Machine learning is one of the applications of artificial intelligence, the science that allows machines and systems to learn and improve based on their own experience, just like humans, without the need for explicit programming. The idea is to feed a machine (a computer) high-quality data, have it find patterns in the data, and then have it learn algorithms to make predictions and decisions based on that data. These algorithms and programs are designed to automatically improve as more data becomes available. You can know more about machine learning through any of the following machine learning certifications.
There are various machine learning qualifications that one can acquire to become a machine learning specialist. Here are some of them.
Stanford University's Certificate in Machine Learning, offered by Coursera, is by far the best machine learning course available online. More than 2.4 million students and professionals have taken it, and Coursera has a rating of 4.9 out of 5. In this machine learning course, you'll learn not only the theoretical foundations of effective machine learning methods but also the practical skills needed to adapt and apply these methods to new real-world problems. You'll also learn about data analysis and statistical pattern recognition.
This is one of the most popular deep learning courses offered on Coursera by Stanford University professors. This deep learning certification course has been taken by more than 225,000 people and has been very well received. The course is divided into five sections that cover the basics of deep learning, how to build neural networks, and how to complete a machine learning project. Coursera recommends spending about 11 hours per week, or about three months, to complete the program at this pace. The course is taught in Python and students should have basic programming skills. Basic knowledge of linear algebra is also recommended.
A typical undergraduate degree consists of a set of core courses, a set of electives, and a set of projects, and takes about four years to complete. Udacity's nano degree programs are similar to regular university degrees in that they include some core subjects and some electives, but the duration of the nano degree programs is very short, from 3 to 12 months, hence the name "nano."
Depending on your previous knowledge, you can choose between two programs: "Introduction to Machine Learning" and "Machine Learning Engineer". If you are new to the world of machine learning, the Nano program "Introduction to Machine Learning" is ideal for you. It's a good starting point to learn the basics of machine learning, such as data cleaning and monitoring models. If you already have experience in this area, you can start with Nanodegree Machine Learning Engineering, which focuses on the latest machine learning production and development techniques.
This Udemy course on machine learning provides a step-by-step introduction to the world of machine learning algorithms. The course is very comprehensive and is taught using Python and R. It is a step-by-step journey through machine learning on Udemy. The course is structured in such a way that all learners can easily understand the concepts and is suitable for both beginners and advanced learners.
No specific skills are required to take this course. Basic knowledge of high school mathematics is sufficient. The course is also open to students with a basic knowledge of machine learning who wish to explore different aspects of machine learning, learn more advanced concepts, and gain the practical skills needed in this area. The course will teach you how to perform robust analysis and make accurate predictions. You will also be able to create your own powerful machine learning models.
Designed and developed by a team of IBM experts, this deep learning certification program is offered on the edX platform. It prepares students to use new technologies in machine learning, computer science, and artificial intelligence to help them advance their careers.
This deep learning specialization consists of five graduate-level courses that require a total of 52-104 hours of coursework. It introduces students to the concepts and applications of deep learning, including the different types of neural networks used for supervised and unsupervised learning. You will also learn how to apply this knowledge by creating models and algorithms using libraries such as Keras, PyTorch, and Tensorflow.
This is an intermediate-level honors program developed by two senior researchers at the University of Washington. It is a comprehensive machine learning training program consisting of four courses taught over several weeks. Students are expected to work about 6 hours per week and complete the program in about 8 months. Most of the projects in this thesis use the Python programming language. Knowledge of mathematics and experience with computer programming is listed as prerequisites for participation in the program.