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Deep Learning for Beginners: Neural Networks in R Studio – 100% Free

Coupon Added/Updated On May 16, 2020
Deep Learning for Beginners: Neural Networks in R Studio โ€“ 100% Free

๐Ÿ“– Overview:

Learn Artificial Neural Networks (ANN) in R. Build predictive deep learning models using Keras and Tensorflow| R Studio

๐Ÿ‘จโ€๐Ÿซ Course Author:

Start-Tech Academy

๐Ÿ“š Requirements:

  • Students will need to install R Studio software but we have a separate lecture to help you install the same

๐Ÿค“ What You will Learn:

  • Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
  • Understand the business scenarios where Artificial Neural Networks (ANN) is applicable
  • Building a Artificial Neural Networks (ANN) in R
  • Use Artificial Neural Networks (ANN) to make predictions
  • Use R programming language to manipulate data and make statistical computations
  • Learn usage of Keras and Tensorflow libraries

๐Ÿ“ƒ Description:

You’re looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right?

You’ve found the right Neural Networks course!

After completing this course you will be able to:

  • Identify the business problem which can be solved using Neural network Models.

  • Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.

  • Create Neural network models in R using Keras and Tensorflow libraries and analyze their results.

  • Confidently practice, discuss and understand Deep Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.

If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create a predictive model using Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

 

  • Part 1 – Setting up R studio and R Crash course

    This part gets you started with R.

    This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.

  • Part 2 – Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 – Creating Regression and Classification ANN model in R

    In this part you will learn how to create ANN models in R Studio.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 – Data Preprocessing

    In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.

    In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.

  • Part 5 – Classic ML technique – Linear Regression
    This section starts with simple linear regression and then covers multiple linear regression.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you

    understand where the concept is coming from and how it is important. But even if you don’t understand

    it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

Go ahead and click the enroll button, and I’ll see you in lesson 1!

Cheers

Start-Tech Academy

————

Below are some popular FAQs of students who want to start their Deep learning journey-

 

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 knowledgeโ€”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.

๐Ÿ‘ฅ Who this course is for?

  • People pursuing a career in data science
  • Working Professionals beginning their Neural Network journey
  • Statisticians needing more practical experience
  • Anyone curious to master ANN from Beginner level in short span of time

 

Enroll now in the Course to get

๐Ÿ… Certificate of Completion
๐Ÿ“น 7.5 hours on-demand video
๐Ÿ“… Full lifetime access to the course

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