![\"\"](\"https:\/\/gtechbooster.com\/media\/2025\/10\/26002.jpg\")
<\/a><\/div><\/div>We’ve all heard the buzz around machine learning and the way it pervades every aspect of our lives, see\u00a0Machine Learning For Cat Control, and even that it pays well. As far\u00a0courses as are concerned we have looked at offerings from Udacity, edX and Google as well as Coursera.<\/p>\n\n\n\n
Coursera already has several options for studying machine learning and it seems a particularly appropriate platform for learning about it given that was co-founded by Andrew Ng. Ng’s seminal course\u00a0Machine Learning<\/a> is still going strong having gone through many state changes over the years. Originally delivered face-to-face at Stanford University, where it was one of the most popular courses offered by the Computer Science department, it initially reached a wider audience as a series of lectures on You Tube. Then in 2011 it was one of Stanford’s Free Computer Science Courses<\/a>\u00a0which propelled the MOOC explosion. For this initial presentation\u00a0Ng adopted a new online format in which lectures were delivered in convenient segments of around 10 minutes with multiple choice questions interspersed.<\/p>\n\n\n\n Although overshadowed by the numbers enrolled in Introduction to AI \ntaught by Sebastian Thrun and Peter Norvig, which had 160,00 wp-signup.phps and\n issued over 23,000 certificates for successful completion, this online \nexperiment was a resounding success with 104,000 enrollments and 13,000 \ncertificates. It also led to the formation of Coursera and was among the\n first classes to be offered there.<\/p>\n\n\n\n Machine Learning\u00a0has now had nearly 2.5 million enrollments and is still available as a free course, with its next starting date being September 2nd. It is an 11-week course but as it has flexible deadlines you can take longer if required. It has a rating of 4.9 out of 5 based on almost 112K ratings and I can personally vouch for its excellence. Programming assignments are in MATLAB or Octave, a free open source alternative.<\/p>\n\n\n\n Initially all Coursera courses were free and issued certificates. \nThen it introduced paid-for certificates. Then along came \nSpecializations in which students complete and gain certificates in a \nseries of short courses (typically 4-6 weeks in length) and then pull \nall their learning together in a Capstone project and earn its final \nCertification. Until fairly recently, if you didn’t want certificates, \ncourses comprising a specialization could be audited for free, although \nthis often meant that while you could view the videos and other course \nmaterials you couldn’t complete the quizzes and programming assignments.\n While much of the content of older Specializations is still available \nfor free new ones require payment on a monthly subscription basis ($75 \nper month) that lets you access all the course in the specialization. \nYou have the benefit of a 7-day free trial during which you can cancel \nyour upfront payment and financial aid is available.<\/p> The new Machine Learning: Algorithms in the Real World<\/a> <\/strong>comes\n from the Alberta Machine Intelligence Institute. Although billed as a \nSpecialization, and leading to a Certificate, there is no Capstone \nproject and each of the four, nominally 4-week courses, are short and \ninvolve watching very short video snippets, plus readings and quizzes \nper week. The whole specialization is expected to require only one month\n to complete, with around 19 hours effort. It has fairly minimal \nprerequisites of a background in analytics, math (linear algebra, matrix\n multiplication), statistics and beginner level python programming.<\/p>\n\n\n\n By completing all four of them, participants will: <\/p>\n\n\n\n Details are available of the first two courses. <\/p>\n\n\n\n There is no syllabus as yet for the other two courses. The third course\u00a0Data for Machine Learning<\/a>\u00a0will highlight how data it is critical to the success of your applied machine learning model and draw attention to bias and feature engineering. The final course\u00a0Optimizing Machine Learning Model Performance<\/a>\u00a0is intended to\u00a0synthesize everything your have learned in the applied machine learning specialization. claiming:<\/p>\n\n\n\n By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.<\/em>\u00a0<\/p>\n\n\n\n![\"\"](\"https:\/\/gtechbooster.com\/media\/2026\/03\/005.webp\")
<\/a><\/div><\/div>\n\n\n\n
36 videos (Total 146 min), 14 readings, 5 quizzes
Introduces problem definition and data preparation in a machine learning project<\/li>
28 videos (Total 169 min), 4 quizzes
Understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbors and support vector machines are optimally used.\u00a0<\/li><\/ol>\n\n\n\nMore Information<\/h2>\n\n\n\n