Know Top 6 AI Courses You Need To Take Today

One of the technologies that has received the most attention recently is artificial intelligence (AI) AI Courses. AI has almost permeated every part of our life, from chatbots to self-driving cars, from personalised recommendation systems to voice and picture recognition. AI has thus grown to be one of the most in-demand abilities on the job market. In fact, the most sought-after career in the US is an AI specialist, according to LinkedIn’s 2020 Emerging Jobs Report. One must be ready to continually learn new things and refresh their abilities if they want to stay current in the rapidly changing field of AI. Here are the top 6 AI courses you should take right now if you want to stay competitive.

1. CHATGPT Prompt Engineering For Developers

Prompt engineering is one of an AI developer’s most crucial talents. The prompt, or input, that an AI model receives, has a significant impact on the calibre of its output. A course called ChatGPT Prompt Engineering for Developers is dedicated to teaching developers how to construct efficient prompts for NLP applications like chatbots and question-and-answer systems.

EleutherAI, a nonprofit with a mission to democratise access to AI research and education, is the provider of this course. quick design principles, quick augmentation, and prompt assessment are some of the subjects covered in the course. Additionally, it offers practical experience using cutting-edge language models like GPT-3.

The course needs a fundamental knowledge of Python programming and is self-paced. Access to the online course is completely free. In conclusion, ChatGPT Prompt Engineering for Developers is a fantastic course for developers who wish to grasp prompt engineering and produce more potent AI models.

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Course Benefits

Developers who successfully complete the AI Course ChatGPT Prompt Engineering for Developers gain access to a number of advantages. Among these advantages are:

Improved employability: Since AI is a fast expanding subject, there is a significant need for engineers with AI knowledge. Developers can raise their employability and possibility of being hired by top firms by completing the course.

Improved skills: The course gives developers the abilities they need to use AI to create intelligent systems. They may be able to improve their abilities and advance their AI knowledge as a result.

Building real-world AI applications requires actual experience, which the project-based learning model gives developers. Through doing so, they will be able to put their knowledge to use and acquire priceless experience that they can use at work.

Networking possibilities: The training gives developers the chance to meet other developers with a similar interest in AI. They may expand their network and meet like-minded people thanks to this.

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One of the pioneers of AI education, Andrew Ng, offers a course called AI For Everyone. The course is designed to give managers, business leaders, and anybody else interested in learning about AI a non-technical introduction to AI. The fundamentals of machine learning, deep learning, and neural networks are covered in the course.

The course is available online for free and is self-paced. There are four modules in the course, and it takes around four hours to finish each one. There are video lectures, tests, and practical tasks in the course.

Anyone who wants to learn about artificial intelligence without getting bogged down in the technical details should take the AI for Everyone course. People may better comprehend the influence of AI on different businesses by taking this course, which offers a wide overview of AI and its applications.

The course is divided into four parts, each of which focuses on a distinct element of AI. The first session covers the fundamentals of AI, outlining what it is, how it functions, and why it is significant. Additionally, it discusses the various subtypes of AI, including deep learning and machine learning, as well as how they are used in various fields.

The most widely applied AI approach, machine learning, is the topic of the second section. It covers several machine learning techniques, such as reinforcement learning, unsupervised learning, and supervised learning. The subject also covers the methods for developing and testing machine learning models, as well as the kinds of data that are employed.

Deep learning, a kind of machine learning used for difficult tasks like image identification and natural language processing, is covered in the third module. It describes the fundamentals of neural networks, which serve as the basis for deep learning models, as well as how they are trained and assessed.

The last subject covers ethical problems surrounding AI, such as those involving prejudice, privacy, and security. Along with the potential advantages and risks of AI, it also discusses how AI will affect society and the economy.

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Well-known computer science course Harvard CS50 includes a variety of areas, including AI. Students of all backgrounds and ability levels will learn the foundations of computer science in this course.

Computer vision, natural language processing, and machine learning are some of the subjects covered in the Harvard CS50 AI curriculum. Additionally, the course includes projects and assignments that require students to apply what they have learned through hands-on programming.

The course is available online for free and is self-paced. Each of the 12 weeks of the course covers a different computer science topic. The course’s final two weeks include the AI module.

People who want to learn the fundamentals of computer science and get an introduction to AI should take Harvard CS50. The course gives students a strong basis on which to build if they decide to enrol in more advanced AI courses.

Students with no prior experience in artificial intelligence or machine learning should be able to take the course. But students with a background in computer science will find that the course covers a lot of complex material and necessitates a high level of mathematical and coding competence.

How is the AI component structured?

The CS50 AI component is broken down into a number of modules, each of which focuses on a different aspect of AI. Deep learning, unsupervised learning, and supervised learning are all covered in the modules. Neural networks, reinforcement learning, and natural language processing are other subjects covered in the course.

A mix of lectures, problem sets, and programming assignments are used to teach the course. The theoretical foundations of AI and machine learning are covered in lectures given by Harvard faculty members. Students are able to apply the principles they learn in lectures to actual issues through problem sets and programming projects.

Why is the AI component of CS50 valuable?

