Great News! As we launch this course on 4th September, we are offering a flat 60% Discount on this course.
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.
1. Introduction to Computer Vision
The module covers an overview of Computer Vision and its applications, to get you upto speed on the current happenings in the Industry.
2. Getting Ready for the Course
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.
3.Building your first computer Vision Model
This module walks you through a simple but impactful task of computer vision - solving an image classification problem with ease.
4. Project I – Image Classification project
In this project, you will apply the learnings of the previous module to solve a real life image classification problem.
5. Introduction to Neural Networks
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.
6.Introduction to Convolutional Neural Network
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.
7.Tips and Tricks to Improve Deep Learning model performance
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.
8.Horizon of Computer Vision and Case Studies
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.
9.Where to go from here ?
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.
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.
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.
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
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.
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.
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.
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.
Why take this course?
This course is ideal for people who are looking to start building their own Computer Vision applications. Several features which make it exciting are:
- Project-driven – You will encounter numerous projects in the course which will give you practice of the concepts throughout in a more pragmatic manner.
- Top-down approach to teaching – This course follows a top-down approach to explain topics, wherein you will get to see the bigger picture of solving Computer Vision tasks, and then go on to understand the details.
- Easy to understand - The biggest challenge that beginners face is that most of the courses explain Deep Learning as a difficult mathematical subject. Not us! We simplify the subject with easy to understand videos and help you build intuition on the concepts.
- Industry-relevant problems - All projects in the course are modeled to ensure that you are ready for industry-relevant problems in your own domain.
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.
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
1. Who should take this course?
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.
2. I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?
The course does not assume any prior background in Machine Learning. So you are welcome to follow through the course.
3. Do I need to install any software before starting the course?
Yes, you will get information about all installations as part of the course.
4. What is the refund policy?
The fee for this course is non-refundable.
5. Do I need to take the modules in a specific order?
We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.
6. Do I get a certificate upon completion of the course?
Yes, you will be given a certificate upon satisfactory completion of the course.
7. What is the fee for this course?
The price of this course is INR 29,999/-
8. How long I can access the course?
You will be able to access the course material for six months since the start of the course.
9. When will the classes be held in this course?
This is an online self-paced course, which you can take any time at your convenience over the 6 months after your purchase.
10. How many hours per week should I dedicate to complete the course?
If you can put between 6 to 8 hours a week, you should be able to finish the course in 8 to 10 weeks.
For people undergoing the course, you can call us any time between 9 a.m. - 5 p.m. on Weekdays Monday - Friday on +91-8368253068 or email us on email@example.com
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- Welcome to Computer Vision
- Documentary on Computer Vision
- Understanding the Computer Vision Application
- Understand Your Course Content
- Getting ready for the course
- Setting up the System
- Getting yourself ready
- Problem Statement
- Introduction to Pre-trained Model
- Solving Computer Vision Task
- Project I : Gender Classification
- Understanding Neural Network
- Neural Network from Scratch
- Solving Emergency Classification Model using NN in Keras
- Exercise : Gender Classification using NN
- Understanding Convolutional Neural Network
- Solving Emergency Classification Model using CNN
- Exercise : Gender Classification using CNN
- Areas of Improvement
- Problem 1 : Less data to train
- Problem 2 : Variation in Data
- Problem 3 : Overfitting
- Problem 4 : Underfitting
- Problem 5 : High Training Time
- Problem 6 : Appropriate Architecture
- Problem 7 : Choosing the Right Metric
- Combining the Tips and Tricks
- Exercise : Improving the model performance on Gender Classification
- Horizon of Computer Vision
- Case Study 1 : Image Regression
- Project II
- Case study 2 : Object Detection
- Project III
- A certiﬁcate will be offered after successful completion of this course.
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You should have stated the configuration of the system as pre-requisite before starting/enrolling for the course, not everyone has the high-end laptop configuration. If we go for other options like the cloud, all of them are paid. After paying for the course so much, we are expected to pay again for running our notebooks. At least you should have set up an environment where we can practise
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- Learn to solve Computer Vision tasks using state-of-the-art Deep learning algorithms
- Apply your learnings on real-life problems