Learn Artificial Neural Networks (ANN) in Python. Building Predictive Deep Learning Models Using Keras & Tensorflow | Python
- 4.3 (191 ratings)
- 27,308 students enrolled
- Created by Start-Tech Academy
- Last updated 4/2020
You’re looking for an entire Artificial Neural Network (ANN) course that teaches you everything you would like to make a Neural Network model in Python, right?
You’ve found the right Neural Networks course!
After completing this course you’ll be able to:
- Identify the business problem which may be solved using Neural network Models.
- Have a transparent understanding of Advanced Neural network concepts like Gradient Descent, forward and Backward Propagation, etc.
- Create neural network models in Python using the Keras and Tensorflow libraries and analyze their results.
- Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all or any students who undertake the course of this Neural network.
If you’re a business Analyst or an executive, or a student who wants to find out and apply Deep learning in world problems of business, this course will give you a solid base for that by teaching you a number of the foremost advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should fancy to create a predictive model using Neural Networks.
Most courses only specialize in teaching the way to run the analysis but we believe that having a robust theoretical understanding of the concepts enables us to make an honest model. And after running the analysis, one should be ready to judge how good the model is and interpret the results to truly be ready to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics consulting company, we’ve helped businesses solve their business problem using Deep learning techniques and that we have used our experience to incorporate the sensible aspects of data analysis in this course
We are also innovators of some of the most popular online courses – with over 250,000 records and thousands of 5-star reviews like this:
This is excellent, I really like the very fact the all explanation given is often understood by a layman – Joshua
Thank you Author for this wonderful course. You are the simplest and this course is worth any price. – Daisy
Teaching our students is our job and that we are committed thereto. If you’ve got any questions on the course content, practice sheet, or anything associated with any topic, you’ll always post an issue within the course or send us a direct message.
Download the exercise files, take the exercise test, and complete the tasks
With each lecture, there are attached class notes that you can follow. You can also take a practice test to see your understanding of the concepts. There is a final practical task for you to carry out your educational process.
What is covered in this course?
This course teaches you all the steps of making a Neural network-based model i.e. a Deep Learning model, to solve business problems.
Following are the contents of this ANN course:
- Part 1 – Python basics
This part gets you started with Python.
This part will assist you found out the python and Jupyter environment on your system and it will teach you ways to perform some basic operations in Python. We will understand the importance of various libraries like Numpy, Pandas & Seaborn.
- Part 2 – Theoretical Concepts
This part will offer you a solid understanding of the concepts involved in Neural Networks.
In this section you’ll study the only cells or Perceptrons and the way Perceptrons are stacked to make a specification. Once the architecture is about, we understand the Gradient descent algorithm to seek out the minima of a function and find out how this is often wont to optimize our network model.
- Part 3 – Create an ANN regression model and classification in Python
In this part you’ll find out how to make ANN models in Python.
We’ll start this section by creating an ANN template with Sequential API to solve the classification problem. We explore how to define specifications, model configuration, and model training. Then we evaluate the performance of our trained model and use it to forecast new data. We also solve the regression problem during which we try to predict house prices throughout the site. We’ll also cover how to create complex ANN structures using the functional API. Finally, we explored how to save and restore many forms.
We also understand the importance of libraries like Keras and TensorFlow during this part.
- Part 4 – Data Preprocessing
In this part you’ll learn what actions you would like to require to organize Data for the analysis, these steps are vital for creating a meaningful.
In this section, we will start with the basic theory of the decision tree, then cover topics for pre-processing data such as missing value calculation, variable conversion, and division of training test.
- Part 5 – Classic ML technique – rectilinear regression
This section starts with a simple rectilinear regression then covers multiple rectilinear regression.
We have covered the essential theory behind each concept without getting too mathematical about it in order that you
understand where the concept is coming from and how it is important. But even if you don’t understand
It will be fine as long as you learn how to operate and interpret the result as it is taught in practical lectures.
We also check out the way to quantify the model’s accuracy, what’s the meaning of F statistic, how categorical variables within the independent variables dataset are interpreted within the results and how can we finally interpret the result to seek out out the solution to a business problem.
By the top of this course, your confidence in creating a Neural Network model in Python will soar. You’ll have a radical understanding of the way to use ANN to make predictive models and solve business problems.
Go ahead and click on the enroll button, and I’ll see you in lesson 1!
Below are some popular FAQs of scholars who want to start out their Deep learning journey-
Why use Python for Deep Learning?
Understanding Python is one of the precious skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is that the programing language of choice for data science. Here’s a brief history:
- In 2016, she outperformed R on Kaggle, the main platform for data science competitions.
- 2017, it overtook R on KDNuggets’s annual poll of knowledge scientists’ most used tools.
- In 2018, 66% of knowledge scientists reported using Python daily, making it the amount one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development within the Python ecosystem. And while your journey to find out Python programming could also be just beginning, it’s nice to understand that employment opportunities are abundant (and growing) also.
What is the difference between data processing, Machine Learning, and Deep Learning?
Put simply, machine learning and data processing using equivalent algorithms and techniques as data processing, except the sorts of predictions vary. As data processing detects previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge – and it automatically applies this information to data, decision making, and actions.
On the other hand, deep learning uses advanced computing power and special types of neural networks and applies them to large amounts of knowledge to discover, understand, and define complex patterns. Automatic language translation and medical diagnoses are samples of deep learning.
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Coupon Code: APRIL2020G