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
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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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.
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.
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).
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.
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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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.
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.
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.
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:
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Online Communities:
- Stack Overflow
- Kaggle
- GitHub
Tools:
- TensorFlow
- PyTorch
- sci-kit-learn
- 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
- First, you need to learn the basics of Python and improve your grasp on linear algebra, calculus, and probability theory.
- Then, you need to explore fundamental AI concepts like machine learning, neural networks, and data preprocessing.
- After that, you can take up the course- “Python for Data Science and Machine Learning Bootcamp” on Udemy.
- You must also complete introductory courses on linear algebra, calculus, and probability theory available on reliable online platforms like Khan Academy/ MIT OpenCourseWare.
- After that, you can work on small AI projects using libraries like NumPy, Pandas, and Scikit-learn.
- You can implement basic ML algorithms like linear regression, logistic regression, and k-nearest neighbors on real datasets.
Intermediate-Level
- You can learn ML algorithms and techniques in greater depth. This will include learning concepts like ensemble methods, dimensionality reduction, and clustering.
- You can learn Deep Learning concepts like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
- ‘Deep Learning Specialization’ on Coursera is a good course for intermediate-level learners.
- You can start exploring specialized topics like natural language processing (NLP), computer vision, and reinforcement learning.
- You can then proceed by applying your learnings to more complex projects that involve deep learning models applied to real-world datasets.
- You can try using frameworks like TensorFlow or PyTorch to build and train advanced neural networks.
Advanced-Level
- 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.
- Then, you can learn advanced-level algorithms and research papers in your chosen specialization.
- 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.
- You can take up highly advanced level AI courses, or even try for a degree program at a good college/university.
- You can use your learnings to contribute to open-source AI projects on GitHub and other such platforms.
- You can participate in AI-related competitions/challenges on platforms like Kaggle, and apply your skills to real-world problems.
- 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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