Quick 5 Ways You Can Enhance Your Computer Vision Skills

This is how you can gain knowledge over computer vision

Computer vision

Computer vision

Computer vision is the hottest research field within deep learning that represents a relative understanding of visual ecosystems. It is one of the booming subjects in the present times. There have also been many developments coming up from neural networks, machine learning, and deep learning approaches too. This is the reason why many are turning towards computer learning skills. Here are the 5 ways you can improve your computer science skills. 


1 To go for a Master’s Degree 

If you want to pursue advanced computer vision skills then, the best option available is to go for a master’s degree. As robotics and machine leasing are popular these days too, going for a combined course of computer vision, machine learning, and robotics is a very good option to understand the theory behind popular computer vision methods and techniques. Let’s see what are the reasons you want to obtain a master’s degree. 

  • Development of an academic understanding by going through research papers and studies with machine learning-related roles. 
  • Having a masters degree in computer vision is always an advantage to stand out in a crowd while searching for a job. 
  • Gain a better understanding of computer vision. 


2 YouTube Videos 

Sometimes even though one has a passion to pursue computer vision projects along with machine learning, they must not have adequate funds to pursue their dreams. In such a case the possible option is to afford a better quality internet and start learning from YouTube platforms to gain an idea over CV and ML from popular channels to further sharpen your practical skills. But now, the question is what channels are right for learning? Well, here are a few! 



The channel has good videos on the basics of machine learning and neural networks and makes maths simple. This channel helps you to understand the fundamental components of a neural network. 


Stanford Computer Vision

Once you are familiar with machine learning and neural networks then you can go ahead and listen to sessions of Stanford Computer Vision to understand computer vision techniques and algorithms in detail. 



After gaining knowledge of neural networks and machine learning algorithms you can move ahead by exploring the practical projects and coding with common machine learning libraries such as PyTorch and Python. 


3 Books 

Books are a great source to gain knowledge and grip over the theory aspect of computer vision skills and machine learning. The books are an amazing way to enhance your computer vision skill. But if you are wondering which books to read then, here they are! 


Hands-on Machine Learning with Scikit-Learn by Aurelien Geron 

If you want to gain a practical exposure too then going for Aurelien Geron is a great option. The book consists of two parts, the first part has all the machine learning concepts and the second part has Convolutional and Recurrent neural networks along with TensorFlow 2 and Keras.


Deep Learning with Python 

Deep Learning with Python by Francois Chollet is also a great book to explore computer vision. It also has two parts, one dealing with machine learning and other with a practical deep learning part containing all the exercises and projects. 


Machine Learning- A Probabilistic Perspective 

This is a book that enhances computer vision skills to a great extent. It encapsulates mathematical as well as theoretical skills with advanced machine learning techniques. 


4 Personal Projects 

One of the best ways to learn machine learning or computer vision skills is to do. This can help in getting familiar with the stages in the machine learning projects. It also creates projects for an online portfolio to showcase to get better opportunities. And lastly to get an understanding of the concepts better. 


5 Websites 

Most popular libraries for ML and DL such as TensorFlow and PyTorch have good vision solutions and implementation for common tasks. The website tutorials on pose estimation, face detection, image classification, transfer learning and many more. This can help you gain a wide knowledge to explore different ways to solve real-world problems.