Artificial Intelligence has accomplished its role as a futuristic concept to become a huge part of our everyday lives. We can notice that it has entered every field including the healthcare to finance, entertainment to transportation, AI's impact is profound.
As AI's reach expands, the demand for skilled professionals is soaring. And toady this demand has led to the proliferation of AI education opportunities, many of which are conveniently accessible online.
Online Courses for AI Learning
Online learning has changed the whole game of education. These courses are today offering flexible schedules and the ability to learn from experts around the world. One remarkable resource for AI courses is the website MLTUT, which curates a collection of the best free online courses for Machine Learning and AI. This comprehensive compilation focuses onto learners of various levels and backgrounds.
Talking more about these courses, they can guide you well with prompt engineering, and wont just give you the basic knowledge or basic understanding of any ethical issues but a vast idea of how and what is going on in the technical aspects.
You are now open to new ways, getting to know deep learning models as well as machine learning models for your business aspects or even just for some knowledge. Some of the courses need a few previous coding experience, while most of them will give a great advice from experts.
Speaking of experts, these courses are designed and are guided through by the most diligent industry experts and will give you the best and trusted verified certificates. Gain some of the best practical knowledge and have a solid understanding about the hands on projects, co relation between human intelligence and Artificial Intelligence, and know popular machines through these advanced courses.
The Notable AI Educator: Andrew Ng
When it comes to AI education, Andrew Ng‘s name has been standing tall. He is regarded as the pioneer in the field, where Ng offers a range of courses that have garnered global recognition.
His “Machine Learning” course is widely accepted as it provides a solid foundation for understanding the core concepts. For those looking out for Ng's “Deep Learning Specialization,” “Natural Language Processing Specialization,” and “Reinforcement Learning Specialization” let us tell you that these are the courses that offer advanced insights into these specialized domains.
Stanford University's AI Course
This is a more structured academic experience, as the Stanford University offers a great treasure trove of AI courses. Stanford's Artificial Intelligence course takes you on a tour into the deepest trenches of AI algorithms, applications, and implications. Also, the certificate of completion from such a prestigious institution adds significant weight to your AI skill set.
Harvard University's Machine Learning Course
Harvard University, which is another big name in the academia provides a top-notch “Machine Learning” course. Exploring the intricacies of machine learning, this course equips learners with a deep understanding of algorithms, predictive modeling, and data analytics.
15 Best AI Courses in 2023
Let's take a look at these courses that will surely boggle your mind and make you to attempt on these amazingly defined courses. Our list consists of a number of AI and ML-related courses that which we hope will solve your queries.
1. AI For Everyone by Andrew Ng
AI for Everyone is a course offered by Coursera and created by DeepLearning.AI, with Andrew Ng as the instructor. This course here aims to provide a comprehensive understanding of AI concepts, especially for non-technical individuals. It has already enrolled over 1,037,518 learners and has received a 4.8-star rating from 38,771 reviews.
Throughout the course, as a learner, you will explore the meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science. You can even learn what AI can and cannot do, how to spot opportunities to apply AI in their organizations, and how to work with an AI team and build an AI strategy in their companies.
The course takes merely 10 hours to complete, with a recommendation of 3 weeks and 3 hours per week. It offers a flexible schedule, allowing learners to study on their own. And the best thing about this is that when you complete the course, as a learner, you receive a shareable certificate that you can add to your LinkedIn profiles.
This course Artificial Intelligence Markup Language (AIML) is an XML-based language used for creating natural language software agents course. This will teach you about chatbots and virtual assistants.
AIML uses a combination of pattern-matching and template-based responses to create dynamic and engaging conversations with users. It is a hugely popular choice for creating chatbots as well as the virtual assistants because it is easy to learn, it is customizable, and it can integrate with a wide range of messaging platforms and other applications.
This is one course on Udemy, created by Steve Worswick, which teaches how to use AIML to create chatbots. The course is designed for people with no prior knowledge of AIML and covers everything from the basics to more advanced features. Students can learn at their own speed and practice with guidance from the instructor, who is a five-time holder of the Loebner Prize.
