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Artificial Intelligence is probably the most revolutionary advancement of the modern age. Through AI, we are creating machines that can think like us. But these machines are far more capable than us humans. They can process large amounts of data, something that is impossible for our species to do.

Machine Learning is a subset of AI. It is the science through which computers are taught to learn and behave like humans. It is surprisingly common for us today to converse with Alexa or Google Assistant as if they were human beings like us. They process large amounts of data and use it to deliver the most accurate results, and also converse with us in the most humane way possible. The same is true for Chatgpt and how it has been a tremendous success in this field. 

This introduction might have made you understand how promising the future is for AI and Machine Learning. So now, let’s talk about how Udemy can help you learn AI and its related fields.

Why Choose Udemy for Learning AI?

I have a deep interest in AI and Machine Learning, which is why I’ve been extensively researching online for the best courses in this field. Out of all the platforms I’ve looked at, Udemy offers some of the best courses in this area. 

It delivers top-class courses on deep learning, machine learning, and other AI technologies. Through these courses, you can learn how to work with or build an artificial intelligence network.

How to Select the Right AI Course on Udemy?

Each of these courses caters to specific learning needs and career goals. Moreover, each one has a different difficulty level. While searching for the right course, it is important that you look for course structure and objectives. 

I have taken all these factors into consideration and curated a list of the top AI courses on Udemy to save you some time and effort.

Top 10 Recommended Artificial Intelligence Courses on Udemy for 2024 

  • Artificial Intelligence A-Z 2024: Build 7 AI + LLM & ChatGPT 

This course will teach you how to use data science, machine learning, and deep learning together to create a powerful AI that has real-world applications.

The topics covered in this course are- Fundamentals of Reinforcement Learning, Deep Q- Learning, Deep Convolutional Q-Learning, A3C, PPO and SAC, Intro to Large Language Models (LLMs), Artificial Neural Networks, and Convolutional Neural Networks. 

This course caters to anyone interested in AI, Machine Learning or Deep Learning. But you should have a grasp on high school level Math, and a basic knowledge of Python to go ahead with this course.

This course offers- 15.5 hours of on-demand video, Mobile and TV access, 19 articles, and 12 downloadable resources. By the end of it, you’ll receive a Certificate of completion.

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  • Machine Learning for Absolute Beginners: Level 1                                                                                        

This course will teach you the fundamentals of Artificial Intelligence and Machine Learning from scratch.

As the name suggests, it will teach you the fundamentals of AI, Machine Learning, and Deep Learning. You’ll learn about Features, Labels, Examples, Under-fitting and Over-fitting, Classification and Regression, Reinforcement Learning, Applied vs Generalized AI, The Process of Training a Model, Supervised/Unsupervised Learning, and Clustering and Dimension Reduction. After the completion of this particular course, you can move to Level 2 and 3, which are created by the same instructor, under the same name. 

You don’t need any prior qualifications in AI to start this course. This has been created for absolute beginners.

It offers- 2 hours of on-demand video, 2 articles, 1 downloadable resource, Mobile and TV access, and a Certificate of completion.

  • Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize

This course will teach you how to create deep-learning models in Python. It covers Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Self Organizing Maps, Boltzmann Machines, AutoEncoders, and Machine Learning Basics (Regression & Classification Intuition, Data Preprocessing, Data Preprocessing in Python, and Logistic Regression).

For this course, you must know high school mathematics and basic Python language. Your instructors for this course will be two machine learning and data science experts. Moreover, code templates are included in the course. 

This course will offer you 22.5 hours of on-demand video, Mobile and TV access, 34 articles, 3 downloadable resources, and a Certificate of completion. It is the best-selling course in Deep Learning.

  • Generative AI – Natural Language Processing Bootcamp 2024

This course will offer you Generative AI mastery with NLP LLM. You’ll learn how to create Chatbots, RASA, ChatGPT, BERT, and Transformers, and also receive Prompt Engineering mastery. 

The course curriculum is- Introduction to Natural Language Processing, Pipeline of NLP, NLP-Text Vectorization, Word Embeddings, End-to-End Pipeline for Text Classification, Information Extraction, Chatbots- Build with Google Cloud Service- Dialog Flow, Deep Dive into Dialog Systems (Chatbot), and Project- Build a Chatbot using RASA. 

For this course, you’ll require Access to Google Colab/Jupyter Notebook. You’ll also need basic to intermediate Python Programming Skills, and a GCP free trial account (this one is optional). 

  • Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Through this course, you’ll learn the methods to create Machine Learning Algorithms in Python and R. The course curriculum is- Data Preprocessing in Python and R, Regression, Classification, Clustering, Association Rule Learning, Reinforcement Learning, Natural Language Processing, Deep Learning, Dimensionality Reduction, and Model Selection and Boosting.

