It 240 Week 4 Assignments

San José State U

Communication Studies

Comm 151i, New Media/ New Methods, Section 1, Spring 2015


Ted M. Coopman

Office location:

HGH 216




Office hours:

Online via Skype (tmcoopman) and W 10:30-11:30 am (by appointment only)

Class days/time:

W 9:00-10:15 am


HGH 219


upper division standing

Catalog Description 

Examines the internet as both a site of and a tool of communication research. Special attention to legal and ethical concerns associated with internet communication research.

Succeeding in a Four-Unit Course

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus. Because this is a four unit hybrid class, you can expect to spend on average 13 hours per week during a regular semester in class and on scheduled tutorials or activities.Careful time management will help you keep up with readings and assignments and enable you to be successful in all of your courses.

Engagement Unit

All 4-unit courses in Communication studies include a unit of engagement. This unit of engagement is designed to enrich students’ learning experiences and to facilitate student achievement of course learning objectives. Students enrolled in 4-unit courses are expected to spend on average 45 hours (or 3 hours per week over the course of a regular semester) outside of the classroom to complete engagement activities. This unit is worth 25% of the overall grade. The engagement unit in the class is the discussion.

Foundations, INQUIRY, Practice

COMM 151i is an Inquiry course. Each course in the Department of Communication Studies primarily focuses on one of three areas: Foundations (theoretical underpinnings of the discipline), Inquiry (research in the discipline), or Practice (application of communication theories and concepts to real world contexts).  Although the course addresses theory (foundations) and practice (application), the primary purpose of COMM 151I is to introduce you to research methods associated with the internet as a site for communication research and a tool to conduct research in communication.

Inquiry Area Outcomes

This course satisfies the INQUIRY area of Communication Studies learning outcomes. All INQUIRY courses, including COMM 151i, share these learning outcomes:

Students will be able to demonstrate proficiency in methods of communication inquiry.

  • Research Methods: Demonstrate understanding of methods of communication research and analyzes, such as rhetorical, critical, interpretive, performative, and social scientific approaches
  • Research Critique: Develop and apply analytical skills for understanding and evaluating communication research studies.

Course Goals

Within the INQUIRY area, COMM 151I is unique in emphasizing communication research in and about the internet. Specifically, it introduces you to concepts of inquiry commonly used in studying internet communication as well as how you can use the internet to study online and offline communication. In COMM 151I you’ll learn about a variety of qualitative, quantitative, and critical/cultural methods used in internet research, such as surveys, interviews, content analysis, ethnography, and textual analysis. The course pays particular attention to legal and ethical issues associated with internet research. In addition, this course provides an opportunity to “enrich the student experience” in research methods by engaging you in assessing and reflecting upon your learning through a semester-long ePortfolio project.

Course Learning Outcomes

After successfully completing this course, you will:

  1. Demonstrate an understanding of research methods commonly used in internet research, such as surveys, interviews, content analysis, ethnography, and textual analysis. (Research Methods)
  2. Determine the appropriate internet research method(s) to apply when studying specific communication phenomena. (Research Methods)
  3. Identify the strengths and weaknesses of the various methods commonly used in internet research. (Research Critique)
  4. Evaluate internet research methods applied in published studies of communication.  (Research Critique)
  5. Articulate the legal and ethical considerations internet communication researchers face in their work.  (Research Critique)

Required Texts


Ackland, R, (2013). Web social science: Concepts, data and tools for social scientists in the digital age.
ISBN: 9781849204828

Available from major online retailers and Sage in paperback and eBook. Please note that the bookstore always under-orders. If you order online make sure you have guaranteed delivery by the first day of class. Unless you add late, lack of a textbook won't be considered a valid excuse for missing related assignments!

Other Readings

as required accessed online or on provided PDFs.

Library Liaison

The Communication Studies Department encourages vigorous and ethical research as part of information literacy for all of its students. For assistance contact Nyle Monday our Academic Liaison Librarian <>, in the library go to the King Library Reference Desk (2nd floor; 408-808-2100) and/or utilize the Communication Research Guide available at

Class Protocol

Don’t be a jerk. Show respect for your peers, the course, and myself. If you do not want to be in class or would rather be doing something else than participate, then don’t come. I expect students to be both physically and mentally present.

