AI is lagging far from replicating functions of human minds, but still lot of serious work is being getting done today. Its influence is increasing exponentially. The irony of all that promise is human minds are still lagging behind. Very few have a baseline understanding of how AI and deep learning works.
Machine learning, which supports many of the today's AI tools are not that easy to grasp. They feeds huge chunk of data to the computer to recognize certain pattern, human voice. This is very dissimilar to how the human brain learns as human brains involves techniques that can't be learnt without an effective teacher.
Best Courses in AI, Deep Learning, and Machine Learning
Luckily, AI's recent popularity has yielded hundreds of articles, videos, webinars, courses and books catering to beginners and experts who aspire to expand their minds. Below, we've curated a selection of the best available.
Fortunately, AI's recent popularity has yielded hundreds of articles, courses, webinars, and books, which caters to beginners and experts, who aspire to expand their knowledge in this field.
The MIT is one of the toughest technical university to get into and it routinely produces some of the best minds in this field. This introductory course is made up of 30 video lectures, starting from the very basic knowledge representation and includes very interactive demonstrations to help students understand how different AI models work under different circumstances.
A solid, non-technical approach to the most talked about AI technique (computer vision runs a close second). The focus is on the AI stars of the business world, from IBM's Jeopardy-winning Watson to LettuceBot, a deep learning system that assists in planting and growing everyone's favorite leaf vegetable. Some hands-on work using tools like Google's TensorFlow is included, but the focus remains squarely on what business leaders need to know.
A more advanced, three-month course that teaches students how to train and optimize different types of neural networks, and how to design systems that learn from massive datasets. This course is a good follow-up or alternative for those too advanced for Ng's deep learning courses.
Andrew Ng, a star both in AI and teaching, runs students through a more technical introduction to the fundamentals of deep learning and neural networks. The course is targeted to people with some technical proficiency, but also demonstrates how deep learning is relevant to business. Later courses in the series follow up with more in-depth material, such as Structuring Machine Learning Projects.
The Salesforce Einstein AI engine offers an interesting example of AI targeted to a particular business problem; supporting customers. While this course is too focused to serve as a general introduction to the field, it also offers a tradeoff; no coding is required to get some hands-on experience creating an AI-enabled app.
Computer vision is a distinct subspecialty within AI, important in everything from driverless cars, to augmented reality, to advanced manufacturing. This four-month course is not for beginners, but it does effectively teach the fundamentals and core concepts behind computer vision.
Learn how to speed up your AI, deep learning, and accelerated computing applications with more than a dozen project-based hands-on training courses. You will work through DLI training online from anywhere, using a fully configured GPU-accelerated workstation in the cloud. All you need is web browser and Internet connection. Examples include Deep Learning for Image Classification, which teaches how to train neural networks to recognize images, and Linear Classification with TensorFlow, which uses Google's extensive machine learning framework.
If the previous courses in deep learning look like child's play to you, this course is a good step up; it adopts a theoretical approach to machine learning, from classic papers on the topic to more recent work. This course will allow students to understand, engage and contribute to the reinforcement learning research community.