Artificial Intelligence: Preparing Your Career for AI is a course offered on Udemy, created by Matthew Renze, a data science consultant, author, and public speaker. This course here that we are talking about aims to help individuals prepare their careers for the coming wave of AI automation. It has a rating of 4.6 out of 5 and has been taken by over 13,000 students.
The course is designed to be accessible to a wide range of individuals, regardless of their technical background. When you take this course, you can learn about the impact of AI on various industries and job roles. This course also teaches you about how to adapt your career to gain the power of AI technologies. This will help you stay competitive in the job market and make the most of the opportunities presented by AI and automation.
To prepare for a career in AI, it is essential to educate yourself about the technology, along with its applications, and its ethical implications. One thing we would like to suggest you is that you should also consider upgrading your skills and knowledge to stay relevant in an AI-driven job market. This may involve learning programming languages, data science techniques, or other relevant skills.
Also, investing in an AI-first economy and using AI responsibly and ethically are crucial aspects of preparing for a career in AI. Finally, being adaptable and open to change will help you navigate the rapidly evolving landscape of AI and automation.
Udacity is offering a free course called “Intro to Artificial Intelligence” that aims to teach the fundamentals of AI. The course is specifically designed for intermediate learners and takes approximately four months to complete. This course covers various aspects of modern AI, including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.
The course is taught by industry experts Peter Norvig and Sebastian Thrun. It includes instructor videos, experiential activities, and interactive quizzes to enhance the learning experience.
Some of the topics that are covered during the span of the course are based on probability theory and linear algebra, so learners should have an understanding of probability theory comparable to that covered in Udacity's Intro to Statistics course.
We think that this should be a must on our huge and intriguing list. This course, Machine Learning for Trading is offered by Udacity in collaboration with the Georgia Institute of Technology.
The course aims to introduce students to the real-world challenges of implementing machine learning-based trading strategies, including the algorithmic steps from information gathering to market orders. It also focuses on applying probabilistic machine learning approaches to trading decisions, considering statistical approaches like linear regression, KNN, and regression trees, and how to apply them to actual stock trading situations.
The course has been designed for students who have really strong coding skills and some familiarity with equity markets. No prior experience in finance or machine learning is assumed. The course is primarily taught by using Python, with heavy use of numerical computing libraries like NumPy and Pandas.
WEKA, which stands for Waikato Environment for Knowledge Analysis, is an open-source machine-learning tool course developed at the University of Waikato in New Zealand. It is a collection of machine learning algorithms for data mining tasks, including data preparation, classification, regression, clustering, association rules mining, and visualization. WEKA is widely used for teaching, research, and industrial applications due to its comprehensive set of built-in tools for standard machine learning tasks.
The Udemy course here is a free tutorial that teaches students how to use the WEKA tool for data pre-processing, classification, regression, clustering, association rules, and visualization.
The course has got a rating of 4.4 out of 5 and has been taken by over 13,000 students. It consists of 3 hours and 30 minutes of on-demand video content and is created by DATAhill Solutions Srinivas Reddy. The course is available in English alongwith English auto-generated subtitles.
Machine Learning: Unsupervised Learning is another free course on our list that is offered by Udacity and is in collaboration with Georgia Institute of Technology. It is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS764. The course aims to teach students how to use unsupervised learning approaches, including randomized optimization, clustering, and feature selection and transformation, to find structure in unlabeled data.
Unsupervised learning is closely related to the patterns recognition and is an extremely powerful tool for identifying structure in data. It has got various applications, such as predicting movie preferences for platforms like Netflix or product recommendations on Amazon.
The course is specially designed for students with programming experience, as they will be expected to work with Python libraries such as NumPy and scikit-learn. A good grasp of probability and statistics is also required for this course, and Udacity's Intro to Statistics (Lessons 8, 9, and 10) may serve as a useful refresher. An introductory course like Udacity's Introduction to Artificial Intelligence can also provide helpful background knowledge for this course.
