Learn the Best Deep Learning Courses for a High-Paying Career in 2023

Here are some surveys and reports related to the deep learning job scenario from 2020 to 2023:

• According to a report by Grand View Research, the global deep-learning market size is expected to reach $10.2 billion by 2025, growing at a CAGR of 33.2% from 2020 to 2030.
   
• A survey by LinkedIn in 2020 showed that the top emerging job roles related to deep learning and AI in the USA included Machine Learning Engineer, AI Specialist, and Data Scientist.
   
• A report by ResearchAndMarkets.com stated that the deep learning market in Europe is expected to grow at a CAGR of 36.5% from 2020 to 2026, driven by the increasing demand for automation and robotics in various industries.

Overall, the surveys and reports suggest that the deep learning job scenario is growing rapidly and will continue to do so in the coming years, with increasing demand for skilled professionals and lucrative career opportunities in various industries.

The job scenario for deep learning professionals in India, the UK, and the USA is currently promising, with increasing demand for skilled individuals in various industries.

• India
  The demand in India for individuals with deep learning skills in areas like healthcare, banking, e-commerce, and others is rapidly increasing. "According to the data collected by The International Data Corporation, the AI market can touch up to 7.8$ billion in India by 2025," said Ms. Srividya Kannan, Director and Founder, Avaali Solutions. Popular deep learning job roles in India include Machine Learning Engineer, Data Scientist, AI Researcher, and NLP Scientist. In India, the typical salary for a Data Scientist with deep learning expertise is roughly ₹10-12 lakhs per year.
   
• UK
  Deep learning professionals are in major demand in the industries like banking, healthcare, and automotive in the UK. Deep learning job roles in the UK include Data Scientist, Machine Learning Engineer, and Computer Vision Engineer. According to Glassdoor, the typical income for a Data Scientist with deep learning abilities in the UK is roughly £61,976 per year.
   
• USA
  The job market for deep learning professionals in the USA is also expanding rapidly, with significant demand for qualified employees in a variety of businesses. Deep Learning Engineer, AI Researcher, Machine Learning Engineer, and Computer Vision Engineer are some of the most prevalent deep learning career roles in the USA. According to Indeed, the annual compensation for a Deep Learning Engineer in the USA is roughly $160,168.

Overall, the current work landscape for deep learning professionals is promising in all these countries, with a growing demand for experienced employees and good wage packages being offered by organizations in diverse industries.

Whether you are new to the subject or want to brush up on your knowledge, we offer courses to suit you. We have finely divided our Deep Learning courses into 3 levels: Beginner, Intermediate, and Advance. In addition to Dr. Andrew Ng's Deep Learning Specialization course, here are some of the best Deep Learning courses.

Beginner

Here are the best Deep Learning Courses for beginners:

• Deep Learning Nanodegree Program
  This course teaches you how to use PyTorch, which is a deep-learning framework for creating and implementing neural networks. Discover how to make image recognition convolutional networks, sequence generation recurrent networks, and image generation generative adversarial networks available via a website.
  • Importance
    This certification course, provided by Udacity, will assist students in expanding their skill set. The students will be able to learn some important industry-related skills that will help them stay in this field for a longer time. The course also provides job-related assistance and counseling. The mentors will lead the learning process and answer any of their questions. You can pursue this Deep Learning course online, which will provide a capstone project that is also included in the course to help applicants improve their expertise. The course provides tailored feedback to all students to help them improve.
     
  • Features
    • The duration of this course is 4 months.
    • It will be delivered online.
    • The language of delivery will be English.
    • The effort required for this course will be 10 hours per week.
    • Accessibility of this course will be on a Desktop/Laptop.
      Students who have a basic working knowledge of Python programming and are interested in machine learning, AI, or deep learning should apply for this program. It is quite user-friendly software, with the exception of Python.

• Deep Learning Inference with Azure ML Studio
  In this project-based course, you will learn how to train a neural system to recognize handwritten digits using Azure Machine Learning Studio's Multiclass Neural Network module. This course focuses on developing and deploying machine-learning models on Azure, and it makes use of the MNIST data set, which contains 70,000 grayscale photographs with handwritten numbers. The trained neural network model will be deployed as an Azure web service, acting as a conduit between your application and the Machine Learning Studio workflow scoring model.
  • Importance
    The course will teach you how to write Python applications that use the Batch Execution service and can predict the class labels for handwritten numbers. It is recommended that you take the first course in this series before proceeding, which teaches you how to create an Azure ML account and get $200 of credit for free to start your experiments. Coursera's Rhyme project platform allows you to take this course and provides instant access to pre-configured cloud desks containing all the data and software needed for your project. Note that access to the cloud desktop is limited to 5 times, but instruction videos can be accessed as many times as desired. This course is best suited for individuals living in North America, although the same experience is offered in other areas as well.
     
  • Features
    • The duration of this course is 1.5 hours.
    • The delivery mode is online.
    • The language used for delivery will be English.
    • The teaching type is self-paced.
    • This course is available with limited access.