Personal Electronic Devices

Often, you will use phones, tablets, and laptops in class to access assignments on Canvas and other course related activities. I allow responsible and on topic use of devices in class. This means refraining from reflexively checking email or texts, checking social media, surfing websites, or doing course work from other classes.  Since we only meet physically for an hour and 15 minutes each week I expect your full attention and focus on class activities. If I find you cannot manage to responsibly use your tech in class you will be asked to surrender it or leave.

Common courtesy and professional behavior dictate that you notify someone when you are recording him/her.  You must obtain the instructor’s permission to make audio or video recordings in this class.  Such permission allows the recordings to be used for your private, study purposes only. 

Course material developed by the instructor is the intellectual property of the instructor and cannot be shared publicly without his/her approval.  You may not publicly share or upload instructor generated material for this course such as quiz questions, lecture notes, or homework solutions without instructor consent.

Your peers, just like yourself, have a reasonable expectation of privacy and that materials produced by students and discussions that take place online or in physical classroom are intended for the consumption of classmates and the instructor only. Please do not audio or video record, or forward discussion posts, assignments, or other student generated content without the expressed permission of those involved.

Assignment Policy

Deadlines for assignments are required for several important reasons. First, deadlines keep students together and moving forward at the same rate. This allows enough time to cover all course material over the semester. Moreover, deadlines help students to distribute their workload and ensure enough time and attention to successfully complete assignments. Second, instructors usually teach between 3 and 5 classes (or more) per term. Designing a course is complex and requires a tight schedule. These classes, in turn, must be scheduled so that they do not conflict with each other and there is enough time for the instructor to assist students and grade assignments. Late assignments complicate this schedule and need to be made-up within specific framework to lessen their negative impact.

SJSU Athletes must submit away-game schedules and supporting paperwork at the start of term, identify any conflicts, and make arrangements PRIOR to missing classes or assignments.

I may be able or willing to accommodate non-school related scheduled or unscheduled events.

For detailed description of the Late/Missed Assignment Policy and instructions on submitting requests for accommodation visit the Late/Missed Assignment Policy page.

Requests to make-up assignments at the end of the term will not be accepted.

Dropping and Adding

You are responsible for understanding the policies and procedures about add/drops, academic renewal, and similar topics found at

Assignments and Grading Policy

For more information on assignments, go to theassignments page. See the calendar or module pages for the assignment of the week.Go here for information on grade review requests.

Assignments and Grading Policy


Readings and lectures will be assessed though regular quizzes. All quizzes are open book and note and are timed. For a detailed overview of quiz design and tips on succeeding on course quizzes visit the Quiz Directions And Tips page.

Online Quizzes (8). 6 on the the Ackland text and other readings, 1 on the Quantitative/Qualitative and 1 on the APA workshops. Administered via Canvas, they consist of 15 multiple-choice questions (15 pts), are open book/note, and timed (25 minutes). Quiz questions are randomly drawn from a pool of questions, so every quiz is different. All quizzes open at the beginning of the semester and close as we finish covering the material. See the Orientation Workshop for details and the course schedule below, quizzes, the weekly overviews in the modules, or the calendar for closing dates. (LO1, L02, LO3)


The final consists of a cumulative exam based on the textbook quizzes and is optional for extra credit.


There are 10 workshops (APA, Annotated Bibliography, & Scholarly Sources; Getting Started; Ethics and Source Credibility; Literature Review; Qualitative/Quantitative; Data Coding 1 and 2; Survey; Focus Group; and Interviewing) to concentrate on different research skills. Workshops may have any combination of online/at home components, in-class components, and a task.  See the assignments page for details (LO1, LO3, LO5).


There is one research assignment for this course. This assignment is broken down into several sections. First, you will create a proposal and revise it based on instructor feedback, a literature review and research design and revise it based on instructor feedback, create and execute a study and video, review other students submissions and write a questions for each and submit it, and write up your findings and revise it based on instructor feedback, and participate in an  in- class forum. Students may work individually, in pairs, or teams of three. See the assignments page for details (LO1, LO2, LO3, LO4, LO5).

Reading Review and Study Guide

While reading, note for each reading the 2 most important things and why they are important and the 2 most interesting things and why they are interesting. Posting the readings in this format ensures the readings for the week are completed in a timely fashion; acts as a student generated study guide for the quizzes; and provides other perspectives on understanding the readings. Understanding the readings is the foundation for understanding the course (LO1, LO3, LO4, LO5).