The “Welcome to Artificial Intelligence!” course on Udemy is actually a non-technical course specifically designed for AI/ML/DL aspirants.
It is known to provide the most important insights into the roadmap to AI and covers various topics related to artificial intelligence and machine learning. The course has a rating of 4.7 out of 5 and has been taken by 52,511 students. It is created by Vinoth Rathinam and is available in English with auto-generated English subtitles.
The course content includes, Basic Idea of Artificial Intelligence and Machine Learning, Prerequisites or Road map to start Machine learning project(ML), How to choose the best programming language for AI and How much Mathematical knowledge is needed for AI.
Along with all of that you can also learn about Which is the best AI Engine/Tool/Framework for AI, Why do we need to learn Algorithms, and Types of Machine Learning Algorithms with Real-time scenario examples.
This course mainly aims to provide a foundation for understanding AI and its applications, as well as guides its learners on how to start their journey in the field of artificial intelligence and machine learning.
Kickstart Artificial Intelligence is a practical hands-on course available on Udemy. The course is designed to help students apply Artificial Intelligence in real-world tasks and build fully functioning AI solutions on their own. It has a rating of 3.5 out of 5, with 304 ratings and 27,702 students enrolled. The course is created by Kashyap Murali and is available in English with English auto-generated subtitles.
This course here that we are talking about is known to offer a 3 hours and 5 minutes of on-demand video content, and upon completion, students will be able to apply AI in real-world tasks and build fully functioning AI solutions independently. The course is currently available for free, making it accessible to a wide range of learners.
Some of the key features of the course include online video content, a certificate of completion, instructor Q&A, and instructor direct messaging. By enrolling in this course, students can gain a solid foundation in AI and its applications, preparing them for further study or practical implementation in their careers.
CatBoost and XGBoost are both gradient boosting algorithms used for classification and regression tasks. They are popular for their high performance and ability to handle large datasets.
CatBoost (Category Boosting) is an open-source gradient boosting library developed by Yandex. It is designed to handle categorical features natively and provides state-of-the-art performance on many tabular data problems. A free tutorial on how to use CatBoost for classification and regression with Python is available on Udemy. The course on this topic is created by Manuel Amunategui that covers topics such as model training, cross-validation, and predicting, as well as useful features like early stopping, snapshot support, feature importances, and parameter tuning.
On the other hand, the XGBoost (eXtreme Gradient Boosting) is a popular gradient boosting library that has been dominating applied machine learning and Kaggle competitions for structured or tabular data. It is known for its high performance and scalability, making it suitable for large-scale machine learning tasks. A similar course is available for the learners that can take them on the high clouds of the related topics of XGBoost.
Artificial Intelligence (AI) has even entered the field of bioinformatics, which involves the analysis of biological data such as genome sequences, protein structures, and scientific publications.
AI techniques, including machine learning and deep learning, have been employed to solve some of the most challenging problems in bioinformatics, such as protein structure prediction, gene expression prediction, and identification of transposable elements.
AI-based technologies, such as deep learning architectures, natural language-based models, transfer learning techniques, and fusion-based techniques, have been instrumental in driving innovations in bioinformatics. However, the application of AI in bioinformatics can be expensive due to the need for large training datasets.
A course has been designed to cover all the related topics on an intermediate level which lasts for a mere 1 hour and 45 mins. The course has enrolled over 6,000 students and is highly regarded as the course that can level up your AI skills.
Artificial Intelligence (AI) is a machine's ability to perform cognitive functions that humans do, such as perceiving, learning, reasoning, and problem-solving. AI takes many forms, like machine learning, computer vision, natural language processing, and robotics. The field of AI research was founded as an academic discipline in 1956.
The course is offered by Udemy and has got a 4.1 out of 5 rating. When it comes to the timings of this course, it spans for at least 1 hour and 22 mins. This course is regarded as a beginners course and helps you explore the history and development of AI.
