Free Deep Learning Courses & Certifications

Deep Learning for Visual Computing course’s aim is to educate the student about the stratified recognition task. This is a free course that covers topics including visual computing, ResNet, PyTorch, Python, GoogleNet, and more.

Swayam Deep Learning for Visual Computing is an appropriate option for people who want to become deep learning scientists, get into research, computer vision engineer, AI research scientist, etc. Join this course to make a career as a software engineer, deep learning engineer, IT artificial intelligence intern, research scientist, computer scientist, etc. 

The course type is ongoing and the length for this course is 12 weeks. There are no strict fee charges to enrol or study the Deep Learning for Visual Computing course, but for the certificate, the student needs to register and appear in an examination conducted in person at a chosen center. Regular assessments and one-on-one doubt clearing sessions are held by IIT-affiliated teachers.

Over the past few years, Deep Learning has received a lot of attention due to its wide-ranging applications in data analytics, natural language processing, and computer vision. Many professionals who are well versed in deep learning technologies have been employed by leading companies like Google, IBM, Facebook, Microsoft, and Twitter. In this Deep learning – IIT Ropar online training, you will get familiar with the building blocks of deep learning algorithms and solutions.

The Deep Learning – IIT Ropar course syllabus places a particular emphasis on neural network topics like attention mechanisms, convolutional neural networks, feed-forward neural networks, and recurrent neural networks. Various deep learning optimisation algorithms such as Nesterov Accelerated Gradient Descent, Gradient descent, Adam, AdaGrad, and RMSProp are also covered. Learning these algorithms and methods will help effectively train deep neural networks.

After completing all the lectures and assignments of the Deep Learning – IIT Ropar programme, you’ll have a robust grasp of deep learning architectures, which you can employ to carry out various computer vision and Natural Language Processing tasks. You can also test your understanding by applying for the final proctored exam. Passing this test will also grant you a certificate by Swayam, which can be used to display your skills and get lucrative jobs.

The availability of massive amounts of image and video data on the internet has made data interpretation and analysis a challenging affair. However, deep learning has proved to be an excellent solution to such a mammoth task. Moreover, deep learning is not only applicable to data analysis but also computer vision and natural language processing tasks. This Deep Learning - IITKGP course will help you get familiar with the concept and its applications.

The Deep Learning - IITKGP course syllabus starts with traditional machine learning methods like multilayer perceptron and Bayesian classification. Towards the end, the course will discuss modern deep learning approaches like autoencoders and convolutional neural networks. After completion, you will have the knowledge and confidence to take on challenging, real-world problems related to data analysis and automation.

By opting for the Deep Learning - IITKGP certification course, you also get industry support from Adobe, TCS, Google, and DRDO. Moreover, the course’s scheduled nature also provides you with enough motivation to study the modules and complete the course. After every module, you can find challenging assignments, which help you gauge your understanding.

The Fuzzy Logic and Neural Networks certification is a multidisciplinary course which can be pursued by graduate or postgraduate students. Students who decide to pursue this course will get to know about fuzzy clustering, fuzzy reasoning, neural network, etc. The programme does not have any limitations regarding the admission process and can be joined by the interested students throughout the year without having to pay any amount. The course is provided as well as mentored by the Indian Institute of Technology, Kharagpur. The teachings of the course are in online mode throughout the Fuzzy Logic and Neural Networks certification syllabus of the course.

The Fuzzy Logic and Neural Networks programme is conducted by the platform of Swayam which is a government-initiated platform and gives free courses to all the students across the country. The duration which is needed to complete the course is 2 months. Candidates can learn from the programme as per their schedules since this is a self-paced course. The online training authorizes the students to be a certified user of the programme by obtaining the certificate through the mode of an exam fee followed by the offline exam. The Fuzzy Logic and Neural Networks online course also provides interactive sessions through case studies and discussions which can be done through the discussion groups created for the user of the course.

Understanding deeply the technique of Deep learning in teaching which guarantees skills and knowledge for lifelong learning by using a range of contemporary digital technologies to enhance the learning, forms the main aim for the intermediate course on Deep learning through transformative pedagogy. This certification course is provided by edX, an online education provider in association with University of Queensland and Microsoft, both known for its educational and technological contributions and the course is taken by the professors from the institution. 

This self-paced Deep learning through transformative pedagogy online course with no required prerequisites comprises a curriculum which is specially curated for educators and education leaders but can be opted by anyone with a keen interest in learning and exploring the technique of deep learning.

The education & teacher training course mainly focuses on exploring deep learning and how it makes the students become more connective, collaborative and creative problem tacklers. Different powerful learning activities and teaching strategies are studied in order to facilitate deep learning which makes your students become innovative creators, effective communicators, and independent thinkers.

By taking up the Specialized Models: Time Series and Survival Analysis online course the students will be working on the ways to identify the modelling challenges that are common within the domain of time series data. The course will further be helping to understand the process of how trends are gauged and accessed in a time series of data. In the Specialized Models: Time Series and Survival Analysis certification syllabus the students will be covering chapters on- Identifying Nonstationarity, Common Transformations, Smoothing Moving Averages, Time Series Smoothing, Deep Learning Notebook, Survival Analysis and Censoring and others. The online course on Specialized Models: Time Series and Survival Analysis by Coursera will be elaborating on the domains of “time-series data” and will be working on its applications.

Fundamentals of Deep Learning certification course is created by Board Infinity, a learning system that aims to help individuals achieve excellence and achieve their professional goals. The Fundamentals of Deep Learning online classes by Board Infinity are for students who are looking for a thorough training program that will help them develop a solid understanding of the principles and practices of deep learning.

Fundamentals of Deep Learning online course is a self-paced learning program that includes more than 6 hours of detailed video lectures, as well as numerous projects, case studies, and assignments to give students a well-rounded learning experience. This course starts with a basic introduction to machine learning and then moves on to more advanced subjects like neural networks. Students will have a thorough grasp of how to use Python, Pandas, NumPy, Tensorflow, and Anaconda for a variety of deep learning tasks by the end of this training course.

Introduction to TensorFlow Lite Certification teaches how to deploy (DL) deep learning models on mobile and embedded devices with TensorFlow Lite. It includes various programming languages support such as Java, Swift, Objective-C, C++, and Python.

Introduction to TensorFlow Lite Classes is developed by the TensorFlow team and Udacity together as a practical approach to model deployment for software developers. All candidates get hands-on experience with the TensorFlow Lite framework as you deploy deep learning models on Android, iOS, and even an embedded Linux platform. 

By the end of this Introduction to TensorFlow Lite Training, all candidates will have the skills necessary to start deploying their own deep learning models into mobile apps.

Introduction to TensorFlow Lite Certification Course is designed for aspirants who want to learn TensorFlow Lite to execute efficiently on most devices with limited compute and memory resources. All students after completion of classes get Introduction to TensorFlow Lite Certification by Udacity.