Process/Workshop Discussion

Post on the week's workshop or your ongoing project and respond to 2 peers. Discussing the research project process is an important aspect of understanding and successfully completing the class project. This discussion reduces stress, provides peer support, allows students to check their process and progress against peers, and allows the instructor to monitor student's progress and head off potential problems. Discussing the workshops and your research project process is an important aspect of understanding and successfully completing assignments.

Extra Credit

  • Discussion Week 1 (10 points max)
  • Exceptional Posts in the Workshop/Process Discussion (Max 2 points per week/20 possible for term)
  • Orientation Workshop Quiz (15 points max)
  • Error Bounty of 1 point per unique found error (typo, spelling, dates, etc.) on this Canvas site.
  • End of Term Process Survey (10)
  • Students who are with 5 points of the next highest grade will have their cores rounded up.
  • Cumulative final drawn from weekly quizzes (15)


Assignments have specific point values that convert letter grades (see below). While Canvas may display the total number of point as higher than 1000 (due to extra credit) the course is based on 1000 points. Therefore nay percentages displayed are an inaccurate measure of your course. See the Orientation workshop for details.

Project/Process Discussion


150 (4th)

Reading Review/Study Guide


60 (4th)

Quizzes (readings)



Quizzes (workshops)



Method Workshops



Research Workshops



Research Proposal v1


Research Proposal v2


Literature and Research Design v1


Literature and Research Design v2


Final Write-up v1


Final Write-up v2


Video Executive Summary


Project Forum Questions/Session 1

20 (4th)

Project Forum Questions/Session 2

20 (4th)



Grading Scale  (points = letter grade>)

























> 600


University Policies

Academic integrity

You must be familiar with the University’s Academic Integrity Policy available at “Your own commitment to learning, as evidenced by your enrollment at San Jose State University and the University’s integrity policy, require you to be honest in all your academic course work. Faculty members are required to report all infractions to the office of Student Conduct and Ethical development.”

I will not tolerate instances of academic dishonesty. Cheating on quizzes or plagiarism (presenting the work of another as your own, or the use of another person’s ideas without giving proper credit) will result in a failing grade and sanctions by the University. For this class, all assignments are to be completed by the individual student unless otherwise specified. “If you would like to include in your assignment any material you have submitted, or plan to submit for another class, please note that SJSU’s Academic Policy F06-1 requires approval of instructors.”

Campus Policy in Compliance with the Americans with Disabilities Act

If you need course adaptations or accommodations because of a disability, or if you need to make special arrangements in case the building must be evacuated, please make an appointment with me as soon as possible, or see me during office hours. Presidential Directive 97-03 requires that students with disabilities requesting accommodations must register with the Accessible Education Center (AEC) to establish a record of their disability.

Student Technology Resources

Computer labs for student use are available in the new Academic Success Center located on the 1st floor of Clark Hall and on the 2nd floor of the Student Union. In addition, computers are available in the Martin Luther King Library. The COMM Lab, located in Clark Hall 240, also has a few computers available for student use.

A wide variety of audio-visual equipment is available for student checkout from Media Services located in IRC 112. These items include digital and VHS camcorders, VHS and Beta video players, 16 mm, slide, overhead, DVD, CD, and audiotape players, sound systems, wireless microphones, screens and monitors.

Communication Center

The Communication Center is located in Hugh Gillis Hall 229 and is open Monday - Thursday 10:30AM - 4:30PM February 4th - May ?th.  The Center provides support for all students interested in developing their personal and professional communication skills, and offers specialized support for those enrolled in Communication Studies courses.  Services include in-person workshops and self-paced online modules via Canvas.  Upper-division Communication Studies students staff the Center and are trained in coaching students on a variety of topics related to our department courses.  Enrollment in COMM 80 provides support for the Center.  More information can be found through the website You are strongly encouraged to use the Center. To add 1 unit of COMM 80, the section numbers are 01 – 07 and the course numbers are 40293, 40294, 40295, 42759, 42760, 42761, and 46522 respectively; no add code necessary.

Academic Counseling Center for Excellence in the Social Sciences (ACCESS)

Clark Hall Room 240, 924-5363,
Dr. Hien Do, Faculty Director,, 924-5461
Valerie Chapman, Academic Advisor,, 924-5364
All COSS students and interested students are invited to stop by the Center for general education advising, help with changing majors, academic policy related questions, meeting with peer advisors, and/or attending various regularly scheduled presentations and workshops. Call or email for an appointment, or just stop by.

