Neural Networks (ANN) using Keras and TensorFlow in Python

Neural Networks (ANN) using Keras and TensorFlow in Python

Neural Networks (ANN) using Keras and TensorFlow in Python
Neural Networks (ANN) using Keras and TensorFlow in Python

Neural Networks (ANN) using Keras and TensorFlow in Python

Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python

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 Python
  • Use Artificial Neural Networks (ANN) to make predictions
  • Learn usage of Keras and Tensorflow libraries
  • Use Pandas DataFrames to manipulate data and make statistical computations.
Requirements
  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the sameStudents will need to install Python and Anaconda 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 Python, right?

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 Python using Keras and Tensorflow libraries and analyze their results.
  • Confidently practice, discuss and understand Deep Learning concepts
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 - Python basics

  • This part gets you started with Python.
  • This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
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 Python
  • In this part you will learn how to create ANN models in Python.
  • 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.
By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.

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
https://www.udemy.com/course/neural-network-understanding-and-building-an-ann-in-python/?couponCode=MARCHFREE20

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Santosh Kumar

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