventures.
5. In your toolkit, put another tool. For any work, no one language is going to be the best instrument. Adding R to your arsenal will make certain projects smoother and, of course, while you are applying for work in data science, it will also make you a more agile and marketable employee.
What is the distinction between Machine Learning, Deep Learning, and Data Mining?
Simply put, machine learning and data mining use the same algorithms and methods as data mining, with the exception of different kinds of forecasts. Although data mining finds previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge and automatically applies information to data, decision-making, etc.
For whom this course is intended:
People who seek a computer processing career
Working professionals starting their journey on the Neural Network
Statisticians who seek more realistic information
Anyone who is interested to master ANN in a short amount of time from the beginner stage