Learning Assistance Resource Center

The Learning Assistance Resource Center is designed to assist students in the development of their full academic potential and to motivate them to become self-directed learners. The center provides support services, such as skills assessment, individual or group tutorials, subject advising, learning assistance, summer academic preparation and basic skills development. The Learning Assistance Resource Center is located in Room 600 in the Student Services Center.

SJSU Writing Center

The Writing Center in Clark Hall 126 offers tutoring services to San Jose State students in all courses. Writing Specialists assist in all areas of the writing process, including grammar, organization, paragraph development, coherence, syntax, and documentation styles. For more information, visit the Writing Center website at or call 924-2308.

The syllabus page shows a table-oriented view of the course schedule, and the basics of course grading. You can add any other comments, notes, or thoughts you have about the course structure, course policies or anything else.

To add some comments, click the "Edit" link at the top.

Course Summary:

Every single Machine Learning course on the internet, ranked by your reviews

A year and a half ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. And I could learn it faster, more efficiently, and for a fraction of the cost.

I’m almost finished now. I’ve taken many data science-related courses and audited portions of many more. I know the options out there, and what skills are needed for learners preparing for a data analyst or data scientist role.So I started creating a review-driven guide that recommends the best courses for each subject within data science.

For the first guide in the series, I recommended a few coding classes for the beginner data scientist. Then it was statistics and probability classes. Then introductions to data science. Also, data visualization.

Now onto machine learning.

For this guide, I spent a dozen hours trying to identify every online machine learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My end goal was to identify the three best courses available and present them to you, below.

For this task, I turned to none other than the open source Class Central community, and its database of thousands of course ratings and reviews.

Since 2011, Class Central founder Dhawal Shah has kept a closer eye on online courses than arguably anyone else in the world. Dhawal personally helped me assemble this list of resources.

How we picked courses to consider

Each course must fit three criteria:

  1. It must have a significant amount of machine learning content. Ideally, machine learning is the primary topic.Note that deep learning-only courses are excluded. More on that later.
  2. It must be on-demand or offered every few months.
  3. It must be an interactive online course, so no books or read-only tutorials. Though these are viable ways to learn, this guide focuses on courses. Courses that are strictly videos (i.e. with no quizzes, assignments, etc.) are also excluded.

We believe we covered every notable course that fits the above criteria. Since there are seemingly hundreds of courses on Udemy, we chose to consider the most-reviewed and highest-rated ones only.

There’s always a chance that we missed something, though. So please let us know in the comments section if we left a good course out.

How we evaluated courses

We compiled average ratings and number of reviews from Class Central and other review sites to calculate a weighted average rating for each course. We read text reviews and used this feedback to supplement the numerical ratings.

We made subjective syllabus judgment calls based on three factors:

  1. Explanation of the machine learning workflow. Does the course outline the steps required for executing a successful ML project? See the next section for what a typical workflow entails.
  2. Coverage of machine learning techniques and algorithms. Are a variety of techniques (e.g. regression, classification, clustering, etc.) and algorithms (e.g. within classification: naive Bayes, decision trees, support vector machines, etc.) covered or just a select few? Preference is given to courses that cover more without skimping on detail.
  3. Usage of common data science and machine learning tools. Is the course taught using popular programming languages like Python, R, and/or Scala? How about popular libraries within those languages? These aren’t necessary, but helpful so slight preference is given to these courses.

What is machine learning? What is a workflow?

A popular definition originates from Arthur Samuel in 1959: machine learning is a subfield of computer science that gives “computers the ability to learn without being explicitly programmed.” In practice, this means developing computer programs that can make predictions based on data. Just as humans can learn from experience, so can computers, where data = experience.

A machine learning workflow is the process required for carrying out a machine learning project. Though individual projects can differ, most workflows share several common tasks: problem evaluation, data exploration, data preprocessing, model training/testing/deployment, etc. Below you’ll find helpful visualization of these core steps:

The ideal course introduces the entire process and provides interactive examples, assignments, and/or quizzes where students can perform each task themselves.

Do these courses cover deep learning?