You only need knowledge of high school mathematics to understand this course. The good part is that this course includes updated coding exercises so that you can practice your skills while simultaneously learning. Your instructors for the course will be two Data Science experts. Code templates are included.

Moreover, you’ll get access to 42.5 hours of on-demand video, 5 coding exercises, 40 articles, 9 downloadable resources, Mobile and TV access, and a Certificate of completion. It is the best-selling course on Machine Learning.

  • The Complete Artificial Intelligence and ChatGPT Course

This course will teach you how to use ChatGPT, right from the basics, including more than 1000 prompts designed by the instructors. You’ll learn the fundamentals of ChatGPT alternatives- Microsoft Bing Chat and Google Bard, Machine Learning, how to create incredible images with the use of AI products DALL-E and Midjourney, and more.

The curriculum is- Introduction to Artificial Intelligence (Machine Learning, Deep Learning, and more), ChatGPT and its Plugins, Alternatives to ChatGPT (Bard and Bing), How to Use Excel and Machine Learning, Images and AI (DALL-E and Midjourney), Voice, Avatars and Cloning, Other AI Applications (Ralph AI, ChatBase, Character AI, etc.), and Using AI for Business Decisions. 

You won’t require any prior knowledge of Artificial Intelligence (AI) or any technical concepts. The only exception to this is the optional Section 15 where you’ll learn Open AI and APIs (Application Programming Interfaces).

The course contains 12 hours of on-demand video, 2 articles, 11 downloadable resources, Mobile and TV access, and a Certificate of completion. 

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  • Complete AI & Machine Learning, Data Science Bootcamp

This course will help you learn Data Science, Data Analysis, Machine Learning (AI), and Python with Tensorflow, Pandas & amp, and more. The curriculum comprises of Machine Learning, ML and Data Science Framework, Python and ML, Data Science Environment Setup, Pandas: Data Analysis, NumPy, Matplotlib: Plotting and Data Visualization, Scikit-learn: Creating Machine Learning Models, and Supervised Learning: Classification & Regression.

This course is extremely beginner-friendly. So much so that you won’t even need great skills in Maths and Statistics to follow through. You’ll need a computer (Windows/Mac/Linux) with internet access. 

The course will follow two different paths- one for those who know programming, and another for those who don’t. All the tools used in the course will be free for you to use. This course also includes updated coding exercises. 

  • Cutting-Edge AI: Deep Reinforcement Learning in Python

Through this course, you’ll learn how to apply Deep Learning to AI and Reinforcement Learning using evolution strategies, A2C, and DDPG.

The course curriculum contains a review of Fundamental Reinforcement Learning Concepts, A2C (Advantage Actor-Critic), DDPG (Deep Deterministic Policy Gradient), ES (Evolution Strategies), and some FAQ sessions. 

To follow the course instructions, you’ll need to know the basics of MDPs (Markov Decision Processes) and Reinforcement Learning. It’ll help if you’ll check out the first two Reinforcement Learning courses by the instructor of this course. 

The course includes 8.5 hours of on-demand video, Mobile and TV access, and a Certificate of completion.

  • AI for Business – AI Applications for Business Success

Want to learn the use of AI for business growth? This course will teach you how to leverage the power of artificial intelligence to solve your business problems and build strong business strategies.

The course curriculum comprises:- 

  • Business Goals: SWOT Analysis, SMART Goals, Limitations of the BI Approach, Correlation vs Causation, Making Recommendations with Descriptive Statistics.
  • Approaches to Solving the Business Objective: The BI Approach, State Space and Takens’ Theorem, Shadow Manifolds, and K-Nearest Neighbors.
  • Artificial Intelligence in Business: Quantifying Attainability, Gradient Boosted Machines Part 1,2,3, SHAP Values, Friedman’s H-Statistic, LIME, and more.
  • Artificial Intelligence Recommends Metrics: The Hybrid Experiment, Quantile Difference Tests.

This course requires a rudimentary knowledge of higher-level mathematics. You must also understand Python codes. The course includes- 2 hours of on-demand video, 2 articles, Mobile and TV access, and a Certificate of completion.

  •  Machine Learning Practical Workout | 8 Real-World Projects

This is a practice-based course in which you’ll build 8 practical projects on Deep Learning, Machine Learning, and Artificial Neural Networks. You’ll gain practical expertise in these areas after implementing your learning from all the previous courses, and then applying those to solve real-world troubles. 

To be able to complete these projects, you must know the basics of Machine Learning and Deep Learning. Also, you’ll require a PC with a stable internet connection. 

