Artificial Neural Networks For Business Managers In R Studio

You do not need coding or advanced mathematics background for this course. Understand how predictive ANN models work

What you'll 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
Requirements
Students will need to install R Studio software but we have a separate lecture to help you install the same
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 conceptsHow 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.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include 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 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.Below are the course contents of this course on ANN:Part 1 - Setting up R studio and R Crash courseThis 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 ConceptsThis 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 RIn 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 PreprocessingIn 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 RegressionThis 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 youunderstand where the concept is coming from and how it is important. But even if you don't understandit,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a Neural Network model in R will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy––––––Below are some popular FAQs of students who want to start their Deep learning journey-Why use R for Deep Learning?Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep 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. R has a big advantage: it was designed specifically 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 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, R 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. 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 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.

Overview

Section 1: Introduction

Lecture 1 Welcome to the course

Lecture 2 Introduction to Neural Networks and Course flow

Lecture 3 Course Resources

Lecture 4 This is a milestone!

Section 2: Setting Up R Studio and R crash course

Lecture 5 Installing R and R studio

Lecture 6 Basics of R and R studio

Lecture 7 Packages in R

Lecture 8 Inputting data part 1: Inbuilt datasets of R

Lecture 9 Inputting data part 2: Manual data entry

Lecture 10 Inputting data part 3: Importing from CSV or Text files

Lecture 11 Creating Barplots in R

Lecture 12 Creating Histograms in R

Section 3: Single Cells - Perceptron and Sigmoid Neuron

Lecture 13 Perceptron

Lecture 14 Activation Functions

Section 4: Neural Networks - Stacking cells to create network

Lecture 15 Basic Terminologies

Lecture 16 Gradient Descent

Lecture 17 Back Propagation

Section 5: Important concepts: Common Interview questions

Lecture 18 Some Important Concepts

Section 6: Standard Model Parameters

Lecture 19 Hyperparameters

Section 7: Practice Test

Section 8: Tensorflow and Keras

Lecture 20 Keras and Tensorflow

Lecture 21 Installing Keras and Tensorflow

Section 9: R - Dataset for classification problem

Lecture 22 Data Normalization and Test-Train Split

Lecture 23 More about test-train split

Section 10: R - Building and training the Model

Lecture 24 Building,Compiling and Training

Lecture 25 Evaluating and Predicting

Section 11: The NeuralNets Package

Lecture 26 ANN with NeuralNets Package

Section 12: R - Complex ANN Architectures using Functional API

Lecture 27 Building Regression Model with Functional AP

Lecture 28 Complex Architectures using Functional API

Section 13: Saving and Restoring Models

Lecture 29 Saving - Restoring Models and Using Callbacks

Section 14: Hyperparameter Tuning

Lecture 30 Hyperparameter Tuning

Section 15: Add-on 1: Data Preprocessing

Lecture 31 Gathering Business Knowledge

Lecture 32 Data Exploration

Lecture 33 The Data and the Data Dictionary

Lecture 34 Importing the dataset into R

Lecture 35 Univariate Analysis and EDD

Lecture 36 EDD in R

Lecture 37 Outlier Treatment

Lecture 38 Outlier Treatment in R

Lecture 39 Missing Value imputation

Lecture 40 Missing Value imputation in R

Lecture 41 Seasonality in Data

Lecture 42 Bi-variate Analysis and Variable Transformation

Lecture 43 Variable transformation in R

Lecture 44 Non Usable Variables

Lecture 45 Dummy variable creation: Handling qualitative data

Lecture 46 Dummy variable creation in R

Lecture 47 Correlation Matrix and cause-effect relationship

Lecture 48 Correlation Matrix in R

Section 16: Linear Regression Model

Lecture 49 The problem statement

Lecture 50 Basic equations and Ordinary Least Squared (OLS) method

Lecture 51 Assessing Accuracy of predicted coefficients

Lecture 52 Assessing Model Accuracy - RSE and R squared

Lecture 53 Simple Linear Regression in R

Lecture 54 Multiple Linear Regression

Lecture 55 The F - statistic

Lecture 56 Interpreting result for categorical Variable

Lecture 57 Multiple Linear Regression in R

Lecture 58 Test-Train split

Lecture 59 Bias Variance trade-off

Lecture 60 Test-Train Split in R

Section 17: Practice Assignment

Section 18: Congratulations & about your certificate

Lecture 61 The final milestone!

Lecture 62 Bonus lecture

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