• Deep Learning for Business
  AI is inside your smartphone, smartwatch, and car (if they are the latest models). It serves you daily. The future will see more sophisticated self-learning capable deep learning and machine learning technology being used in nearly every industry and aspect of your company. The present time with boosting the market of AI and machine learning is perfect to find out what deep learning and machine learning are and how you can use them to your advantage. This course is divided into three sections.
  • The first section discusses Deep Learning/Machine Learning technology-based business strategies. It provides information about reducing new services and products, and open-source deep learning software.
     
  • The second part discusses core technologies for Deep Learning/Machine Learning systems, including Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. 
     
  • The third part explains 4 TensorFlow Playground projects, to help you gain experience in designing deep learning neural networks using a simple but powerful TensorFlow Playground application. This course is designed to assist you in developing business strategies as well as technical planning for new deep learning/machine learning products and services.
     
  • Importance
    This course here will help you learn skills like Deep Learning, TensorFlow, AI, as well as Machine Learning.
     
  • Features
    • The duration of this course is 8 hours.
    • It is available for lifetime access.
    • It is accessible on Mobile, Desktop, and Laptop.
    • The effort required will be 1 hour per week.
    • The teaching type is self-paced.

Intermediate

Here are the best Deep Learning Courses for intermediate-level learners:

• Deep Learning with PyTorch
  This 2-hour-long course teaches you how to use PyTorch to implement neural style transfer. Neural style transfer is an optimization technique that takes a content image and a style picture and merges them so the output image looks exactly like the content image but is painted in the style of a specified style. We will create an artistic style image from the content and the given style image. We will calculate the style and content loss function. This loss function will be minimized using optimization techniques to create an artistic style image that preserves style and content features.
  
  This project is designed for those who are interested in learning how to use PyTorch to implement neural style transfer. To be successful in this guided assignment, you must be familiar with the theory of neural style transfer, Python programming, and convolutional neural networks.
  
  • Features
    • The duration of the course is 2 hours.
    • It is available with limited access.
    • The teaching type is self-paced.
    • Delivery mode is online, and the course will be delivered in English.
      
      In this course, you will be learning Image Processing, Deep Learning Applications, Convolutional Neural Networks, Python Programming, and Deep Learning. You will be able to understand Neural Style Transfer Practically and you will be able to showcase this hands-on experience in an interview and be able to create artistic style images by applying style transfer using PyTorch.

• Practical Deep Learning with Keras and Python
  This course is designed for individuals who want to learn Machine Learning without thorough mathematical knowledge, as well as for those who might have struggled with applying machine learning in real-life practical situations. The course begins with the basics of machine learning and quickly progresses to coding. It uses Keras as a tool to develop increasingly complex models such as Convolutional Neural Networks, Residual Connections, and Inception Modules. Each example includes starter code and finished code for reference.
  
  Students also have access to a real-time chat system via which they may connect with the instructor and other students to obtain assistance and collaborate on ideas. The course is designed to be highly practical, with a focus on code and then application and a little theory.
  
  • Features
    • The duration of the course is 3.37 hours.
    • It is available for lifetime access.
    • The teaching type is self-paced.
    • Delivery mode is online, and the course will be delivered in English.
      
      By the end of the course, you will learn the principles of Test-Driven Development, know what React Testing Library (RTL) and its difference from Enzyme (another popular test framework for React), understand how to Create React App (CRA) works, and understand how Jest works with RTL.

• Deep Learning Applications for Computer Vision
  This course here is offered for academic credit as part of CU Boulder's Master of Science degree in Data Science (MSDS), which is already available on Coursera. The MSDS is a cross-disciplinary program that brings together faculties from the departments of Applied Mathematics, Computer Science, and Information Science at CU Boulder. Those individuals with a wide range of degrees and/or experience in information science, computer science, statistics, and mathematics are welcome to apply for the MS-DS program.
  
  • Features
    • The duration of the course is 23 hours.
    • It is available with limited access.
    • The teaching type is self-paced.
    • Delivery mode is online, and the course will be delivered in English.
    • It is accessible on Mobile, Desktop, and Laptop.
      
      The learner will be able to define the method underlying classic algorithmic solutions to Computer Vision challenges and identify their merits and cons after completing this course. They will also be able to use hands-on modern machine learning techniques and Python libraries.

Advanced

Here are the best advanced Deep Learning Courses:

• Deep Learning on Azure with Python
  This course of machine learning is focused on reinforcement learning and its application in solving complex problems that require multiple decisions. Reinforcement learning acknowledges the intricate nature of machine learning challenges and provides a framework for exploring all possible paths through a series of decisions. By doing so, AI can determine the most efficient or effective solution to complex problems. The course will teach students how to use reinforcement learning with Python in Microsoft Azure, including framing relational learning issues, using common relational learning algorithms such as dynamic programming algorithms and temporal difference learning, and using Project Malmo, a Minecraft-based platform for AI experimentation.
  
  • Features
    • The duration of the course is 6 weeks.
    • It is available for lifetime access.
    • The teaching type is self-paced.
    • Delivery mode is online, and the course will be delivered in English.
    • It is accessible on Mobile and Desktop.
    • The effort required will be 5 hours per week.
      
