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.
How we picked courses to consider
Each course must fit three criteria:
- 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.
- It must be on-demand or offered every few months.
- 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:
- 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.
- 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.
- 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 examples:
My top three recommendations from that list would be:
- Creative Applications of Deep Learning with TensorFlow by 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
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!
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.
The following six courses are offered by DataCamp. DataCamp’s hybrid teaching style leverages video and text-based instruction with lots of examples through an in-browser code editor. A subscription is required for full access to each course.
Introduction to Machine Learning (DataCamp): Covers classification, regression, and clustering algorithms. Uses R. Fifteen videos and 81 exercises with an estimated timeline of six hours.
Supervised Learning with scikit-learn (DataCamp): Uses Python and scikit-learn. Covers classification and regression algorithms. Seventeen videos and 54 exercises with an estimated timeline of four hours.
Unsupervised Learning in R (DataCamp): Provides a basic introduction to clustering and dimensionality reduction in R. Sixteen videos and 49 exercises with an estimated timeline of four hours.
Machine Learning Toolbox (DataCamp): Teaches the “big ideas” in machine learning. Uses R. 24 videos and 88 exercises with an estimated timeline of four hours.
Machine Learning with the Experts: School Budgets (DataCamp): A case study from a machine learning competition on DrivenData. Involves building a model to automatically classify items in a school’s budget. DataCamp’s “Supervised Learning with scikit-learn” is a prerequisite. Fifteen videos and 51 exercises with an estimated timeline of four hours.
Unsupervised Learning in Python (DataCamp): Covers a variety of unsupervised learning algorithms using Python, scikit-learn, and scipy. The course ends with students building a recommender system to recommend popular musical artists. Thirteen videos and 52 exercises with an estimated timeline of four hours.
Machine Learning (Tom Mitchell/Carnegie Mellon University): Carnegie Mellon’s graduate introductory machine learning course. A prerequisite to their second graduate level course, “Statistical Machine Learning.” Taped university lectures with practice problems, homework assignments, and a midterm (all with solutions) posted online. A 2011 version of the course also exists. CMU is one of the best graduate schools for studying machine learning and has a whole department dedicated to ML. Free.
Statistical Machine Learning (Larry Wasserman/Carnegie Mellon University): Likely the most advanced course in this guide. A follow-up to Carnegie Mellon’s Machine Learning course. Taped university lectures with practice problems, homework assignments, and a midterm (all with solutions) posted online. Free.
Undergraduate Machine Learning (Nando de Freitas/University of British Columbia): An undergraduate machine learning course. Lectures are filmed and put on YouTube with the slides posted on the course website. The course assignments are posted as well (no solutions, though). de Freitas is now a full-time professor at the University of Oxford and receives praise for his teaching abilities in various forums. Graduate version available (see below).
Machine Learning (Nando de Freitas/University of British Columbia): A graduate machine learning course. The comments in de Freitas’ undergraduate course (above) apply here as well.
Wrapping it Up
This is the fifth of a six-piece series that covers the best online courses for launching yourself into the data science field. We covered programming in the first article, statistics and probability in the second article, intros to data science in the third article, and data visualization in the fourth.
I ranked every Intro to Data Science course on the internet, based on thousands of data points
A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data…
The final piece will be a summary of those articles, plus the best online courses for other key topics such as data wrangling, databases, and even software engineering.
If you’re looking for a complete list of Data Science online courses, you can find them on Class Central’s Data Science and Big Data subject page.
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If you have suggestions for courses I missed, let me know in the responses!
If you found this helpful, click the 💚 so more people will see it here on Medium.
This is a condensed version of my original article published on Class Central, where I’ve included detailed course syllabi.