Data science and machine learning are now finding their way into almost every industry. The demand for highly qualified data scientists, engineers and visualisation experts can hardly be satisfied. - also at [at]. If you are thinking about getting a foothold in the field of data & AI, tackling exciting data-driven projects, working on cool use cases and exploring new technologies, this is the right place for you. It doesn't matter if you're just starting basic concepts of ML and working with data, or you can develop your Expertise you would like to expand your knowledge on certain topics: Here you can find our top 10 Machine Learning & Data Science courses. We provide an overview of the huge range of online learning content, sorted from beginner to expert. And the best thing about it: Our top 10 learning contents only cost you one thing: thinking power. Happy Learning!
Disclaimer: The course content of university courses is all freely available, usually even on different platforms: As videos, slides, or e.g. Coursera course.
Rank 10 - Elements of AI
The University of Helsinki and the company Reaktor have put together a course to demystify AI. With small, easy to understand content the Elements of AI course introduces the basics of Machine Learning (ML), Deep Learning and neural networks and their applications. The course serves as a solid introduction and is particularly suitable for for absolute beginners in the field of data & AI. In the first part, "Introduction to AI", no programming knowledge is required. The second part, "Building AI", requires basic Python programming skills so that you can start developing your first machine learning application and learn the methods in practice.
Rank 9 - Applied Data Science with Python
Those who would like to specialise specifically in Data Science and already have good Python knowledge You will learn how to use statistics, machine learning, visualisation, text analysis and social network analysis in practice. The course builds on popular Python toolkits such as pandas, matplotlib, scikit-learn and nltk. The course consists of 5 parts that teach how to perform various data science tasks in Python. For Beginners in the field of Data Science the course is optimal, because here "hands-on"and you can quickly gain your first experience with the help of your own implementations.
Rank 8 - Machine Learning by Stanford
The course "Machine Learning by Stanford" is probably one of the most popular courses on ML. The course is taught by the former head of Google Brain and founder of the online learning platform Coursera and offers a comprehensive introduction to logistic regression methods, ML and deep learning. Using numerous examples, Andrew Ng teaches best practices and methods for implementing machine learning in various domains. The course is Suitable for interested parties with general programming and statistical knowledge. But watch out: Not for absolute beginners - a little mathematical and previous programming knowledge should be brought along.
Rank 7 - Stanford CS229
Now it's down to the nitty gritty: Those with a mathematics background will notice that some courses do not fully cover the concepts behind ML. This course is different. In Stanford CS229 you will learn Many known algorithms and the mathematical concepts behind it and gain deep insights into how ML works down to the last detail. A lot of things may be difficult to grasp at first, but it's worth it: If you have a sophisticated knowledge about how different ML technologies work, can use his knowledge unerringly in the end and develop good ML models.
6th place - MIT 6.S191 Introduction to Deep Learning
Deep Learning Dive: If you want to dive deeper into the topic of Deep Learning and learn more about the enormous amount of potential use cases such as autonomous driving, NLP, computer vision and more, this is the place to be. The course deals with the Practical applications of Deep Learning and explains how neural networks, reinforced learning and transformers work in detail. Those who already know the basics of ML and have their Deepen knowledge would like to attend, should join directly. All lectures are also available on Youtube.
5th place - Neural Networks for NLP CMU CS 11-747
NLP experts and interested parties take note: Neural networks offer powerful tools for modelling language. This course shows how neural networks can be applied to natural language processing problems for advanced ML enthusiasts. The learning content covers useful techniques in the creation of neural network models including handling sentences of different length and structure, managing large amounts of data, semi- and unsupervised learning as well as structured prediction and multilingual learning.. For anyone specifically interested in NLPthis course is a must!
Place 4 - Stanford CS321n
Computer vision has become ubiquitous in our society, with applications in many fields: search, image understanding, apps, mapping, medicine, drones and self-driving cars - computer vision is used everywhere. At the heart of many of these applications are visual recognition tasks such as Image classification, localisation and recognition. Recent developments in neural networks (also known as "deep learning") have greatly improved the performance of these state-of-the-art visual recognition systems. This course is a deep dive into the details of deep learning architectures with a focus on the Learning end-to-end models for these tasks, especially image classification. Delivered by renowned computer vision expert Dr Fei-Fei Li, this course provides a good foundation for all computer vision experts-to-be.
Since neural networks have been very successful in practice lately, it is worthwhile to deal with them if you are already familiar with the basic concepts of ML. The course gives you a short introduction to the Practical application of neural networks. These have rapidly gained popularity in recent years and are now at the heart of production systems in major tech companies such as Google, Apple and Facebook. Anyone who wants a Overview of the basic ideas and the latest advances in the field of neural networks should definitely take a look here.
Place 2 - CS 228 Probabilistic Graphical Models
Probalistic graphical models are a powerful framework for representing complex domains using probability distributions and have numerous applications in machine learning, computer vision and NLP. The course covers the principles of Bayesian networks, interference models and the estimation and structure of graphical models. This course is suitable for advanced learners in the field of ML as well as statistics and imparts interesting advanced ML knowledge. What are probalistic graphical models anyway? It's not so easy to explain - so do some research!
Number one? When it comes to machine learning, Andrew Ng is one of the pioneers in teaching. With more than 40,000 participants, this course is one of the most famous teaching pieces for beginners in the field of ML and Data Science. Andrew Ng shows how to create, train, and optimise ML models with many best practices and valuable practical tips. As up-to-date version of his earlier ML course, this specialisation offers good, easy-to-understand content for anyone who wants to get serious about ML and Data Science.