First off, let’s define deep learning. Here is a succinct description:

“Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.”
— Jason Brownlee from Machine Learning Mastery

As would be expected, portions of some of the machine learning courses contain deep learning content. I chose not to include deep learning-only courses, however. If you are interested in deep learning specifically, we’ve got you covered with the following article:

Dive into Deep Learning with 12 free online courses
Every day brings new headlines for how deep learning is changing the world around us. A few

My top three recommendations from that list would be:

  • Creative Applications of Deep Learning with TensorFlowby Kadenze
  • Neural Networks for Machine Learning by the University of Toronto (taught by Geoffrey Hinton) via Coursera
  • Deep Learning A-Z™: Hands-On Artificial Neural Networks
    by Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team via Udemy

Recommended prerequisites

Several courses listed below ask students to have prior programming, calculus, linear algebra, and statistics experience. These prerequisites are understandable given that machine learning is an advanced discipline.

Missing a few subjects? Good news! Some of this experience can be acquired through our recommendations in the first two articles (programming, statistics) of this Data Science Career Guide. Several top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar.

Our pick for the best machine learning course is…

  • Machine Learning (Stanford University via Coursera)

Stanford University’s Machine Learning on Coursera is the clear current winner in terms of ratings, reviews, and syllabus fit. Taught by the famous Andrew Ng, Google Brain founder and former chief scientist at Baidu, this was the class that sparked the founding of Coursera. It has a 4.7-star weighted average rating over 422 reviews.

Released in 2011, it covers all aspects of the machine learning workflow. Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms. The estimated timeline is eleven weeks, with two weeks dedicated to neural networks and deep learning. Free and paid options are available.

Ng is a dynamic yet gentle instructor with a palpable experience. He inspires confidence, especially when sharing practical implementation tips and warnings about common pitfalls. A linear algebra refresher is provided and Ng highlights the aspects of calculus most relevant to machine learning.

Evaluation is automatic and is done via multiple choice quizzes that follow each lesson and programming assignments. The assignments (there are eight of them) can be completed in MATLAB or Octave, which is an open-source version of MATLAB. Ng explains his language choice:

In the past, I’ve tried to teach machine learning using a large variety of different programming languages including C++, Java, Python, NumPy, and also Octave … And what I’ve seen after having taught machine learning for almost a decade is that you learn much faster if you use Octave as your programming environment.

Though Python and R are likely more compelling choices in 2017 with the increased popularity of those languages, reviewers note that that shouldn’t stop you from taking the course.

A few prominent reviewers noted the following:

Of longstanding renown in the MOOC world, Stanford’s machine learning course really is the definitive introduction to this topic. The course broadly covers all of the major areas of machine learning … Prof. Ng precedes each segment with a motivating discussion and examples.
Andrew Ng is a gifted teacher and able to explain complicated subjects in a very intuitive and clear way, including the math behind all concepts. Highly recommended.
The only problem I see with this course if that it sets the expectation bar very high for other courses.

A new Ivy League introduction with a brilliant professor

  • Machine Learning (Columbia University via edX)

Columbia University’s Machine Learning is a relatively new offering that is part of their Artificial Intelligence MicroMasters on edX. Though it is newer and doesn’t have a large number of reviews, the ones that it does have are exceptionally strong. Professor John Paisley is noted as brilliant, clear, and clever. It has a 4.8-star weighted average rating over 10 reviews.

The course also covers all aspects of the machine learning workflow and more algorithms than the above Stanford offering. Columbia’s is a more advanced introduction, with reviewers noting that students should be comfortable with the recommended prerequisites (calculus, linear algebra, statistics, probability, and coding).

Quizzes (11), programming assignments (4), and a final exam are the modes of evaluation. Students can use either Python, Octave, or MATLAB to complete the assignments. The course’s total estimated timeline is eight to ten hours per week over twelve weeks. It is free with a verified certificate available for purchase.

Below are a few of the aforementioned sparkling reviews:

Over all my years of [being a] student I’ve come across professors who aren’t brilliant, professors who are brilliant but they don’t know how to explain the stuff clearly, and professors who are brilliant and know how explain the stuff clearly. Dr. Paisley belongs to the third group.
This is a great course … The instructor’s language is precise and that is, to my mind, one of the strongest points of the course. The lectures are of high quality and the slides are great too.
Dr. Paisley and his supervisor are … students of Michael Jordan, the father of machine learning. [Dr. Paisley] is the best ML professor at Columbia because of his ability to explain stuff clearly. Up to 240 students have selected his course this semester, the largest number among all professors [teaching] machine learning at Columbia.