“Artificial Intelligence and Machine Learning Made Simple” is a non-technical course available on Udemy that aims to help individuals navigate the rise of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning. The course is designed for business leaders, managers, and CXOs, and it is applicable across various industries and roles. It is particularly useful for those who want to understand and make better decisions surrounding ML projects in a business environment.
The course covers various topics, including the history of ML, the capabilities and limitations of AI, types of ML systems, and how to apply AI and ML to your business. Upon completion, students will have a better understanding of AI applications and how to apply them to their businesses.
The course includes 44 minutes of on-demand video, access on mobile and TV, and a certificate of completion. There are no coding experience or prerequisites needed since this is a non-technical course.
The Machine Learning Specialization offered by Stanford University on Coursera is a beginner-friendly program that aims to teach the fundamentals of machine learning and how to apply these techniques to real-world AI applications. The specialization is taught by Andrew Ng, a renowned AI expert who has led critical research at Stanford University and has worked with Google Brain, Baidu, and Landing.AI.
The specialization consists of three courses and covers a broad range of topics in modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, etc.).
By the end of the specialization, learners will have mastered key concepts and gained practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. The program is designed for beginners, and no prior knowledge is required.
The Machine Learning Specialization has received positive reviews, with a 4.9 rating out of 5 based on 12,548 reviews. The course is estimated to take approximately 15 hours to complete and offers a flexible schedule, allowing learners to study at their own pace. Upon completion, students will receive a shareable certificate that can be added to their LinkedIn profile.
“Machine Learning for All” is a course offered by the University of London on Coursera. The course aims to provide a comprehensive understanding of modern machine learning technologies for beginners, with no previous experience necessary. The course is taught by Prof Marco Gillies and has received a rating of 4.7 out of 5 stars based on 3,232 reviews.
The course takes approximately 21 hours to complete, with a recommended pace of 3 weeks at 7 hours per week. It offers a flexible schedule, allowing learners to study at their own pace.
Throughout the course, students will learn the basics of how modern machine learning technologies work, how data affects the results of machine learning, and how to use a non-programming based platform to train a machine learning module using a dataset. Additionally, learners will be able to form an informed opinion on the benefits and dangers of machine learning to society.
Impact of AI on the Job Market 2023 and beyond
The World Economic Forum concluded that while AI would likely take away 85 million jobs globally by 2025, it would also generate 97 million new jobs in fields ranging from big data and machine learning to information security and digital marketing. AI is automating repetitive tasks, which can lead to wage stagnation or even wage decreases. However, it is also creating new job opportunities in emerging fields and industries.
AI's breadth and depth demand a comprehensive learning approach. The courses available cover a wide range of topics including machine learning, deep learning, natural language processing, and reinforcement learning. These comprehensive learning paths ensure that learners are equipped to tackle real-world challenges across industries.
A cornerstone of AI development is the ability to program and implement AI models. Python and R are the programming languages most commonly associated with AI and machine learning. Many AI courses, including those mentioned above, integrate hands-on coding exercises using these languages, allowing learners to translate theory into practice.
AI's impact extends beyond theory into real-world applications. Self-driving cars, a prime example, rely heavily on AI technologies. Dedicated courses catering to this specialized field provide insights into the unique challenges, opportunities, and algorithms that power autonomous vehicles.
AI education isn't solely about theory; hands-on experience is invaluable. Coding assignments and projects embedded within courses provide learners with practical skills and the ability to apply theoretical knowledge to solve real-world problems effectively.
Today when it comes to AI, staying up-to-date is essential. Whether you're stepping into AI for the first time or aiming to deepen your expertise. These courses that we have discussed above will widen your view. From Andrew Ng's renowned offerings to the structured programs of Stanford and Harvard, the opportunities are boundless. So, dive into the world of AI education, equip yourself with the latest skills, and prepare to shape the AI-powered future.