Create predictive deep learning models using Keras and Tensorflow in Learn Artificial Neural Network (ANN) R. | R Studio
Created by Start-Tech Academy
Scroll through the middle of the blog to get enroll in this course
📍Course length: 7 Hour 35 minutes
📍Coupon expired in 48 hours
📍Course Language: English
What you'll learn
• Gain a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
• Understand business scenarios where artificial neural network (ANN) applies
• Construction of an artificial neural network (ANN) in R
• Use of artificial neural network (ANN) makeup predictions
• Use R programming language to manipulate data and make statistical calculations
• Learn to use Kerr and TensorFlow libraries
Requirements
· Students will be required to install R-Studio software, but we have a different lecture to help you establish the same
Description
You are looking for a complete Artificial Neural Network (ANN) course that needs to create a Neural Network Model in R that teaches you, right?
You found the right neural networks course!
After studying this course you will learn to:
Identify business problems that can be solved using neural network models. Create neural network models using the Keras and TensorFlow libraries in R and analyze their results.
Practice, discuss and understand deep learning concepts with confidence
How will this course help you?
A verifiable certificate of completion is presented to all students who conduct this neural network course.
If you are a business analyst or an executive, or a student who wants to learn and apply deep learning to real-world problems of business, this course will give you a solid foundation for it, including some of the neural networks Advanced concepts will be taught. And their implementation in R Studio is not too mathematical.
Why should you choose this course?
This course covers all the steps that one must take to create a predictive model using neural networks.
Most courses only focus on teaching how to run an analysis but we believe that having a strong theoretical understanding of concepts enables us to build a good model. And after running the analysis, one needs to be able to determine how good the model is and be able to help the business to interpret the results.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers at a global analytics consulting firm, we have helped businesses solve their business problem using deep learning techniques and we have used our experience to cover the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses - with over 250,000 nominations and 5-star reviews such as:
This is great, I love the fact that all the explanations given can be understood by a common man - Joshua
Thanks writer for this amazing course. You are the best and this course is worth any cost. - Daisy
Our promise
It is our job to teach our students and we are committed to it. If you have any questions related to course material, practice papers, or any subject, you can always post a question in the syllabus or send us a direct message.
Download practice files take practice tests and complete assignments
With each lecture, class notes are attached for you to follow. You can also do practice tests to check your understanding of concepts. A final practical task is for you to practically apply your education.
What is included in this course?
Below is the course content for this course on ANN:
Part 1 - Installation of R Studio and R Crash Course
This part starts with u r.
This section will help you to install R and R Studio on your system and it will teach you how to do some basic operations in R.
Part 2 - Theoretical Concepts
This section will give you a solid understanding of the concepts involved in neural networks.
In this section you will learn about single cells or perceptrons and how to stack perceptrons to create a network architecture. Once the architecture is set, we understand the gradient descent algorithm to find the minima of a function and learn how it is used to optimize our network model.
Part 3 - Creating regression and classification ANN models in R
In this section you will learn how to create an ANN model in R Studio.
We will begin this section by creating an ANN model using a sequential API to solve a classification problem. We then evaluate the performance of our trained model and use it to make predictions on new data. We also solve a regression problem in which we try to predict house prices at a place.
We will also cover how to build complex ANN architectures using functional APIs. Finally we learn how to save and restore the model.
We also understand the importance of libraries such as Keres and TensorFlow in this part.
Part 4 - Data Preprocessing
In this section you will know what actions you need to take to prepare the data for analysis, these steps are very important to make it meaningful.
In this section, we will start with the fundamental principle of the decision tree.
Part 5 - ML Technique
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic principle behind each concept so that you do not get too mathematical about it.
This will be fine as long as you learn how to run and interpret the result taught in a practical lecture.
We also look at how to determine the accuracy of the model, what F statistic means, how the dataset variable in the independent variable is interpreted in the results, and we interpret the result to find the answer to a business problem.
By the end of this course, your confidence in building a neural network model in R will grow. You will have a complete understanding of how to use ANN to build forecasting models and solve business problems.
Go ahead and click the enrollment button, and I'll see you in 1 lesson!
Why use R for Deep Learning? Understanding R is one of the valuable skills required for a career in machine learning. Below are some reasons why you should learn to learn Deep in R
1. It is a popular language for machine learning in top tech firms. Nearly everyone employs data scientists who r. Using Facebook, for example, the user uses R to conduct behavior analysis with post data. Google uses R to assess advertising effectiveness and make economic forecasts. And by the way, this is not just a tech firm: R is in use in analysis and consulting firms, banks and other financial institutions, educational institutions and research laboratories, and requires a lot of data analysis and visualization everywhere.
2. Learning the basics of data science r. Ar is certainly easy to use, it has one major advantage: it was specifically designed with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has an excellent ecosystem of packages and other resources that is great for data science.
4. A growing community of data scientists and statisticians. As data science exploded, R exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). This means that it is easy to find answers to questions and community guidance as you do your work through projects in R.
5. Put another tool in your toolkit. One language is not the right tool for everything. Adding R to your repertoire will make some projects easier - and of course, it will also make you a more flexible and marketable employee when you are looking for jobs in data science.
How we could differentiate the term Machine Learning, Data Mining, and Deep Learning?
Simply put, machine learning and data mining use the same algorithms and techniques like data mining, keeping the predictions of type aside.
Deep learning, on the other hand, uses computing power and advanced neural networks to specific types and applies them to large amounts of data, knowing, understanding, and identifying complex patterns. Automatic language translation and medical diagnosis are examples of deep learning.