The perks of this course are- 14 hours of on-demand video, 6 articles, Mobile and TV access, and a Certificate of completion. 

Additional Resources to Excel Your AI Learning

Apart from these Udemy courses, you can consider good books, online communities, and tools to learn about AI and its related topics. 

I have meticulously found the resources to help you learn more about artificial intelligence. Here they are-

Books:

  1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
  2. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  3. Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
  4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Online Communities:

  1. Stack Overflow
  2. Kaggle
  3. GitHub

Tools:

  1. TensorFlow
  2. PyTorch
  3. sci-kit-learn
  4. Jupyter Notebook

Tips to Stay Updated on the Latest Artificial Intelligence Trends And Advancements

The best way to keep yourself updated on the latest trends and advancements in AI, on a regular basis, is to follow the works of industry leaders and researchers. You should also subscribe to newsletters and blogs related to AI. 

Learning Pathways: From Beginner to Expert

I am writing down a learning pathway for beginner, intermediate, and advanced-level AI learners. This pathway is in line with the different career goals and expertise levels. It offers continued learning and has real-world applications.

Beginner- Level

  1. First, you need to learn the basics of Python and improve your grasp on linear algebra, calculus, and probability theory.
  2. Then, you need to explore fundamental AI concepts like machine learning, neural networks, and data preprocessing.
  3. After that, you can take up the course- “Python for Data Science and Machine Learning Bootcamp” on Udemy.
  4. You must also complete introductory courses on linear algebra, calculus, and probability theory available on reliable online platforms like Khan Academy/ MIT OpenCourseWare.
  5. After that, you can work on small AI projects using libraries like NumPy, Pandas, and Scikit-learn.
  6. You can implement basic ML algorithms like linear regression, logistic regression, and k-nearest neighbors on real datasets. 

Intermediate-Level

  1. You can learn ML algorithms and techniques in greater depth. This will include learning concepts like ensemble methods, dimensionality reduction, and clustering.
  2. You can learn Deep Learning concepts like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
  3. ‘Deep Learning Specialization’ on Coursera is a good course for intermediate-level learners.
  4. You can start exploring specialized topics like natural language processing (NLP), computer vision, and reinforcement learning. 
  5. You can then proceed by applying your learnings to more complex projects that involve deep learning models applied to real-world datasets.
  6. You can try using frameworks like TensorFlow or PyTorch to build and train advanced neural networks.

Advanced-Level

  1. This is when you’ll have to choose a subset of AI to gain specialized knowledge in. You can choose specialized fields such as computer vision, NLP, or reinforcement learning.
  2. Then, you can learn advanced-level algorithms and research papers in your chosen specialization.
  3. You’ll have to stay updated with the latest advancements in AI, and for this, you can choose to read papers from top-tier conferences like NeurIPS, ICML, and CVPR. 
  4. You can take up highly advanced level AI courses, or even try for a degree program at a good college/university.
  5. You can use your learnings to contribute to open-source AI projects on GitHub and other such platforms.
  6. You can participate in AI-related competitions/challenges on platforms like Kaggle, and apply your skills to real-world problems.
  7. You can participate in conferences and workshops related to the latest developments in artificial intelligence. Also, this is when you can start networking with skilled and learned individuals from this field.

Conclusion

At the end, I would suggest you thoroughly go through the objectives and course material before selecting your Udemy course on AI. Make sure that the course you choose aligns with your own goals and objectives. 

This is the reason I have listed down the objectives, syllabus, and requirements of each of the above-mentioned courses. Each of these courses caters to a different set of students. Some of these are extremely beginner-friendly courses, some require a bit of grasp on certain areas, and some are meant for experts to practice and sharpen their skill set. 

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By Nikita Joshi

A creative advocate of multi-disciplinary learning ideology, Nikita believes that anything can be learned given proper interest and efforts. She completed her formal education in BSc Microbiology from the University of Delhi. Now proficiently dealing with content ideation and strategy, she's been a part of Coursevise since August 2023 working as a content writer Having worked with several other things during these two years, her primary fields of focus have been SEO, Google Analytics, Website Traffic, Copywriting, and PR Writing. Apart from all that work, Nikita likes to doodle and pen down her rhymes when she feels free.

4.8 /5
Based on 19 ratings

Reviewed by 19 users

    • 3 weeks ago

    How can I easily master AI?

      • 3 weeks ago

      Mastering AI is like mastering any skill – it takes time, practice, and dedication. Start by learning the basics of AI concepts and algorithms. Then, move to practical projects to apply the theory. Most importantly, don’t be afraid to make mistakes – they’re all part of the learning process.

