BS Sociology (Virtual University) All handouts [UpDated]

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 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.

  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.