#NoPayJan: CloudxLab tech courses free for 1 month

A LinkedIn survey carried out in 2020 found that more than 60% of professionals have increased the amount of time spent on online learning for upskilling during the first global lockdown.

Co-founder and managing director of upGrad, Mayank Kumar said, online learning is gaining traction because of two factors and one of them is people working from home have more time at their disposal.

He added that the other reason is spending cuts on travel, eating out and other activities have given individuals a savings buffer to spend on upskilling.

The survey also revealed that 60% of respondents were interested in content that can help them gain industry knowledge – and this includes tech professionals.

CloudxLab is an online cloud platform developed by a team of developers, researchers, and engineers who build fresh and lasting learning experiences for its users.

In an effort to provide upskilling opportunities to ensure that education does not feel like a luxury but a basic need that everybody is entitled to, CloudxLab has launched “#NoPayJan” where learners can access some of the most sought after and industry-relevant courses completely free of cost.

During #NoPayJan learners who sign up at CloudxLab will have free access to the contents of all the self-paced courses from January 1 till January 31, 2021.

The courses offered for free during this period are categorised by specialisation and cover topics on Data Science, Deep Learning, Machine Learning, Big Data, and Python.

Data Science Certification Specialisation

This top-selling course by CloudxLab has over 220 hours of learning and consists of five courses that include Big Data with Hadoop, Big Data with Spark, Python, Machine Learning, and Deep Learning.

This course comes with access to CloudxLab’s exclusive lab to help learners gain the much-needed hands-on experience to solve real-world problems.

Learners enrolled in this course will receive support from forums made up of an international community of peers that provide an avenue for problem resolution.

Machine Learning Specialisation

This specialisation is designed for learners who want to gain hands-on experience in solving real-life problems using machine learning and deep learning principles.

Upon completing this specialisation, learners are encouraged to develop creative solutions to help them become more effective in their roles.

This course is suitable for learners who are keen on developing robots that carry out tasks such as recognising faces or capable of recognising obstacles in their path.

The course is also suitable for learners who are keen on developing solutions such as revenue prediction, detecting fraudulent transactions, or even building a recommendation engine.

The specialisation consists of three courses that include Python for Machine Learning, Machine Learning, and Deep Learning.

Learners will also have an opportunity to undertake several projects including building a spam classifier, deploying machine learning models to production using Flask framework, working with Custom Loss Function, and even developing an NYSE Stock Closing Price Prediction system using TensorFlow 2 and Keras.

DevOps Specialisation

CloudxLab also offers DevOps certification training delivered by industry experts with over 17 years of experience in the field.

This specialisation which will be carried out by virtual classroom training covers DevOps tools such as GIT, Jenkins, Ansible, Docker, Kubernetes.

The courses offered aim to provide learners with a hands-on learning experience, helping them to become certified professionals able to handle real-time DevOps projects in any enterprise.

Learners enrolled in the DevOps specialisation will carry out a Taxi Stream Processing Project where they will build a fault-tolerant, scalable web service that calculates basic metrics over taxi data.

Deep Learning Specialisation

CloudxLab’s Deep Learning specialisation provides courses that help learners develop skills in deep neural networks, convolutional neural networks, recurrent neural networks, and autoencoders.

Learners will have the opportunity to select from various projects including projects that will have them classify images from the Fashion MNIST dataset using Tensorflow 2, Matplotlib, and Python, create a custom loss function in Keras with TensorFlow 2 backend and also build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python and Keras.