Artificial Neural Networks (ANN) Using Keras And TensorFlow In Python

Artificial Neural Networks (ANN) Using Keras and TensorFlow in Python helps you learn everything you need to know to build a neural network model.

You will be able to recognize business problems that neural network models can answer once you have finished this course. Advanced neural network concepts like Gradient Descent, forward and backward propagation, etc. will be crystal clear to you.

With the Keras and Tensorflow frameworks, you can build neural network models in Python and examine the outcomes. With the help of this course, you can Artificial Neural Networks (ANN) Using Keras And TensorFlow In Python 

Course Overview

The entire process of building a predictive model using neural networks is covered in this course. The course material gives you a solid theoretical grasp of the ideas needed to build an effective model. After conducting the analysis, one ought to be able to evaluate how accurate the model is and interpret the findings in order to genuinely assist the company.

How this course will help you?

Each student who completes this Neural Networks course receives a Verifiable Certificate of Completion.

This course will provide you with a strong foundation for applying deep learning to real-world business problems if you are a business analyst, executive, or student. It does this by teaching you some of the most cutting-edge concepts of neural networks and how to implement them in Python without getting overly mathematical.

What makes us qualified to teach you?

Abhishek and Pukhraj are the instructors for the course. We have leveraged our expertise as managers at a Global Analytics Consulting company to include the practical parts of data analysis in this course. We have assisted businesses in solving their business problems utilizing Deep Learning techniques.

Our Promise

Our job is to teach our students, and we are dedicated to it. You can always post a question in the course or send us a direct message if you have any queries concerning the course material, the practice sheet, or anything else associated with any topic.

Take a practice test, do assignments, and get practice materials.

There are class notes connected to each lecture so you may follow along. You can assess your conceptual understanding by taking practice exams. You must complete a final practical assignment to put your knowledge into practice.

What is covered in this course?

This course teaches you how to build a Deep Learning model, i.e., a neural network-based model, to solve business challenges.

Below are the course contents of this course on ANN:

1. Part 1- Python basics
   This part gets you started with Python.
   Here, you will learn to set up the Python and Jupyter environments 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.
2. Part 2 - Theoretical Concepts
   This part will give you a solid understanding of the concepts involved in Neural Networks.
   In this section, you will learn about single cells, or Perceptrons, and how Perceptrons are stacked to create a network architecture. Once the 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.
3. 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 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.
4. Part 4: Data Preprocessing
   In this part, you will learn what actions you need to take to prepare Data for analysis. These steps are very important for creating a meaningful.
   In this section, we will start with the basic theory of decision trees, then cover data pre-processing topics like missing value imputation, variable transformation, and Test-Train split.
5. 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 model accuracy, what the meaning of F statistic is, how categorical variables in the independent variables dataset are interpreted in the results, and how 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.

Below are some popular FAQs for students who want to start their deep learning journey:

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

• In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
• In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
• In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

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.

Who this course is for:

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