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About Computer Vision using Deep Learning 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.
Prerequisites of Computer Vision using Deep Learning Course
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.
Computer Vision using Deep Learning Course Curriculum

1
Introduction to computer vision
 Getting Started
 Knowing each other
 Welcome to Computer Vision
 Documentary on Computer Vision FREE PREVIEW
 Exercise1
 Applications of Computer Vision
 Exercise2
 Why Computer Vision is more in Demand?
 Exercise3
 Understand your course content
 Exercise 4

2
Getting ready for the course
 Getting ready for the course
 System Requirements
 Setting up the System on Cloud
 Setting up locally
 Accessing the course material
 Getting yourself ready

3
Building your first computer vision model
 Understanding the problem
 Exercise : Understanding the problem
 Introduction to Pretrained Model FREE PREVIEW
 Exercise : Introduction to Pretrained Model
 How to handle Image data
 Exercise : How to handle Image data
 Exploring the Emergency Classification Dataset
 Exercise : Exploring the Emergency Classification Dataset
 Loading and PreProcessing Dataset
 Exercise : Loading and PreProcessing Dataset
 Solving the challenge using Pretrained Model
 Solving the challenge using Pretrained Model
 Exercise : Solving the challenge using Pretrained Model
 Quick Recap : What have we done till now?

4
Project I
 Project I

5
Introduction to Neural Network
 Getting started with Neural Network
 Exercise : Getting started with Neural Network
 Understanding Forward Propogation
 Exercise : Forward Propogation
 Math Behind Forward Propagation
 Exercise : Math Behind Forward Propagation
 Error and Reason for Error
 Exercise : Error and Reason for Error
 Gradient Descent Intuition
 Understanding Math Behind Gradient Descent
 Exercise : Gradient Descent
 Optimizer
 Exercise : Optimizer
 Back Propagation
 Exercise : Back Propagation
 Why Numpy?
 Exercise : Why Numpy?
 Understanding the Steps in Numpy FREE PREVIEW
 Exercise : Understanding the Steps in Numpy
 Defining Parameters in Numpy
 Exercise : Defining Parameters in Numpy
 Implementing Forward Propagation
 Exercise : Implementing Forward Propagation
 Implementing Backward Propagation
 Exercise : Implementing Backward Propagation
 Why Keras?
 Exercise : Why Keras?
 Neural Network in Keras
 Exercise : Neural Network in Keras
 Loading and PreProcessing Dataset
 Exercise : Loading and PreProcessing Dataset
 Solving the challenge
 Exercise : Solving the challenge
 Hyperparameter Tuning
 Exercise : Hyperparameter Tuning
 Performance Comparision with Pretrained Model
 Exercise : Performance Comparision with Pretrained Model
 Exercise : Gender Classification using NN

6
Introduction to Convolutional Neural Network
 Why CNN?
 Exercise : Why CNN?
 Understanding the working of CNN Filters
 Exercise : Understanding the working of CNN Filters
 Introduction to Padding
 Exercise : Introduction to Padding
 Padding Strategies
 Exercise : Padding Strategies
 Padding Strategies in Keras
 Exercise : Padding Strategies in Keras
 Introduction to Pooling
 Exercise : Introduction to Pooling
 CNN architecture and its working
 Exercise : CNN architecture and its working
 Loading and PreProcessing Dataset
 Solving the challenge
 Exercise : Solving the challenge
 Hyperparameter Tuning
 Exercise : Hyperparameter Tuning
 Performance Comparision with Pretrained Model
 Exercise : Gender Classification using CNN

7
Tips and Tricks to Improve Model Performance
 Understand areas to improve
 Exercise : Understand areas to improve
 Introduction to the Problem
 Exercise : Introduction to the Problem
 Implementation and Solution
 Exercise : Implementation and Solution
 Introduction to the Problem
 Exercise : Introduction to the Problem
 Implementation and Solution
 Exercise : Implementation and Solution
 Introduction to the Problem
 Exercise : Introduction to the Problem
 Implementation and Solution
 Exercise : Implementation and Solution
 Introduction to the Problem
 Exercise : Introduction to the Problem
 Introduction to the Problem
 Exercise : Introduction to the Problem
 Implementation and Solution
 Introduction to the Problem
 Exercise : Introduction to the Problem
 Implementation and Solution
 Exercise : Implementation and Solution
 Wrong Evaluation Metric  Part I
 Exercise : Wrong Evaluation Metric  Part I
 Wrong Evaluation Metric  Part II
 Exercise : Wrong Evaluation Metric : Part II
 Combining Tips and Tricks
 Exercise : Combining the Trips and Tricks
 Exercise : Improving the model performance on Gender Classification

8
Horizon of Computer Vision and Case Studies
 Understanding the Types of Computer Vision Problems
 Exercise : Understanding the type of problems
 Approach to solve different type of problems
 Exercise : Approach to solve different type of problems
 Understanding the Regression Problem
 Exploring Facial Keypoint Identification Problem
 Exercise : Exploring Facial Keypoint Identification Problem
 Loading and PreProcessing Dataset
 Solving the Regression Challenge
 Exercise : Solving the Regression Challenge
 Project II
 Understanding Object Detection Problem
 Exercise : Understanding Object Detection Problem Statement
 Naïve Appoach to solve detection problem
 Exercise : Naïve Appoach to solve detection problem
 Exploring Blood Cell Detection Problem
 Loading and PreProcessing Dataset
 Building the model
 Other Approaches to solve Detection Problem
 Exercise : Other Approaches to solve Detection Problem
 Implementing Stateoftheart Model on Blood Detection
 Understanding Stateoftheart Model
 Exercise : Understanding Stateoftheart Model
 Project III

9
Where to go from here?
 What did we discuss?
 Where to go from here?
ProjectClassify Emergency Vehicles from NonEmergency Vehicles
Project Age Prediction of People from closeups of Facial Images
Project Identify the Location of Red Blood Cells
Instructor(s)

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. 
Neeraj Singh Sarwan
Neeraj is working at Fractal Analytics. Prior to that Neeraj was a data scientist with Analytics Vidhya. He has extensive experience in converting business problems to data problems. He has previously conducted several corporate trainings and is also an avid blogger. He's a graduate of IITBHU and will be your instructor for the Python and Modeling modules.
FAQ

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.

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.

What is the refund policy?
The fee for this course is nonrefundable.

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.

Do I get a certificate upon completion of the course?
Yes, you will be given a certificate upon satisfactory completion of the course.

What is the fee for this course?
The price of this course is INR 15,999/

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.

When will the classes be held in this course?
This is an online selfpaced course, which you can take any time at your convenience over the 6 months after your purchase.

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.
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