    • 3 weeks ago

    Is it a good time to learn AI?

      • 3 weeks ago

      It’s a great time to learn AI. As AI advances, the demand is also increasing across industries and there are plenty of opportunities to explore and grow in this field. Whether you’re interested in machine learning, neural networks, or natural language processing, multiple courses are available to help you learn and succeed.

    • 3 weeks ago

    In which field AI is used the most?

      • 3 weeks ago

      AI is mostly used in fields like healthcare, finance, and technology. In healthcare, it helps with diagnosing diseases and developing personalized treatments. In finance, AI is used for fraud detection and financial analysis. Finally, in technology, AI powers virtual assistants and smart devices.

    • 3 weeks ago

    Can I self-learn AI?

      • 3 weeks ago

      Yes, you can self-learn AI. There are multiple resources available online (like tutorials, courses, and books), that can help you get started. So, you can start with the basics, and then move to the more advanced topics.

    • 1 month ago

    Q: How can I collaborate or network with other AI professionals and researchers?

      • 1 month ago

      Hey Raghavan, Collaboration and networking are essential in the AI field, as it fosters knowledge sharing, collaboration on projects, and potential career opportunities. Here are some ways to connect with the AI community:

      Attend conferences, meetups, or local AI events in your area.
      Join online communities, forums, or social media groups focused on AI.
      Participate in open-source AI projects on platforms like GitHub.
      Engage with AI professionals and researchers on platforms like LinkedIn or Twitter.
      Consider joining professional organizations or associations related to AI, such as the Association for the Advancement of Artificial Intelligence (AAAI) or the Institute of Electrical and Electronics Engineers (IEEE).
      From my personal experience, networking and collaborating with others in the AI field has been invaluable for learning, staying up-to-date, and finding new opportunities.

    • 1 month ago

    How can I get started with building an AI project-based portfolio? Will it help me with my interviews?

      • 1 month ago

      Hii Samiksha, A strong project-based portfolio reflects your AI skills and experience. Here is how you can work on building your AI portfolio:
      I would first recommend you watch online tutorials and take an online course to learn the fundamentals of AI and machine learning.
      Participate in coding challenges or competitions like those on Kaggle or HackerEarth.
      Work on personal projects or contribute to open-source AI projects on platforms like GitHub.
      Attend hackathons or coding events on AI and machine learning topics.
      Search for internships or freelance gigs in AI-related fields to gain practical experience. LinkedIn is a great place to look for such opportunities.

    • 1 month ago

    What kind of requirements, in terms of hardware and software are required for AI and machine learning?

      • 1 month ago

      Hii Vinay, the hardware and software requirements for AI and machine learning vary depending on the complexity of the tasks and the size of the datasets involved. In general, powerful GPUs (Graphics Processing Units) are essential for training deep learning models. Cloud computing services like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer scalable and cost-effective solutions for AI-related tasks. For personal projects or small-scale experiments, a decent CPU and GPU are sufficient. After reading a couple of articles by professionals working with AI, I’ve found that a decent power PC and storage are crucial for tackling large-scale AI problems.

    • 1 month ago

    Will I be able to learn everything about Artificial Intelligence if I don’t have a background in Computer Science or Mathematics?

      • 1 month ago

      Yes, there are many Artificial Intelligence courses on Udemy designed for beginners with no background in Computer Science and Mathematics. However, I would recommend you to study those Artificial Intelligence courses that also teach you the basics of Computer Science and Mathematics relevant for AI technologies.

      Here are some best-selling and beginner-friendly AI courses on Udemy –
      1. Artificial Intelligence for Business + ChatGPT Prize [2024]
      2. How to use Artificial Intelligence – A guide for everyone!
      3. Modern Artificial Intelligence with Zero Coding

    • 1 month ago

    What programming languages should I learn for Artificial Intelligence?

      • 1 month ago

      The most widely used programming languages for AI and machine learning are Python, R, and Java. Python is particularly popular due to its diverse libraries like TensorFlow, Keras, and PyTorch. However, the choice of language often depends on the specific AI application and your prior experience with these languages.

    • 1 month ago

    What are the major challenges and limitations of AI technologies?

      • 1 month ago

      While AI has made remarkable progress in recent times, there are still several challenges and limitations that we can’t overlook.

      1. Lack of General Intelligence: The current AI systems are best when it comes to automating daily tasks but they lack the human-like reasoning capabilities.
      2. Data Availability and Quality: AI models heavily rely on large, high-quality datasets, which can be difficult to source at times.
      3. Ethical and Societal Implications: AI raises concerns about privacy, bias, job displacement, and potential misuse, which require careful consideration and regulation.
      4. Computational Resources: Training advanced AI models can be computationally intensive and resource-heavy, limiting accessibility for some organizations or individuals.

