The certification course on Computer Vision is a self-paced online course. This gives you complete freedom about your schedule and convenience.This course has over 50+ hours of self-paced learning.
Additionally, this course comes with our exclusive lab access to gain the much needed hands-on experience to solve the real-world problems.
Upon successfully completing the course, you will get the certificate from CloudxLab which you can use for progressing in your career and finding better opportunities.
CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable & future-ready.
Churn the mail activity from various individuals in an open source project development team.
Classify images from the Fashion MNIST dataset using Tensorflow 2, Matplotlib, and Python
Learn how to train a neural network from scratch to classify data using TensorFlow 2, and how to use the weights of an already trained model to achieve classification to another set of data.
Create a custom loss function in Keras with TensorFlow 2 backend.
Learn how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2 to classify images.
In this project, you will build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python and Keras.
Learn how to read a pre-trained TensorFlow model for object detection using OpenCV.
Use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image using Neural Style Transfer.
Predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data, with Keras and Python.
Create a sentiment analysis model with the IMDB dataset using TensorFlow 2.
Learn how to practically implement the autoencoder, stacking an encoder and decoder using TensorFlow 2, and depict reconstructed output images by the autoencoder model using the Fashion MNIST dataset.
Learn how to deploy a deep learning model as a web application using the Flask framework.
Complete 100% of the topics of the course along with mandatory(non optional) projects
Highlight your skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.
“Sessions were great, pace was also very good. Each of the steps were explained well multiple times to ensure everyone understands the concepts. Thanks Sandeep!”
“Thanks a lot,it was great course! I'm happy that you lead in this path to AI/ML/DL.I hope to continue to collaborate with you in future.”
“Thank you so much Sandeep for all your great sessions. It will help in our career a lot. Your session is very much explanatory and understandable. Kudos to you.Thanks for all your hard work and time. Definitely, we will recommend all our friends and colleagues to attend your different course.Thanks a ton”
“I have been using CloudxLab for a while now, and they are amazing! The best part about using CloudxLab is that you do not need to wait for someone to tell you whether what you did was right or not, it is done automatically on the go. The training materials are of top notch quality. If you get stuck, they have a huge community of trainers and learners to help you out with all your doubts. They have a course structure for everyone, whether you are new to programming or are a seasoned programmer, they have something to offer you. And they are affordable too! I would recommend CloudxLab all the time.”
“This course is suitable for everyone. Me being a product manager had not done hands-on coding since quite some time. Python was completely new to me. However, Sandeep Giri gave us a crash course to Python and then introduced us to Machine Learning. Also, the CloudxLab’s environment was very useful to just log in and start practising coding and playing with things learnt. A good mix of theory and practical exercises and specifically the sequence of starting straight away with a project and then going deeper was a very good way of teaching. I would recommend this course to all.”
“It has been a wonderful learning experience with CXL. This is one of the courses that will probably stay with me for a significant amount of time. The platform provides a unique opportunity to try hands-on simultaneously with the coursework in an almost real-life coding example. Besides, learning to use algebra, tech system and Git is a good refresher for anyone planning to start or stay in technology. The course covers the depth and breadth of ML topics. I specifically like the MNIST example and the depth to which it goes in explaining each and every line of code. Would definitely recommend the instructor-led course.”
“This is one of the best-designed course, very informative and well paced. The killer feature of machine/deep learning coursed from CloudxLab is the live session with access to labs for hands-on practices! With that, it becomes easy following any discourse, even if one misses the live sessions(Read that as me!). Sandeep(course instructor) has loads of patience and his way of explaining things are just remarkable. I might have better comments to add here, once I learn more! Great Jobs guys!”
Senior Software Developer at Decision Resources Group
We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access to the course material so that you can refer to the course material anytime.
Yes, you can renew your subscription anytime. Please choose your desired plan for the lab and make payment to renew your subscription.
Course requires a good internet connection and a browser to watch videos and do hands-on the lab. We've configured all the tools in the lab so that you can focus on learning and practising in a real-world cluster.