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Artificial 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
Development,Data Science,Neural Networks
Lectures -62
Resources -1
Duration -9 hours
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Course Description
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 evident 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 confidently practice, talk about, and comprehend Deep Learning principles.
Artificial Neural Networks (ANN) Using Keras And TensorFlow In Python 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.
We also developed some of the most well-liked online courses, which have attracted over 250,000 students and thousands of 5-star evaluations, like the ones below:
This is very good, I love the fact that the explanation given can be understood by a layman - Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy
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:
- Part 1 - Python basics
This part gets you started with Python.
Here, you will learn to 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 the 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 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. - 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. - 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 trees 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 model accuracy, what is the meaning of F statistic, 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 needing more practical experience
- Anyone curious to master ANN from the Beginner level in a short span of time
Goals
What will you learn in this course:
- Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
- Understand the business scenarios where Artificial Neural Networks (ANN) is applicable
- Building an Artificial Neural Network (ANN) in Python
- Use Artificial Neural Networks (ANN) to make predictions
- Learn the usage of Keras and Tensorflow libraries
- Use Pandas DataFrames to manipulate data and make statistical computations.
Prerequisites
What are the prerequisites for this course?
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
3 Lectures
- Welcome to the course 02:59 02:59
- Introduction to Neural Networks and Course flow 04:38 04:38
- Course resources
Setting up Python and Jupyter Notebook 9 lectures
9 Lectures
Single Cells - Perceptron and Sigmoid Neuron
3 Lectures
Neural Networks - Stacking cells to create network
3 Lectures
Important concepts: Common Interview questions
1 Lectures
Standard Model Parameters
1 Lectures
Tensorflow and Keras
2 Lectures
Python - Dataset for classification problem
2 Lectures
Python - Building and training the Model
4 Lectures
Python - Solving a Regression problem using ANN
1 Lectures
Complex ANN Architectures using Functional API
1 Lectures
Saving and Restoring Models
1 Lectures
Add-on 1: Data Preprocessing
18 Lectures
Add-on 2: Classic ML models - Linear Regression
12 Lectures
Instructor Details
Abhishek and Pukhraj
Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Founded by Abhishek Bansal and Pukhraj Parikh.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.
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