Where should I start learning AI

Artificial Intelligence: In these online courses you will learn everything about AI (and how to program AI)

2. Why is AI important right now? Why the hype?

Various developments mean that the importance of artificial intelligence has increased enormously:

  • Technology progress: The programming of machine learning / deep learning systems with artificial neural networks is now well advanced.
  • Amount of data: In many areas, incredibly large amounts of data (big data) are available for training purposes of the AI ​​systems (e.g. image data, voice data, search queries).
  • Computing power: The computing power for processing the large amounts of data is now available.
  • Relevant applications: AI applications for the mass market are becoming possible, for example:

- Internet search (Google RankBrain)

- Email spam filter

- Voice recognition (Siri, Amazon Echo / Alexa, Google Home)

- Automated translation (Google Translate)

- Image recognition (for example in radiology or face recognition)

- Robotics and self-driving cars (combine AI technology and mechanical components).

The AI ​​machines are already human experts in many areas far superior. Reports about the successes in chess and poker, in lip reading and mind reading prove this. Even the first pieces of music and paintings can already be created independently by computer systems. And even chatbots can now sound amazingly natural.

According to experts, Artificial Intelligence will be in the next few years many areas of life and reach industries. Serious critics like Professor Russel from UC Berkeley therefore even think about one possible future threat by "super-intelligent" robots (video lecture).

Lots Jobs and projects at companies and organizations are currently emerging in the development and application of corresponding software programs. IT experts with relevant knowledge are of course in great demand.

The AI ​​expert from Google / Baidu says: "Artificial Intelligence is the New Electricity" (from 2:30):

(from minute 2:30; neural networks from minute 25:30)

3. How can you learn more about AI programming?

In the following, we will introduce you to books and online courses that primarily focus on the programming by AI systems (some are even free). These are mostly intended for computer science students or software developers who want to continue their education. But also other interested parties can of course gain an insight here!

(Note: Basic knowledge in individual areas is sometimes required. If there is a lot of catching up to do, you can find corresponding video courses in our directory, for example on math for machine learning or coding skills in the Python programming language.)

A) Introductory Books to AI

Even if they do not replace a university seminar or an online course: textbooks can be a useful tool Supplement and learning aid for every AI learner. Here are some well-known books:

  • Artificial Intelligence: A Modern Approach: A classic textbook by the well-known Professors Russel and Norvig. The functionality of an "intelligent agent" is introduced in detail on around 1,000 pages. At the technical level of the first stage of study.
  • Deep Learning: Gives a comprehensive explanation of machine learning with artificial neural networks for students and practitioners. Also contains sections on the required knowledge of linear algebra and statistics.
  • Basic course in Artificial Intelligence: German introductory textbook, slightly shorter than the classic by Russel / Norvig. Intended for computer science students and for self-study.

B) Online courses on Artificial Intelligence

Introductory courses for beginners

The basic courses are often created for students or programmers who want to get closer to the field of AI. The mechanics of artificial intelligent systems are explained here step by step. Some of the courses can free can be learned, only an (optional) certificate then has to be paid for.

  • One Beginners course about artificial intelligence is available in the MOOC AI For Everyone, which was created by one of the most famous AI experts from the USA. The specialty of the course is that it was designed not only for programmers, but also for business users and other interested parties.
  • In the well-known basic course Intro to Artificial Intelligence from the provider Udacity, participants also receive one broad overview on the subject. Many important areas are explained, starting with the statistical relationships and the functioning of Artificial Neural Networks through to image processing, speech recognition and robotics. Participation is free, but basic knowledge in statistics and linear algebra should be brought along.
  • A basic understanding of AI is also conveyed in the AI ​​course at Columbia University (via edX). Since here already too practical AI problems prior knowledge of Python is beneficial. Learning in the course is free, an optional certificate can also be purchased.
  • The German machine learning complete course is a right one practice-oriented course at Udemy. Here, for a course fee, it is shown in more than 100 short video lessons how to program an artificial intelligence yourself.
  • Machine learning from Stanford University is one of the most famous introductory courses in the subject. AI expert Andrew Ng was responsible for this area at Google and Baidu (and also founded Coursera). Participation in the course free of charge (without a certificate) is possible, the necessary algebra basics are included in the course.
  • Google also offers a free machine learning crash course. The course was already from good 18,000 Google employees documents and of course also contains an introduction to Google's own TensorFlow programs.
  • A free Online lecture in Germanis available from the University of Erlangen-Nürnberg. Since this is only the live recorded lecture, no exams or certificates are possible.
  • It is also worth mentioning Elements of AI of the University of Helsinki. Although this is not a video-based online course (only texts / links), it also provides an overview of the topic. The German version is supported by the IHK.

Longer series of courses for starting your career in AI

The paid course series usually consist of approx. 4-6 individual Online courses (MOOCs) and prepare you comprehensively for an activity in this area. Anyone who takes part here should, in addition to studying or working forsome months plan some time. In return, if successful, there are also reputable employers Certificates. ("What do the certificates bring?")

Columbia University's MOOC series contains 4 courses. In addition to the basics, machine learning skills and robotics also play a role. The program contains around 1/4 of the content from the Master's degree Computer science from Columbia.

This series of courses represents a comprehensive training in the field of artificial intelligence, which is intended to enable immediate subsequent practical work. The courses and practical projects were with Industry partners such as IBM and Amazon created, Udacity's Nanodegree certificate is well known among industry experts. Duration: approx. 6 months.

The course sequence by the world-famous AI expert Andrew Ng gives a detailed introduction to the most important sub-area of ​​machine learning. The video courses and projects can be completed within a few months. However, prior coding knowledge is advantageous.

Other interesting AI courses

  • A German-language overview of the Difference between AI and conventional programs is available in this course at openHPI;
  • A Python boot camp for ML applications, participants in this online course will receive from Udemy;
  • A Robotics training from the University of Pennsylvania (4 individual MOOCs) are included in this course series;
  • All details on the AI ​​application field of the Self-driving cars are taught to learners in these Nanodegree courses (created with Daimler, BWM and Uber);
  • How Recommendation services (Recommender Systems) can be programmed using ML, this Coursera specialization shows;
  • The Deep learning As a special feature of the machine learning concept explain both this free video course, which was developed by Udacity together with Google AI experts, as well as these free courses from a course platform operated by IBM; the advanced Deep reinforcement learning-Concept is the content of this course series;
  • They are central to modern machine learning systems artificial neural networkswho are at the heart of this Coursera MOOC;
  • Like ML models in the Google Cloud with the open source platform Tensorflow can be implemented is shown in this short video course;
  • A first non-technical introduction to AI gives this course at edX (English); a series of courses from IBM examines the Business applicationsof AI;
  • An overview of various AI fields of application in industry (e.g. at SAP, PwC, Amazon and Munich Re) there is also this German MOOC (in the last part of the course);
  • Anyone who first uses the Function of the human mind want to inform can do so with this MIT video lecture (free of charge), which moves on the border between computer science and philosophy. The Minds and Machines online course covers similar topics. The human brain is also the subject of this video lecture at the LMU Munich. There is also a (fee-based) course on human intelligence from the ZEIT Academy.
  • Selected aspects of the Artificial Intelligence Philosophy MIT researcher Lex Fridman discusses with Gary Kasparov, among others, who talks about the moment when he had to acknowledge the superiority of the AI ​​chess computer.
  • If you prefer to use the Face-to-face seminar learns, can watch the AI ​​seminars of the Bitkom Academy (together with DFKI).

An expert from the Fraunhofer Institute gives one in this interview brief overview of AI:

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