A practical intro in Python & R from industry experts

  • Machine Learning A-Z™: Hands-On Python & R In Data Science (Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team via Udemy)

Machine Learning A-Z™ on Udemy is an impressively detailed offering that provides instruction in both Python and R, which is rare and can’t be said for any of the other top courses. It has a 4.5-star weighted average rating over 8,119 reviews, which makes it the most reviewed course of the ones considered.

It covers the entire machine learning workflow and an almost ridiculous (in a good way) number of algorithms through 40.5 hours of on-demand video. The course takes a more applied approach and is lighter math-wise than the above two courses. Each section starts with an “intuition” video from Eremenko that summarizes the underlying theory of the concept being taught. de Ponteves then walks through implementation with separate videos for both Python and R.

As a “bonus,” the course includes Python and R code templates for students to download and use on their own projects. There are quizzes and homework challenges, though these aren’t the strong points of the course.

Eremenko and the SuperDataScience team are revered for their ability to “make the complex simple.” Also, the prerequisites listed are “just some high school mathematics,” so this course might be a better option for those daunted by the Stanford and Columbia offerings.

A few prominent reviewers noted the following:

The course is professionally produced, the sound quality is excellent, and the explanations are clear and concise … It’s an incredible value for your financial and time investment.
It was spectacular to be able to follow the course in two different programming languages simultaneously.
Kirill is one of the absolute best instructors on Udemy (if not the Internet) and I recommend taking any class he teaches. … This course has a ton of content, like a ton!

The competition

Our #1 pick had a weighted average rating of 4.7 out of 5 stars over 422 reviews. Let’s look at the other alternatives, sorted by descending rating. A reminder that deep learning-only courses are not included in this guide — you can find those here.

The Analytics Edge (Massachusetts Institute of Technology/edX): More focused on analytics in general, though it does cover several machine learning topics. Uses R. Strong narrative that leverages familiar real-world examples. Challenging. Ten to fifteen hours per week over twelve weeks. Free with a verified certificate available for purchase. It has a 4.9-star weighted average rating over 214 reviews.

Python for Data Science and Machine Learning Bootcamp (Jose Portilla/Udemy): Has large chunks of machine learning content, but covers the whole data science process. More of a very detailed intro to Python. Amazing course, though not ideal for the scope of this guide. 21.5 hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.6-star weighted average rating over 3316 reviews.

Data Science and Machine Learning Bootcamp with R (Jose Portilla/Udemy): The comments for Portilla’s above course apply here as well, except for R. 17.5 hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.6-star weighted average rating over 1317 reviews.

Machine Learning Series (Lazy Programmer Inc./Udemy): Taught by a data scientist/big data engineer/full stack software engineer with an impressive resume, Lazy Programmer currently has a series of 16 machine learning-focused courses on Udemy. In total, the courses have 5000+ ratings and almost all of them have 4.6 stars. A useful course ordering is provided in each individual course’s description. Uses Python. Cost varies depending on Udemy discounts, which are frequent.

Machine Learning (Georgia Tech/Udacity): A compilation of what was three separate courses: Supervised, Unsupervised and Reinforcement Learning. Part of Udacity’s Machine Learning Engineer Nanodegree and Georgia Tech’s Online Master’s Degree (OMS). Bite-sized videos, as is Udacity’s style. Friendly professors. Estimated timeline of four months. Free. It has a 4.56-star weighted average rating over 9 reviews.

Implementing Predictive Analytics with Spark in Azure HDInsight (Microsoft/edX): Introduces the core concepts of machine learning and a variety of algorithms. Leverages several big data-friendly tools, including Apache Spark, Scala, and Hadoop. Uses both Python and R. Four hours per week over six weeks. Free with a verified certificate available for purchase. It has a 4.5-star weighted average rating over 6 reviews.

Data Science and Machine Learning with Python — Hands On! (Frank Kane/Udemy): Uses Python. Kane has nine years of experience at Amazon and IMDb. Nine hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.5-star weighted average rating over 4139 reviews.

Scala and Spark for Big Data and Machine Learning (Jose Portilla/Udemy): “Big data” focus, specifically on implementation in Scala and Spark. Ten hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.5-star weighted average rating over 607 reviews.

Machine Learning Engineer Nanodegree (Udacity): Udacity’s flagship Machine Learning program, which features a best-in-class project review system and career support. The program is a compilation of several individual Udacity courses, which are free. Co-created by Kaggle. Estimated timeline of six months. Currently costs $199 USD per month with a 50% tuition refund available for those who graduate within 12 months. It has a 4.5-star weighted average rating over 2 reviews.

