What you would learn in Neural Networks in Python: Deep Learning for Beginners course?
You're searching for a comprehensive artificial Neural Network (ANN) course which teaches all you need to know to build a Neural Network model in Python. Must you be?
You've found the perfect Neural Networks class!
When you've completed this program, you can:
Find the business issue that can be solved with Neural Network Models.
Be able to comprehend Advanced Neural network concepts such as Gradient Descent forward, Forward and backward Propagation, and more.
Make Neural network models using Python with the help of Keras along with Tensorflow libraries, and then analyze the outcomes.
Practice confidently, talk about and comprehend Deep Learning concepts
What can this Course do to aid you?
A Certificate of Completion that is Verifiable is awarded to all students who take the Course of this Neural network.
Suppose you're a business analyst or executive or even a student wanting to master how to apply deep learning to real-world problems in business. In that case, This Course will give you a solid foundation for this by teaching you sure of the sophisticated ideas in Neural networks and their applications in Python without becoming too Mathematical.
The reason why you should select this program?
This course will cover all the steps to follow to develop an effective predictive model employing Neural Networks.
Most courses focus on running the analysis, but the fact that we have a solid knowledge of the theories allows us to construct an accurate model. When the investigation is completed, it is expected to determine how well the model is and how to interpret the results helpful to the business.
Get Practice file download, take a Practice test, and then complete assignments.
In each lecture, there are notes for a class that allow you to follow through. It is also possible to take a test to test your comprehension of the concepts. There's also a final practice task that you can use to apply your knowledge.
What's taught by this class?
This course will teach you the steps to create the Neural model based on networks, i.e., an Deep Learning model, to help solve business challenges.
Here is the course content of this class on ANN:
Part I - Python the basics
This section will get you to know Python.
This section will assist you in creating the Python and Jupyter environments on your system. It will help you perform certain basic operations with Python. We'll appreciate the significance of the various libraries, such as Numpy, Pandas & Seaborn.
Part 2 Theoretical Concepts
This section will provide you with an understanding of the aspects associated with Neural Networks.
In this section, you will be introduced to the single cells, also known as Perceptrons, and how Perceptrons are placed on building a network. After the architecture is established, we will learn how to use the Gradient descent algorithm to determine the smallest value of a given function and how it is utilized to optimize our network model.
Part 3: Creating the Regression and Classification models in Python
In this segment, you'll learn to create ANN models using Python.
This section will begin by developing an ANN model employing Sequential API to resolve the classification problem. We will define the networks, design the model, and set the model. Then, we assess how well our learned model is and apply it to make predictions based on new data. We also tackle an equation problem where we try to predict home prices for a specific location. We will also discuss how to construct complicated ANN models using the functional API. Finally, we will learn you can save models and recover them.
We also recognize the importance of libraries like Keras or TensorFlow in this area.
Part 4 Data Preprocessing
In this segment, you'll discover the steps you have to do to prepare Data for analysis. These steps are crucial in creating a user.
In this section, we'll begin by introducing the basics of decision trees and then discuss topics in data pre-processing such as missing value, the imputation of variables, variable transformation, and split of the Test-Train.
Part 5 - Classic ML technique - Linear Regression This section begins with simple linear regression and later goes over the multiple linear regression.
We've explained the fundamental theories behind each concept without going into a mathematical discussion about it so that you can be more comfortable with it.
Be aware of where the idea comes from and why it's crucial. But even if you don't understand
It's okay so long as you know how to interpret the lecture results on practical aspects.
We also examine the best way to measure model accuracy, the significance of F statistic, how categorical variables from data on independent variables are considered in the analysis, and finally, how to look at the results to figure the solution to a business question.
At the end of this Course and your confidence in creating the Neural Network model in Python will skyrocket. You'll be able to grasp the basics of utilizing ANN to develop predictive models and solve business issues.
Content of the Course:
Learn the basics the concepts of Artificial Neural Networks (ANN) and Deep Learning
Learn about the business scenarios that Artificial Neural Networks (ANN) can be used
The creation of Artificial Neural Networks (ANN) with Python. Python
Utilize Artificial Neural Networks (ANN) to help make predictions
Learn to make use of Keras as well as Tensorflow library
Make use of Pandas DataFrames for manipulating data and performing statistical calculations.
Download Neural Networks in Python: Deep Learning for Beginners from below links NOW!
Write your comment!
Access Permission Error
You do not have access to this product!
Dear User! To download this file(s) you need to purchase this product or subscribe to one of our VIP plans.