      At the end of the course, students will have a thorough understanding of reinforcement learning strategies and formal notation, as well as practical experience using them to address challenging issues using Python programming and Microsoft Azure Cognitive Services.

• Post Graduate level Advanced Certification Course in Deep Learning
  Professionals can gain expertise in Deep Learning by completing the PG Level Advanced Certification Program (Foundations and Applications). This program covers everything you are going to need for knowing about Deep Learning. This weekend program is ideal for AI and Machine Learning professionals who have programming experience.
  
  This program provides a practical understanding of Machine Learning algorithms and how they can be improved for the hardware. These systems can be used for edge computing, where performance and power are important constraints. These interactive sessions contained in this course will provide an overview of the basics of deep learning, as well as its applications, including speech, image, and video processing.
  
  • Features
    • The duration of the course is 10 months.
    • It is available with limited access.
    • The teaching type is instructor-paced.
    • Delivery mode is online, and the course will be delivered in English.
    • It is accessible on Desktop and Laptop.
      
      The prerequisites/requirements for the course are that candidates must have completed their graduation with at least 50% marks and have relevant experience of 1 year. Additionally, programming knowledge is required.
      
      The course promises to provide academic and practical knowledge of the fast-evolving world of Deep Learning and allied areas. Upon completion, participants will receive a PG Level Advanced Certificate from IISc. The course will also provide hands-on experience through industry-oriented use cases and projects. Additionally, participants will have a lifelong professional association with Deep Learning practitioners in the industry.

• Professional Certificate in Deep Learning
  This is a series of courses that will introduce you to Deep Learning concepts and their applications. Additionally, it will introduce you to different neural network types that may be applied to both supervised and unsupervised learning. The last stage is to explore Deep Learning more and create models and algorithms using tools like PyTorch and Keras. Deep Learning will be possible with GPU-accelerated hardware. This includes image and video processing as well as object recognition in Computer Vision.
  
  Through this program, you will be able to practice your Deep Learning skills by getting involved in hands-on labs, assignments, as well as projects that are inspired by real problems and data sets from the industry. The program will also include a capstone project in Deep Learning that will show potential employers your applied skills.
  
  • Features
    • The duration of the course is 7 months.
    • It is available with limited access.
    • The teaching type is self-paced.
    • Delivery mode is online, and the course will be delivered in English.
    • It is accessible on Desktop and Laptop.
    • The effort required will be 4 hours per week.
      
      The course examines practical Deep Learning applications in areas like object recognition, Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other classifiers. Additionally, participants will learn how to employ accelerated hardware and GPUs for Deep Learning at scale, apply popular Deep Learning libraries like Keras, PyTorch, and TensorFlow to real-world issues, and obtain an IISc PG Level Advanced Certificate.

Deep learning is a fast-expanding technology. As more businesses incorporate AI and machine learning into their operations, there will be an increasing demand for qualified experts who can develop, deploy, and manage these systems. 

Many might think, “Is Deep Learning easy to learn?” It is, with the right online certifications! Getting a deep learning certificate might show potential employers that you have the knowledge and skills to excel in this industry. Here are some of the advantages of obtaining deep learning degrees in today's work market:

• Increased Employability: Companies are more willing to consider candidates who have proved deep learning proficiency through certification.
   
• Higher Salary Potential: Certified professionals are frequently paid more than their non-certified competitors.
   
• Competitive Advantage: A deep learning certificate can set you apart from other job hopefuls by proving that you have devoted time and effort to obtaining specific skills and knowledge.
   
• Continuous Learning: It demonstrates a dedication to continual learning and improvement, which is critical in a sector as fast-growing as AI and machine learning.
   
• Opportunities for Advancement: A deep learning credential can position you for these opportunities, whether you are searching for a promotion or new employment.

Below mentioned is a list of job profiles/career options available in India, the UK, and the USA for deep learning as of March 2023 along with their average salary package.

We must remember that the actual wage package will vary depending on a variety of aspects such as the firm, industry, location, experience, talents, and education. Also, these are only average wage ranges; certain individuals may be paid more or less than these ranges.

In conclusion, acquiring a deep learning certification can be very advantageous for anyone looking to pursue a career in the fields of AI and machine learning. It not only improves employability and earnings potential, but it also demonstrates a dedication to lifelong learning and development. This can be a critical aspect in setting individuals for future chances in a rapidly expanding subject like AI and machine learning. 

Dr. Andrew Ng, a top AI researcher and instructor, has underlined the significance of deep learning degrees in today's labor market. “A certificate is a way of signaling to potential employers that you have the abilities and expertise to excel in the field of AI and machine learning," he says. 

Moving ahead, it is indeed important to realize that obtaining a deep learning certification is just the start of a longer learning and growth process. It is also essential to stay up to date with new developments and technologies if you want to be competitive in the job market. Furthermore, the experience can supplement certification by providing individuals with significant skills and knowledge that can be applied in real-world scenarios. Individuals can position themselves for success in the field of AI and machine learning by combining certification with practical experience and continual learning.