Created by Start-Tech Academy
Scroll through the middle of the blog to get enroll in this course
📍Course length: 7 Hour 35 minutes
📍Coupon expired in 48 hours
📍Course Language: English
What you'll learn
• Gain a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
• Understand business scenarios where artificial neural network (ANN) applies
• Construction of an artificial neural network (ANN) in R
• Use of artificial neural network (ANN) makeup predictions
• Use R programming language to manipulate data and make statistical calculations
• Learn to use Kerr and TensorFlow libraries
Requirements
· Students will be required to install R-Studio software, but we have a different lecture to help you establish the same
Description
You are looking for a complete Artificial Neural Network (ANN) course that needs to create a Neural Network Model in R that teaches you, right?
You found the right neural networks course!
After studying this course you will learn to:
Identify business problems that can be solved using neural network models. Create neural network models using the Keras and TensorFlow libraries in R and analyze their results.
Practice, discuss and understand deep learning concepts with confidence
How will this course help you?
A verifiable certificate of completion is presented to all students who conduct this neural network course.
If you are a business analyst or an executive, or a student who wants to learn and apply deep learning to real-world problems of business, this course will give you a solid foundation for it, including some of the neural networks Advanced concepts will be taught. And their implementation in R Studio is not too mathematical.
Why should you choose this course?
This course covers all the steps that one must take to create a predictive model using neural networks.
Most courses only focus on teaching how to run an analysis but we believe that having a strong theoretical understanding of concepts enables us to build a good model. And after running the analysis, one needs to be able to determine how good the model is and be able to help the business to interpret the results.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers at a global analytics consulting firm, we have helped businesses solve their business problem using deep learning techniques and we have used our experience to cover the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses - with over 250,000 nominations and 5-star reviews such as:
This is great, I love the fact that all the explanations given can be understood by a common man - Joshua
Thanks writer for this amazing course. You are the best and this course is worth any cost. - Daisy
Our promise
It is our job to teach our students and we are committed to it. If you have any questions related to course material, practice papers, or any subject, you can always post a question in the syllabus or send us a direct message.
Download practice files take practice tests and complete assignments
With each lecture, class notes are attached for you to follow. You can also do practice tests to check your understanding of concepts. A final practical task is for you to practically apply your education.
What is included in this course?
Below is the course content for this course on ANN:
Part 1 - Installation of R Studio and R Crash Course
This part starts with u r.
This section will help you to install R and R Studio on your system and it will teach you how to do some basic operations in R.
Part 2 - Theoretical Concepts
This section will give you a solid understanding of the concepts involved in neural networks.
In this section you will learn about single cells or perceptrons and how to stack perceptrons to create a network architecture. Once the architecture is set, we understand the gradient descent algorithm to find the minima of a function and learn how it is used to optimize our network model.
Part 3 - Creating regression and classification ANN models in R
In this section you will learn how to create an ANN model in R Studio.
We will begin this section by creating an ANN model using a sequential API to solve a classification problem. We then evaluate the performance of our trained model and use it to make predictions on new data. We also solve a regression problem in which we try to predict house prices at a place.
We will also cover how to build complex ANN architectures using functional APIs. Finally we learn how to save and restore the model.
We also understand the importance of libraries such as Keres and TensorFlow in this part.
Part 4 - Data Preprocessing
In this section you will know what actions you need to take to prepare the data for analysis, these steps are very important to make it meaningful.
In this section, we will start with the fundamental principle of the decision tree.
Part 5 - ML Technique
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic principle behind each concept so that you do not get too mathematical about it.
This will be fine as long as you learn how to run and interpret the result taught in a practical lecture.
We also look at how to determine the accuracy of the model, what F statistic means, how the dataset variable in the independent variable is interpreted in the results, and we interpret the result to find the answer to a business problem.
By the end of this course, your confidence in building a neural network model in R will grow. You will have a complete understanding of how to use ANN to build forecasting models and solve business problems.
Go ahead and click the enrollment button, and I'll see you in 1 lesson!
Why use R for Deep Learning? Understanding R is one of the valuable skills required for a career in machine learning. Below are some reasons why you should learn to learn Deep in R
1. It is a popular language for machine learning in top tech firms. Nearly everyone employs data scientists who r. Using Facebook, for example, the user uses R to conduct behavior analysis with post data. Google uses R to assess advertising effectiveness and make economic forecasts. And by the way, this is not just a tech firm: R is in use in analysis and consulting firms, banks and other financial institutions, educational institutions and research laboratories, and requires a lot of data analysis and visualization everywhere.
2. Learning the basics of data science r. Ar is certainly easy to use, it has one major advantage: it was specifically designed with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has an excellent ecosystem of packages and other resources that is great for data science.
4. A growing community of data scientists and statisticians. As data science exploded, R exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). This means that it is easy to find answers to questions and community guidance as you do your work through projects in R.
5. Put another tool in your toolkit. One language is not the right tool for everything. Adding R to your repertoire will make some projects easier - and of course, it will also make you a more flexible and marketable employee when you are looking for jobs in data science.
How we could differentiate the term Machine Learning, Data Mining, and Deep Learning?
Simply put, machine learning and data mining use the same algorithms and techniques like data mining, keeping the predictions of type aside.
Deep learning, on the other hand, uses computing power and advanced neural networks to specific types and applies them to large amounts of data, knowing, understanding, and identifying complex patterns. Automatic language translation and medical diagnosis are examples of deep learning.
Certificate of Completion
This is dummy text. It is not meant to be read. Accordingly, it is difficult to figure out when to end it. But then, this is dummy text. It is not meant to be read. Period.
1 comments:
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