Year in Review: 10 Most Popular Coursera Courses in 2017

Let see what are those cutting-edge tech skills that continue to be the most sought after in online education,here is  2017’s 10 most popular courses.

1. Machine Learning – Stanford University

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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2. Neural Networks and Deep Learning – deeplearning.ai

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago.

In this course, you will learn the foundations of deep learning. When you finish this class, you will:

• Understand the major technology trends driving Deep Learning
• Be able to build, train and apply fully connected deep neural networks
• Know how to implement efficient (vectorized) neural networks
• Understand the key parameters in a neural network’s architecture

This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.

3. Programming for Everybody (Getting Started with Python) – University of Michigan

This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.

4. Algorithms, Part I – Princeton University

Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.

About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.

5. Introduction to Mathematical Thinking – Stanford University

Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years.

Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking.

This ten-week course is designed with two particular audiences in mind. First, people who want to develop or improve mathematics-based, analytic thinking for professional or general life purposes. Second, high school seniors contemplating a mathematics or math-related major at college or university, or first-year students at college or university who are thinking of majoring in mathematics or a math-dependent subject. To achieves this aim, the first part of the course has very little traditional mathematical content, focusing instead on the thinking processes required for mathematics. The more mathematical examples are delayed until later, when they are more readily assimilated.

6. Learning How to Learn: Powerful Mental Tools to Help You Master Tough Subjects – University
of California, San Diego

This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. We’ll learn about the how the brain uses two very different learning modes and how it encapsulates (“chunks”) information. We’ll also cover illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects.

Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give you ideas for: turbocharging successful learning, including counter-intuitive test-taking tips and insights that will help you make the best use of your time on homework and problem sets. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.

This course can be taken independent of, concurrent with, or prior to, its companion course, Mindshift. (Learning How to Learn is more learning focused, and Mindshift is more career focused.)

7. Bitcoin and Cryptocurrency Technologies – IBM

If you’re a software developer and new to blockchain, this is the course for you. Several experienced IBM blockchain developer advocates will lead you through a series of videos that describe high-level concepts, components, and strategies on building blockchain business networks. You’ll also get hands-on experience modeling and building blockchain networks as well as create your first blockchain application.

The first part of this course covers basic concepts of blockchain, and no programming skills are required. However, to complete three of the four labs, you must understand basic software object-oriented programming and how to use the command line. It’s also helpful, but not required, that you can write code in JavaScript.

When you complete the course, you should understand what a blockchain business network is, how to build and model a simple blockchain solution, and the role of the developer in creating blockchain applications.

If you successfully complete the course, you’ll receive a certificate of completion. You’ll need to pass several end-of-section quizzes and a final exam that include multiple choice, true and false, and fill in the blank questions.

8. R Programming – Johns Hopkins University

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

9. Build Your First Android App (Project-Centered Course) – CentraleSupélec

In this project-centered course, you’ll design, build, and distribute your own unique application for the Android mobile platform. We’ll provide you with a set of customizable building blocks that you can assemble to create many different types of apps, and that will help you become familiar with many important specificities of Android development. When you complete the project, in addition to having a personalized app that you can use and share, you’ll have the skills and background you need to move on to more advanced coursework in Android development.

This project-centered course is designed for learners who have some prior experience programming in Java, such as an introductory college course or Coursera’s Java Programming Specialization.

You will need a computer with a stable Internet connection, but you will not need an Android phone – we’ll use free software that you can use to emulate a phone on your computer. We’ll use Android Studio as IDE; it is compatible with most computer and operating systems.

10. Introduction to Data Science in Python – University of Michigan

This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it,manipulate it, and run basic inferential statistical analyses.

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

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