Learning From Data (Introductory Machine Learning) (California Institute of Technology/edX): Enrollment is currently closed on edX, but is also available via CalTech’s independent platform (see below). It has a 4.49-star weighted average rating over 42 reviews.

Learning From Data (Introductory Machine Learning) (Yaser Abu-Mostafa/California Institute of Technology): “A real Caltech course, not a watered-down version.” Reviews note it is excellent for understanding machine learning theory. The professor, Yaser Abu-Mostafa, is popular among students and also wrote the textbook upon which this course is based. Videos are taped lectures (with lectures slides picture-in-picture) uploaded to YouTube. Homework assignments are .pdf files. The course experience for online students isn’t as polished as the top three recommendations. It has a 4.43-star weighted average rating over 7 reviews.

Mining Massive Datasets (Stanford University): Machine learning with a focus on “big data.” Introduces modern distributed file systems and MapReduce. Ten hours per week over seven weeks. Free. It has a 4.4-star weighted average rating over 30 reviews.

AWS Machine Learning: A Complete Guide With Python (Chandra Lingam/Udemy): A unique focus on cloud-based machine learning and specifically Amazon Web Services. Uses Python. Nine hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 62 reviews.

Introduction to Machine Learning & Face Detection in Python (Holczer Balazs/Udemy): Uses Python. Eight hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 162 reviews.

StatLearning: Statistical Learning (Stanford University): Based on the excellent textbook, “An Introduction to Statistical Learning, with Applications in R” and taught by the professors who wrote it. Reviewers note that the MOOC isn’t as good as the book, citing “thin” exercises and mediocre videos. Five hours per week over nine weeks. Free. It has a 4.35-star weighted average rating over 84 reviews.

Machine Learning Specialization (University of Washington/Coursera): Great courses, but last two classes (including the capstone project) were canceled. Reviewers note that this series is more digestable (read: easier for those without strong technical backgrounds) than other top machine learning courses (e.g. Stanford’s or Caltech’s). Be aware that the series is incomplete with recommender systems, deep learning, and a summary missing. Free and paid options available. It has a 4.31-star weighted average rating over 80 reviews.

From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase (Loony Corn/Udemy): “A down-to-earth, shy but confident take on machine learning techniques.” Taught by four-person team with decades of industry experience together. Uses Python. Cost varies depending on Udemy discounts, which are frequent. It has a 4.2-star weighted average rating over 494 reviews.

Principles of Machine Learning (Microsoft/edX): Uses R, Python, and Microsoft Azure Machine Learning. Part of the Microsoft Professional Program Certificate in Data Science. Three to four hours per week over six weeks. Free with a verified certificate available for purchase. It has a 4.09-star weighted average rating over 11 reviews.

Big Data: Statistical Inference and Machine Learning (Queensland University of Technology/FutureLearn): A nice, brief exploratory machine learning course with a focus on big data. Covers a few tools like R, H2O Flow, and WEKA. Only three weeks in duration at a recommended two hours per week, but one reviewer noted that six hours per week would be more appropriate. Free and paid options available. It has a 4-star weighted average rating over 4 reviews.

Genomic Data Science and Clustering (Bioinformatics V) (University of California, San Diego/Coursera): For those interested in the intersection of computer science and biology and how it represents an important frontier in modern science. Focuses on clustering and dimensionality reduction. Part of UCSD’s Bioinformatics Specialization. Free and paid options available. It has a 4-star weighted average rating over 3 reviews.

Intro to Machine Learning (Udacity): Prioritizes topic breadth and practical tools (in Python) over depth and theory. The instructors, Sebastian Thrun and Katie Malone, make this class so fun. Consists of bite-sized videos and quizzes followed by a mini-project for each lesson. Currently part of Udacity’s Data Analyst Nanodegree. Estimated timeline of ten weeks. Free. It has a 3.95-star weighted average rating over 19 reviews.

Machine Learning for Data Analysis (Wesleyan University/Coursera): A brief intro machine learning and a few select algorithms. Covers decision trees, random forests, lasso regression, and k-means clustering. Part of Wesleyan’s Data Analysis and Interpretation Specialization. Estimated timeline of four weeks. Free and paid options available. It has a 3.6-star weighted average rating over 5 reviews.

Programming with Python for Data Science (Microsoft/edX): Produced by Microsoft in partnership with Coding Dojo. Uses Python. Eight hours per week over six weeks. Free and paid options available. It has a 3.46-star weighted average rating over 37 reviews.