    • 1 month ago

    How can I get started with building an AI project-based portfolio? Will it help me with my interviews?

      • 1 month ago

      A strong project-based portfolio reflects your AI skills and experience. Here is how you can work on building your AI portfolio:

      1. I would first recommend you watch online tutorials and take an online course to learn the fundamentals of AI and machine learning.
      2. Participate in coding challenges or competitions like those on Kaggle or HackerEarth.
      3. Work on personal projects or contribute to open-source AI projects on platforms like GitHub.
      4. Attend hackathons or coding events on AI and machine learning topics.
      5. Search for internships or freelance gigs in AI-related fields to gain practical experience. LinkedIn is a great place to look for such opportunities.

    • 1 month ago

    What kind of requirements, in terms of hardware and software are required for AI and machine learning?

      • 1 month ago

      The hardware and software requirements for AI and machine learning vary depending on the complexity of the tasks and the size of the datasets involved. In general, powerful GPUs (Graphics Processing Units) are essential for training deep learning models. Cloud computing services like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer scalable and cost-effective solutions for AI-related tasks. For personal projects or small-scale experiments, a decent CPU and GPU are sufficient. After reading a couple of articles by professionals working with AI, I’ve found that a decent power PC and storage are crucial for tackling large-scale AI problems.

    • 1 month ago

    How can I collaborate or network with other AI professionals and researchers?

      • 1 month ago

      Hii Sneha, collaboration and networking are essential in the AI field, as it fosters knowledge sharing, collaboration on projects, and potential career opportunities. Here are some ways to connect with the AI community:

      Attend conferences, meetups, or local AI events in your area.
      Join online communities, forums, or social media groups focused on AI.
      Participate in open-source AI projects on platforms like GitHub.
      Engage with AI professionals and researchers on platforms like LinkedIn or Twitter.
      Consider joining professional organizations or associations related to AI, such as the Association for the Advancement of Artificial Intelligence (AAAI) or the Institute of Electrical and Electronics Engineers (IEEE).
      From my personal experience, networking and collaborating with others in the AI field has been invaluable for learning, staying up-to-date, and finding new opportunities.

    • 1 month ago

    Can I get an AI job after an online course?

      • 1 month ago

      Hey Abhishek, Yes, completing an online AI course can lead to a job in the field, especially if you combine your learning with hands-on projects to showcase your skills.

    • 1 month ago

    Will I be able to learn everything about Artificial Intelligence if I don’t have a background in Computer Science or Mathematics?

      • 1 month ago

      Hii Saniya, Yes, there are many Artificial Intelligence courses on Udemy designed for beginners with no background in Computer Science and Mathematics. However, I would recommend you to study those Artificial Intelligence courses that also teach you the basics of Computer Science and Mathematics relevant for AI technologies.

    • 1 month ago

    How long does it typically take to learn AI?

      • 1 month ago

      Hey Naresh, it vary depending on your prior knowledge, learning pace, and desired depth of understanding. Generally, for a beginner to gain a solid grasp of AI fundamentals and basic applications, it may take several months to a year of dedicated study and practice. However, mastering advanced AI techniques and developing expertise can take years of continuous learning and hands-on experience.

    • 1 month ago

    Are there any ethical considerations or potential risks associated with AI?

      • 1 month ago

      Hey Neeta, Yes, the rapid development of AI technologies has raised various ethical concerns and potential risks. These include issues related to privacy, bias in AI systems, transparency and accountability, job displacement, and the potential for misuse or unintended consequences. As an AI practitioner, it’s crucial to be aware of these ethical implications and work towards developing responsible and trustworthy AI systems that prioritize human values and well-being.

    • 1 month ago

    How can I stay up-to-date with the latest advancements in AI?

      • 1 month ago

      Hii Kavya, the AI landscape is constantly evolving, with new developments and breakthroughs occurring regularly. To stay current, I recommend following reputable AI publications, attending industry conferences and webinars, participating in online forums and communities, and engaging with other professionals in the field. Additionally, continuing education through online courses or specialized certifications can help you acquire the latest skills and knowledge.

    • 2 months ago

    Does AI require coding?

      • 2 months ago

      AI often involves coding, but the amount of coding required can vary. Some AI tools and platforms allow people to create AI models without extensive coding knowledge, using visual interfaces and pre-built algorithms. However, building custom AI solutions or working on advanced AI research usually requires coding skills. So, while basic AI tasks can be done without much coding, more complex AI development and research typically involve coding to some extent.

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