The AI element of CS50 is useful for a number of reasons. It first gives students a thorough introduction to artificial intelligence (AI) and machine learning. The course provides students with a strong foundation in the subject by covering a wide variety of AI-related topics, from supervised learning to natural language processing.

Second, top authorities in the fields of AI and machine learning teach the AI component of CS50. Students get access to some of the most advanced and cutting-edge concepts and methodologies since the course is taught by Harvard academic members who are at the forefront of research in this area.

Third, the AI portion of CS50 is really fascinating and participatory. Students are required to apply the principles they learn in the lectures to actual issues through problem sets and programming assignments in the course. This practical approach aids students in grasping the subject matter thoroughly and recognising how it may be used in real-world situations.

Fourth, the industry holds CS50’s AI component in high regard. Employers respect CS50 education and are aware that students who have finished the course’s AI section have a strong foundation in the fields of artificial intelligence and machine learning.

Last but not least, the AI portion of CS50 is a component of a larger course that covers a wide range of computer science subjects. This implies that students who take the course receive a wide grasp of computer science as a whole in addition to a strong foundation in AI and machine learning.


Natural language processing (NLP) is the primary subject of Stanford CS 224N. Word embeddings, sequence models, and machine translation are some of the subjects covered in the course. Individuals with a background in probability theory, calculus, and linear algebra should take the course.

The master of science in computer science programme at Stanford offers the course, which is instructed by renowned NLP researcher Christopher Manning. The course comprises reading materials, programming exercises, and video lectures, all of which are free to access online.

Anyone interested in specialising in NLP should take Stanford CS 224N. The course gives a thorough overview of the subject and includes advanced NLP topics. Although the course is difficult, those who are willing to put forth the effort will find it rewarding.

Key topics covered in CS 224N

The following are some of the key topics covered in CS 224N:

  1. Deep learning and neural networks Backpropagation and gradient descent fundamentals are covered in the course’s opening lecture on neural networks and deep learning. Students gain knowledge of neural network construction and training for NLP applications including sentiment analysis and language modelling.
  2. A potent method for displaying words as vectors in a high-dimensional space is word embedding. The fundamentals of word embeddings are covered throughout the course, including Word2Vec and GloVe. In order to perform tasks like language modelling, sentiment analysis, and machine translation, students learn how to train and use word embeddings.
  3. Sequence modelling, first The course covers a variety of methods for modelling sequences, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. Sequence modelling is a fundamental topic in NLP. Students learn how to apply these methods to projects like sentiment analysis and machine translation.
  4. Mechanisms of Attention The course covers a variety of attention-based models, including the Transformer model, because attention mechanisms are a significant advancement in NLP. When performing tasks like text classification and machine translation, students learn how to use attention mechanisms.
  5. Language Development The course covers a variety of methods for creating text, including generative adversarial networks (GANs) and autoregressive models. Language production is a difficult problem in NLP.
  6. Students learn how to produce realistic text and use these methods to chatbots and language translation, among other applications.


One of the top AI research organisations, OpenAI, offers a course called Learn Prompting. The goal of the course is to instruct developers in the creation of excellent prompts for GPT-3 and other language models. Topics including prompt engineering, prompt tuning, and prompt debugging are covered throughout the course.

The course is available online for free and is self-paced. Video lectures, reading materials, and practical tasks are all included in the course. Additionally, the course offers access to OpenAI’s GPT-3 API, which enables programmers to test out various prompts and view the outcomes in real-time.

Developers who want to master the craft of prompt engineering and build more powerful AI models should take the Learn Prompting course. For those who are interested in working with cutting-edge language models, the course is demanding but also very rewarding.


Andrew Ng instructs a course on Coursera called Introduction to Machine Learning. A thorough introduction to machine learning will be given in this course. Topics including clustering, logistic regression, neural networks, and linear regression are covered throughout the course.

The course is available for free online access and is self-paced. There are video lectures, tests, and programming assignments in the course. People with a fundamental knowledge of mathematics and programming should be able to succeed in this course.

For those who wish to begin studying machine learning, Introduction to Machine Learning is a great course to take. The course equips students with a strong foundation in machine learning and gets them ready for more difficult courses.

Keeping up with the most recent advances is crucial for anyone who wish to excel in the continuously expanding field of artificial intelligence. From beginner courses to advanced courses in specialised fields like NLP and prompt engineering, the six courses mentioned above offer a wide spectrum of AI knowledge and abilities. Individuals can stay on top of developments and set themselves up for success in the rapidly expanding field of AI by enrolling in these courses.


What course should I take for AI?

The very minimum need for admittance into the field of artificial intelligence engineering is a bachelor’s degree in a related discipline, such as information technology, computer engineering, statistics, or data science.

Can I learn AI without coding?

The short answer is yes if you’ve been wondering whether it’s feasible to study AI without learning to code. There are various methods for someone without a background in coding to begin their path into artificial intelligence because there are so many different courses and resources online.

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Riya Kapoor

Riya Kapoor writes about lifestyle, entertainment, news and gadgets. She has been in this industry for almost 4 years now. She is a graduate from Delhi University with English Hons and had deep connection with writing since her childhood.

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