Machine Learning for Trading (Georgia Tech/Udacity): Focuses on applying probabilistic machine learning approaches to trading decisions. Uses Python. Part of Udacity’s Machine Learning Engineer Nanodegree and Georgia Tech’s Online Master’s Degree (OMS). Estimated timeline of four months. Free. It has a 3.29-star weighted average rating over 14 reviews.

Practical Machine Learning (Johns Hopkins University/Coursera): A brief, practical introduction to a number of machine learning algorithms. Several one/two-star reviews expressing a variety of concerns. Part of JHU’s Data Science Specialization. Four to nine hours per week over four weeks. Free and paid options available. It has a 3.11-star weighted average rating over 37 reviews.

Machine Learning for Data Science and Analytics (Columbia University/edX): Introduces a wide range of machine learning topics. Some passionate negative reviews with concerns including content choices, a lack of programming assignments, and uninspiring presentation. Seven to ten hours per week over five weeks. Free with a verified certificate available for purchase. It has a 2.74-star weighted average rating over 36 reviews.

Recommender Systems Specialization (University of Minnesota/Coursera): Strong focus one specific type of machine learning — recommender systems. A four course specialization plus a capstone project, which is a case study. Taught using LensKit (an open-source toolkit for recommender systems). Free and paid options available. It has a 2-star weighted average rating over 2 reviews.

Machine Learning With Big Data (University of California, San Diego/Coursera): Terrible reviews that highlight poor instruction and evaluation. Some noted it took them mere hours to complete the whole course. Part of UCSD’s Big Data Specialization. Free and paid options available. It has a 1.86-star weighted average rating over 14 reviews.

Practical Predictive Analytics: Models and Methods (University of Washington/Coursera): A brief intro to core machine learning concepts. One reviewer noted that there was a lack of quizzes and that the assignments were not challenging. Part of UW’s Data Science at Scale Specialization. Six to eight hours per week over four weeks. Free and paid options available. It has a 1.75-star weighted average rating over 4 reviews.

The following courses had one or no reviews as of May 2017.

Machine Learning for Musicians and Artists (Goldsmiths, University of London/Kadenze): Unique. Students learn algorithms, software tools, and machine learning best practices to make sense of human gesture, musical audio, and other real-time data. Seven sessions in length. Audit (free) and premium ($10 USD per month) options available. It has one 5-star review.

Applied Machine Learning in Python (University of Michigan/Coursera): Taught using Python and the scikit learn toolkit. Part of the Applied Data Science with Python Specialization. Scheduled to start May 29th. Free and paid options available.

Applied Machine Learning (Microsoft/edX): Taught using various tools, including Python, R, and Microsoft Azure Machine Learning (note: Microsoft produces the course). Includes hands-on labs to reinforce the lecture content. Three to four hours per week over six weeks. Free with a verified certificate available for purchase.

Machine Learning with Python (Big Data University): Taught using Python. Targeted towards beginners. Estimated completion time of four hours. Big Data University is affiliated with IBM. Free.

Machine Learning with Apache SystemML (Big Data University): Taught using Apache SystemML, which is a declarative style language designed for large-scale machine learning. Estimated completion time of eight hours. Big Data University is affiliated with IBM. Free.

Machine Learning for Data Science (University of California, San Diego/edX): Doesn’t launch until January 2018. Programming examples and assignments are in Python, using Jupyter notebooks. Eight hours per week over ten weeks. Free with a verified certificate available for purchase.

Introduction to Analytics Modeling (Georgia Tech/edX): The course advertises R as its primary programming tool. Five to ten hours per week over ten weeks. Free with a verified certificate available for purchase.

Predictive Analytics: Gaining Insights from Big Data (Queensland University of Technology/FutureLearn): Brief overview of a few algorithms. Uses Hewlett Packard Enterprise’s Vertica Analytics platform as an applied tool. Start date to be announced. Two hours per week over four weeks. Free with a Certificate of Achievement available for purchase.

Introducción al Machine Learning (Universitas Telefónica/Miríada X): Taught in Spanish. An introduction to machine learning that covers supervised and unsupervised learning. A total of twenty estimated hours over four weeks.

Machine Learning Path Step (Dataquest): Taught in Python using Dataquest’s interactive in-browser platform. Multiple guided projects and a “plus” project where you build your own machine learning system using your own data. Subscription required.

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