Free Deep Learning Courses & Certifications

The Georgia Institute of Technology, USA, offers the Reinforcement Learning online programme by Udacity. This four-month-long course aims to teach you important Reinforcement Learning (RL) concepts. You will study through a blend of recent papers and classic work in the area.

Throughout the Reinforcement Learning online training, you will learn about automated decision-making from the point of view of Computer Science. The course offers in-depth learning content taught by industry professionals. You will learn via interactive quizzes, video lectures and practical exercises.

The Reinforcement Learning syllabus covers a wide range of RL topics comprehensively. These include convergence, generalisation, game theory, Bellman equations, MDP (Markov Decision Process), among others. You will also explore efficient algorithms, multiagent and single-agent planning, and more. 

The Reinforcement Learning course by Udacity also describes Temporal Difference (TD) learning and related concepts. You will also reiterate a result from a published paper in RL at the end of this advanced online programme.

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Deep Reinforcement Learning Certification Courses

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.

There is no doubt that the AI revolution is being driven by deep learning. Machine learning and AI are gaining popularity as it helps make data-driven business decisions. With a deep understanding of AI & ML, you can take your business to an all-new level.  PyTorch makes it convenient to build applications used for deep learning. The programme will teach you how to build and train deep neural networks using Pytorch. This practical experience would hone your skills to use them in your personal projects. 

Intro to Deep Learning with PyTorch online course explores how to use PyTorch using coding exercises and projects. This will help you implement state-of-the-art AI applications such as text generation and style transfer.  This is a free course that offers instructional videos, interactive quizzes, and self-paced learning. 

Intro to Deep Learning with PyTorch takes about two weeks to complete. This is an intermediate-level course and requires a working knowledge of Python and its libraries Matplotlib and NumPy. The industry experts for this course are a panel of five educators: Matt Leonard, Soumith Chintala, Luis Serrano, Cezanne Camacho, and Alexis Cook. 

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.

The Deep Learning Essentials program by edX will help candidates discover the issues of discrimination and bias in Machine Learning and gain valuable insights regarding the subject. They will become adept at performing tasks like signal processing, computer vision, robotics, sequential decision-making, and natural language processing.

Besides, the University of Montreal offers the Deep Learning Essentials training which is designed by IVADO, a hub of scientific and economic data science and Mila. The scientific director of this course is Yoshua Bengio, a reputed expert in artificial intelligence and a pioneer in Deep Learning, who is also a professor at the University of Montreal.

Rife with video tutorials, tests, and assignments, the Deep Learning Essentials certification course also provides diverse learning tools to help candidates become adept in deep learning's fundamental concepts and methods. 

On successful course completion, candidates can also secure a verified certificate from edX to acknowledge their newly acquired knowledge in the field. The candidates may choose from any one of the tracks available namely verified and audit to complete the programme. Both the tracks are self-paced but during the audit track, the candidates shall receive limited period access to the course. On the other hand, the verified track will offer unlimited time for accessing the course.

The Applied Deep Learning Capstone Project certification programme is a five-week-long online course curated by IBM. During this capstone project, you will get practical, hands-on experience in developing, training and testing a Deep Learning model. For this purpose, you will be using either PyTorch or Keras.

edX’s Applied Deep Learning Capstone Project is an advanced-level programme that is also a part of a Deep Learning professional certificate of edX. With the help of this course, you will learn how to preprocess and load data for a real-life problem, create a model and finally validate it. 

The Applied Deep Learning Capstone Project programme by edX requires only two to four hours per week to study. You will learn through video lessons and ultimately master the procedure of generating a Deep Learning pipeline. At last, you are required to present a project report to showcase your competence in the domain of Deep Learning. EdX provides two tracks to pursue the self-paced course; verified and free audit. In the former mode, the learners can access the programme unlimited and get a certificate of completion. In the free mode, they can audit it for a limited duration. 

Training sophisticated Deep Learning models takes plenty of time. The Using GPUs to Scale and Speed-up Deep Learning certification programme sheds light on how accelerated hardware like Nvidia GPU (Graphics Processing Unit) or Google’s TPU (Tensor Processing Unit) can be used to shorten computations time on the cloud.

During edX’s Using GPUs to Scale and Speed-up Deep Learning course, you will understand what accelerated GPU-based hardware is and how it can help you overcome scalability challenges in Deep Learning. You will also get familiar with IBM’s Power Systems with Power AI and Nvidia GPU.

The Using GPUs to Scale and Speed-up Deep Learning will also acquaint you with popular Machine Learning dependencies and learnings supported by Power AI. These include Caffe, Theano, Tensorflow and Torch. Lastly, with this course, you will get practical experience of deploying Deep Learning networks on accelerated hardware for numerous problems, such as classifying videos and images.

The Deep Learning Fundamentals with Keras online course by edX is designed to familiarise you with the domain of Deep Learning. It is a five-week-long programme, during which you will learn about the basics of various Deep Learning models, the fundamentals of neural networks, and some of the most interesting Deep Learning applications.

Through the Deep Learning Fundamentals with Keras certification course, you will get practical experience, building a Deep Learning model with the powerful and easy-to-use Keras library. Also, you will develop an understanding of unsupervised and supervised Deep Learning models.

The Deep Learning Fundamentals with Keras certification syllabus is divided into four extensive modules. You are required to devote merely two to four hours every week to your studies.  As you progress across the modules, you acquire in-depth knowledge of how Keras is used to train, test and build Deep Learning models. Moreover, you will understand how neural networks learn. Upon completion, you can also get the verified certificate and earn a skill badge to showcase your acquired knowledge if you pursue it by paying free. For free, you can get a limited duration of access to 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.

Deep learning, also known as deep neural learning or deep neural network, is an artificial intelligence functionality that resembles the functioning of the human brain in digesting data and developing correlations used in decision making. Deep Learning and Computer Vision online certification is designed by Board Infinity, an online educational platform for job applicants and learners that will assist them in realizing their full potential to achieve their career goals. 

Deep Learning and Computer Vision online course focuses on providing a hands-on experience of the core concepts and principles associated with deep learning and computer vision. Deep Learning and Computer Vision online classes include a variety of data science topics, including natural language processing and neural networks, such as convolutional and recurrent neural networks. Candidates will have developed competencies in technologies such as OpenCV, Jupyter, Tensorflow, Python, Numpy, and others by the end of this course.

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.