Top 7 Courses on Coursera in 2020
Overview of the most popular Data Science Courses on Coursera in 2020
We all know that online learning is becoming a trend, especially in 2020, the year of the remote work. So what’s the best course to learn from on Coursera?
Below, I’ll review the most popular 7 courses that will boost your career and skills, especially in data science and machine learning.
Machine learning is the study of machines behaving without being configured explicitly. In the last decade, automated technology has equipped us with self-driving vehicles, functional voice recognition, successful online search and even greater knowledge of the human world.
Machine learning is so daunting today that you still do not realize it hundreds of times a day. Many scientists even think that this is the only path to improve human AI.
You can hear about the most powerful machine learning methods in this class and learn how to apply them to get them to function for you. Overall, you can not only understand the theoretical underpinnings of learning but also acquire the realistic know-how to adapt these methods quickly and efficiently to new topics.
Finally, you may explore some of the latest techniques in engineering for machine learning and IA in Silicon Valley. This course gives a comprehensive introduction to machine learning, data surveying and identification of statistical trends.
This latest six-course beginning certificates, created by Google, gives IT professionals the skills required to progress their careers-including Python, Git and IT automation.
Vital expertise for everyone in IT is learning how to compose code to fix challenges and automate solutions. Python is already employers’ most common programming language.
This software draws on the IT roots to help you achieve the next stage of the career. It is built to show you how to program with Python and how to automate necessary machine management activities with Python. You can also learn to use Git and GitHub, troubleshoot and debug complicated issues and use configuration management and cloud to implement automation on a scale.
This degree can be done in around six months and will train you for a range of IT jobs, such as specialized IT support specialists or junior systems administrators. When the software is finished, the details can be exchanged with future employers, such as Walmart, Sprint, Hulu, Bank of America, Google (of course!), and more.
Python is suggested for installation on your computer. You may require specific computers for some courses where you can install Git or ask your administrator to install it.
Data science is one of the hottest careers, and there has never been a stronger demand for data scientists. Companies need someone who can interpret data and convey observations to support decisions informed by evidence.
This IBM Technical Qualification would improve the expertise and knowledge of all those involved in seeking a career in data science or machine learning. It’s a fallacy that you require a PhD to become a data scientist.
Anyone excited about learning is entitled to this Technical Credential. There is no previous knowledge of computer science or programming languages required.
The curriculum contains nine online courses, which provide up-to-date career preparation resources and technologies, including open-source resources and repositories, Python, databases, SQL, data processing, mathematical analysis, quantitative analytics and machine learning algorithms. You will practice data analysis utilizing real-world data analysis software and real-world data sets in the IBM Cloud.
After completing these courses successfully, you would have created a portfolio of data science ventures to offer you the confidence to immerse yourself in an exciting field of data science.
You can also get a digital badge from IBM, which honors the data-processing expertise, as well as a technical credential from Coursera.
This course is designed to show all the fundamentals of Python programming computers. We address the fundamentals of creating a software-based on a set of easy instructions in Python. The course is without preconditions and excludes anything but the easiest algebra.
Anyone with good programming expertise in this course should be able to learn the content. This course includes Chapters 1–5 of the “Python for All” textbook. When a student has finished this tutorial, they are eligible to take advanced classes in programming. Python 3 is covered in this lesson.
This specialization will support you if you want to enter into AI. Deep Learning is one of the most sought after technology capabilities. We’ll help you get strong at meaningful studying.
You will master the basics of Deep Learning in five lessons, grasp how neural networks are designed and how practical machine learning experiments are carried out.
You can hear about CNNs, Adam, LSTM, Dropout, Xavier, BatchNorm, and more. You will research instances including healthcare, independent driving, interpreting sign language, creation of music and natural language processing.
You will not only learn philosophy, but you may also see how it is implemented in business. In Python and TensorFlow, you will exercise both of these concepts, which we will show.
You’ll also hear from several of the leading Deep Learning experts who share their personal experiences and educate you about their professions. AI develops many fields. You can find innovative ways to apply it to your work after completing this specialization.
This speciality covers ideas and methods that you use in the data science process, from problems of the correct sort to observations and publication outcomes. In the final Capstone Project, you can use real-world data to create a data app. Afterwards, the students would have a portfolio that shows their mastery.
AI’s not just for engineers. If you want the company to use AI more, this is the way to say all of the non-technical colleagues, in particular.
What you can learn in this course:
- The implications of common AI terms such that neural networks, machine learning, in-depth learning and data science;
- What AI can — and cannot — do realistically
- How to detect AI applications for problems in your own company
- How does it feel to create a machine learning and data science projects?
- How to work in your company with an AI team and build an AI strategy
- How to conduct ethical and societal AI discussions
Although this class is mostly non-technical, engineers can also learn AI’s business aspects in this course.
What’s next after Coursera for Data Scientists?
If you’re looking for more hands-on education, have a look at Data Science Job course I’m running. The goal is to provide you knowledge about how to build a successful data science career: from no CS background to a PhD in Data Science included.
*disclaimer: the links are affiliate, thank you for your support*