A bachelor’s degree usually lasts six semesters; master’s studies last four. But this is just an outline. I’ve seen people complete a BA in three semesters and some have completed nine semesters. Sometimes it is so many exciting courses that you volunteer longer to learn everything. That’s why I’ve loosely rebuilt the re-created curriculum into four semesters. The primary focus is on machine learning and in-depth learning.
Like fin the first year curriculum, I recommend completing AI for everyone. It is a low-level course and an ideal start to learning about general terminology, the features of artificial intelligence systems, the handling of ML projects, and ethical considerations. Although this is a non-technical course, I still suggest taking this course at an early stage. This way you get to the concepts and are well prepared for future classes. Alternatively, you can also do IBM Introduction to artificial intelligence a course with a similar focus.
Mathematics for machine learning
Machine learning is a field that requires math. While packages like TensorFlow and PyTorch make it easier to create, train, and deploy neural networks, you’re in a better position if you know the concepts behind them. The mathematics required is not rocket science, but is based on linear algebra and probability theory. In addition, when you familiarize yourself with custom datasets, you will often find yourself working with a statistical description.
Courses can make the learning process easier and easier. Take it Mathematics for machine learning specialization for example. The whole package costs you a small fee, but you can check individual courses for free. The curriculum covers discrete mathematics, graph theory, derivatives, linear algebra, and probability theory. The figures involve practical exercises pythonalso.
Stanford Machine learning the course provides a broad introduction to machine learning topics. It is taught by Andrew Ng, one of the most prominent figures in Deep Learning research. The course begins with supervised learning that includes both classical ML techniques and neural networks. Next, it covers unsupervised learning used in clustering algorithms, for example. Finally, it also discusses best practices in machine learning. Real world examples are involved in all of this. This course is available with or without certification, but without certification.
To get an in-depth introduction to deep learning, you can choose between three courses.
The first is CS230 Deep Learning and provided by Stanford University. The lecture videos are available for free and cover a wide range of topics. They include competitive attacks, interpretability, and reading papers.
Second course Deep specialization in learning, also taught by Andrew Ng, is more practice focused. In this course, you will actively encode neural networks, train them, and change hyperparameters. Covered networks include CNNs, LSTMs, and word embedding techniques. All this is done with the help of TensorFlow.
Third course Introduction to deep learning, offered by MIT. I heard this lecture last year and found it well done and thoughtful. It is taught by two doctors. researchers dealing with sequence modeling, computer vision, generative networks, and also reinforcing learning. In addition to this standard schedule, presentations will include discussions by invited speakers from Nvidia, Google, and similar companies.
Full stack of deep learning
Full stack of deep learning the course is offered by UC Berkeley as an official course, but all materials are available free of charge. To complete this course, you will need to bring experience in both python and model training. Lectures then focus on the production side of in-depth learning: project cost estimation, selection of computing infrastructure, and implementation of models on a scale.
The course begins with an examination of the fundamentals (CNNs, RNNs, transformers) and then deals with project management and experimental topics. This includes data management, monitoring, ethical considerations and teamwork. Most of these lectures are related to a hands-on laboratory lesson. If you don’t have time for a full lecture video or just want to look at certain parts, you can check out the detailed notes below each video.
Since the publication of “Generative Adversarial Nets” in 2014, generative techniques have become very popular. Many improvements to the original GAN structure have since been proposed, but most still rely on the idea of a two-player game.
In this setting, one player (one network) produces artificial samples. The quality of the samples is evaluated by another player (another network). This second player sees both actual and produced data samples and learns to distinguish them. With feedback from the second player, commonly referred to as the separator, the first player, called the generator, learns to produce more realistic samples. During the exercise, both networks will improve: The generator will create better artificial samples, and the separator – the contradictor (i.e. the name) – will be better able to detect counterfeit samples.
The course that teaches this is Specialization in a generative competitive network. In this course, you will learn the basics by building basic architectures and then improving your models. The entire specialization is subject to a fee, but you can check individual sub-courses for free.
Confirmation learning lecture is an educational collaboration between DeepMind and UCL and covers RL ‘s core technologies. Among these techniques are Markov’s decision-making processes, a fundamental way to model the environment. If you have such a model, you can use value functions to create an (optimal) practice. With this optimal practice, you and your representative can resolve the goal in the best possible way. If that sounds interesting, then go ahead playlist or attend an alternative lecture series (including DeepMind) here.
Natural language processing
There has been tremendous progress in dealing with natural language. Starting with simple, simple coded vectors for classification, we can now observe the widespread use of attention-based architectures. This progression has not happened overnight; there are a couple of fascinating intermediate stages.
Take embedding as an example. Instead of coding a word (or any text) as a single integer, you use context and world knowledge to add informative value to its presentation. By the term “tree” this means that we no longer encode it as 17 (an arbitrary index here), but represent it as a vector of floating values. This vector captures more information about the “tree”: That it cleans the air, gives protection to all kinds of animals, that it provides shade.
However, embedding is only one technique and is complemented (or replaced) by other techniques. For example, if you want more information about NLP, you can take Natural language processing specialization. It begins with a discussion of classical NLP techniques, progress to methods based on in-depth learning, and finally attention patterns. As with other specialties, you can check out individual courses for free to get a first impression.
The ImageNet dataset is still a popular benchmark for the ability to classify neural networks. It is often described as a crucial factor in the development of computer vision technologies. In 2010, when the ImageNet Large Scale Visual Recognition Challenge was first hosted, the classification accuracy was about 50%. Today, with 90% accuracy, we are a truly impressive step forward.
This progress was made possible by a few things:
- Model training is now more comfortable.
- Data processing is easier.
- Researchers used many image enhancement techniques over the years.
For more information on the basics, go to Basics of computer vision. Once you have completed this course, you can move on Advanced computer vision and TensorFlow course. This course covers image classification, object identification, segmentation, category activation maps, and more. see also TensorFlow Lucid dive deeper into neural networks for image-related tasks.
Machine learning and computer science are not solitary fields that stand apart from others. Instead, like other sciences, both open up their forces combined with real problems.
Think about the folding of proteins: Given the amino acid sequence, it is unclear how they fold into a three-dimensional structure. And proteins fulfill many important functions: they carry material, receive signals from the cell surface, are part of hormones.
Specialization in bioinformatics, offered by UC San Diego, covers this – and many more. It starts with DNA introduction, sequencing, genome comparison, and ends with genome sequencing from real data. It is a broad specialization that includes seven individual courses. I think running sequencing algorithms with computers makes this field so interesting to study.
Medical artificial intelligence
The previous course is bioinformatics. For this reason, we are now also dealing with medical information technology. Introduction to Health Care The course, offered free of charge by Stanford, is a good resource for researching the health care system. Although it focuses on the United States, most of the information is generally true. After all, you need medical people in any country.
Once you have an overview of the system, you can continue AI for medical specialization gain hands-on experience. In this three-part program, you will learn how to classify diseases on CNN, predict the likelihood of injury, extract data from health data, and evaluate the effectiveness of treatments.