Computer Vision using Deep Learning

CVDL101
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Computer Vision using Deep Learning

About this Course

There has been a tremendous boom in the applications of Computer Vision now a days.
The applications of Computer Vision range from understanding the environment in a Self - Driving Car to build Facial Recognition based Attention Systems for classrooms in Education Industry.
A question you might ask is: why would I even want to know about Computer Vision ? As a matter of fact, there is an undeniable demand for people who have knowledge in this domain, so that they can bring about disruptive solutions in any industry possible.
Computer Vision systems deal with high variety and volume of data, specifically images or videos.It is represented as bits and blobs which is hard to explain to a machine.As a result, these systems need intricate techniques to make sense of the data and then make data driven decisions.
This course is designed to give you a taste of how the underlying techniques work in current State - of -the - Art Computer Vision systems, and walks you through a few of the remarkable Computer Vision applications in a hands - on manner so that you can create such solutions on your own.

Pre-requisites

This is a beginner friendly course, so it does not assume any familiarity with Computer Vision or Deep Learning algorithms. But, this course assumes that you are comfortable with Python programming.

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Syllabus

The module covers an overview of Computer Vision and its applications, to get you upto speed on the current happenings in the Industry.

In this module, you will get to know how to setup your own system, or on the cloud to run computer vision algorithms. This module also covers the pre-requisites you need to know to follow along with the course.

This module walks you through a simple but impactful task of computer vision - solving an image classification problem with ease.

In this project, you will apply the learnings of the previous module to solve a real life image classification problem.

In this module, you will dive deep into the backbone of Deep Learning (viz Neural Networks) where you will learn how they work and build a Deep Learning model from scratch.

Building a Deep Learning models is more of an Art than Science. In this module, you will get to learn this art in the form of tips and tricks by looking at the areas of improvement.

In this module, you will learn a more evolved form of Neural Networks - called Convolutional Neural Networks, which have been shown to outperform all other methods for image-related problems.

In this module, you will be working on case studies of different computer vision tasks. This will help to strengthen your understanding of deep learning models and how they can be used to solve real life problems.

Now that you are capable of solving practical computer vision applications, this module will show you what is that you can do with the knowledge that you have acquired.

Projects

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Classify Emergency Vehicles from Non-Emergency Vehicles

Fatalities due to traffic delays of emergency vehicles such as ambulance & fire brigade is a huge problem. In daily life, we often see that an emergency vehicles face difficulty in passing through traffic. So differentiating a vehicle into an emergency and non emergency category can be an important component in traffic monitoring as well as self drive car systems as reaching on time to their destination is critical for these services. In this project, you will get to design a computer vision system that can do just this.

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Age Prediction of People from closeups of Facial Images

We now have systems that can correctly identify faces in the wild, but they fail to give us the the facial properties to build intelligent systems, like age of the person or their gender. This project will urge you to create algorithms that would power these intelligent systems, specifically by predicting the age of the person directly from an image clipping of his/her face.

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Identify the Location of Red Blood Cells

The analysis of blood cells allows the evaluation and diagnosis of a vast number of diseases. But this is generally done manually by skilled operators. In practice, we can automate a part of this process by identifying individual blood cell from a microscopic image. The task of this project will challenge you to find the locations of red blood cells through Deep Learning

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Gender Classification from close-ups of Facial Images

It is very simple for humans to look at facial images and identify males from females. But making a computer do the same task is altogether a different story. In this project, We will be employing Deep Learning techniques in Computer Vision to try and be closer to human accuracy in identifying an image as male or female.

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Facial Key-point Detection

We are in the era where opening our mobile phones is simply a task of looking at it. To be able to do that, the phone must first decide the location of the eyes and a whole lot of other key facial features. In this project, we will be learning about how we can identify the location of eyes on the face image.

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Face Detection

Cameras of today do not require physical buttons or touch screens to click photos. They employ the use of a technology called the smile detection which detects a smiling face and instantaneously clicks photos. For the same task, cameras must identify the location of the face in the frame and then detect whether the face is a smiling one or not. In this project, we will be learning how to build the former part of the system i.e. identifying the location of the face in an image.

Instructors

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Faizan Shaikh

Faizan is working as a data scientist at Analytics Vidhya. Being a Deep Learning enthusiast, he aims to utilize his skills to push the boundaries of AI research. Faizan is an avid blogger on Analytics Vidhya, and has contributed to many articles to explain complex concepts of Deep Learning in a simple manner. He will be your instructor for the course.

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Neeraj Singh Sarwan

Neeraj is working as a data scientist at Analytics Vidhya. He has extensive experience in converting business problems to data problems. He has previously taken several corporate trainings and is also an avid blogger. He will be your instructor the course.

Frequently Asked Questions

This course is for people who are looking to get into the field of Computer Vision and start building their own Computer Vision applications using Deep Learning.

The course does not assume any prior background in Machine Learning. So you are welcome to follow through the course.

The fee for this course is non-refundable.

We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

Yes, you will be given a certificate upon satisfactory completion of the course.

The price of this course is INR 15,999/-

You will be able to access the course material for six months since the start of the course.

This is an online self-paced course, which you can take any time at your convenience over the 6 months after your purchase.

If you can put between 6 to 8 hours a week, you should be able to finish the course in 8 to 10 weeks.

₹15999($246)

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  • Status Active
  • Estimated Effort 08:00 (Hours per Week)

Highlights

  • Projects 6 Real Life

Support

For people undergoing the course, you can call us any time between 9AM - 5PM on Weekdays Monday - Friday on +91-8368253068 or email us on training_queries@analyticsvidhya.com

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