Microsoft Professional Program for Artificial Intelligence Review Reddit
by David Venturi
Every single Machine Learning course on the net, ranked past your reviews
A year and a half agone, I dropped out of one of the best estimator science programs in Canada. I started creating my own data scientific discipline master's programme using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. And I could acquire it faster, more efficiently, and for a fraction of the price.
I'm most finished at present. 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 office. And then I started creating a review-driven guide that recommends the best courses for each bailiwick within data science.
For the starting time guide in the series, I recommended a few coding classes for the beginner data scientist. Then it was statistics and probability classes. And then introductions to data science. Also, data visualization.
At present onto machine learning.
For this guide, I spent a dozen hours trying to place every online motorcar learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My cease goal was to identify the three best courses available and nowadays them to you, beneath.
For this job, I turned to none other than the open source Class Central customs, and its database of thousands of course ratings and reviews.
Since 2011, Form Primal founder Dhawal Shah has kept a closer center 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 form must fit three criteria:
- Information technology must accept a significant amount of auto learning content. Ideally, machine learning is the primary topic. Notation that deep learning-but courses are excluded. More than on that subsequently.
- Information technology must be on-demand or offered every few months.
- It must be an interactive online course, and then no books or read-merely tutorials. Though these are viable means to larn, 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 class that fits the higher up criteria. Since in that location are seemingly hundreds of courses on Udemy, we chose to consider the most-reviewed and highest-rated ones but.
In that location's always a take chances that we missed something, though. So please permit the states know in the comments section if we left a skilful course out.
How we evaluated courses
Nosotros compiled average ratings and number of reviews from Class Key and other review sites to calculate a weighted average rating for each form. We read text reviews and used this feedback to supplement the numerical ratings.
We fabricated subjective syllabus judgment calls based on three factors:
- Explanation of the car learning workflow. Does the grade 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 (eastward.g. regression, classification, clustering, etc.) and algorithms (e.g. within classification: naive Bayes, determination copse, support vector machines, etc.) covered or just a select few? Preference is given to courses that encompass more than without skimping on item.
- Usage of common data science and machine learning tools. Is the course taught using popular programming languages like Python, R, and/or Scala? How near popular libraries within those languages? These aren't necessary, but helpful then slight preference is given to these courses.
What is car learning? What is a workflow?
A popular definition originates from Arthur Samuel in 1959: machine learning is a subfield of calculator science that gives "computers the ability to learn without being explicitly programmed." In practise, this means developing computer programs that can make predictions based on data. Merely as humans can learn from experience, then tin can computers, where data = experience.
A machine learning workflow is the process required for conveying out a machine learning project. Though private projects can differ, nearly workflows share several mutual tasks: problem evaluation, data exploration, data preprocessing, model training/testing/deployment, etc. Beneath yous'll find helpful visualization of these core steps:
The ideal course introduces the entire procedure and provides interactive examples, assignments, and/or quizzes where students can perform each chore themselves.
Do these courses embrace deep learning?
Beginning off, let's define deep learning. Here is a succinct description:
"Deep learning is a subfield of automobile learning concerned with algorithms inspired by the structure and office of the encephalon called artificial neural networks."
— Jason Brownlee from Machine Learning Mastery
As would be expected, portions of some of the car learning courses incorporate deep learning content. I chose non to include deep learning-only courses, yet. If you lot are interested in deep learning specifically, nosotros've got you lot 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 usa. A few examples:medium.freecodecamp.com
My tiptop iii recommendations from that list would exist:
- 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™: Easily-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 avant-garde subject field.
Missing a few subjects? Skilful news! Some of this experience can exist caused through our recommendations in the kickoff two manufactures (programming, statistics) of this Data Science Career Guide. Several pinnacle-ranked courses beneath likewise 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'due south Auto 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, information technology covers all aspects of the machine learning workflow. Though information technology has a smaller scope than the original Stanford course upon which it is based, information technology nonetheless manages to cover a large number of techniques and algorithms. The estimated timeline is 11 weeks, with 2 weeks dedicated to neural networks and deep learning. Free and paid options are available.
Ng is a dynamic even so gentle instructor with a palpable feel. He inspires confidence, peculiarly when sharing practical implementation tips and warnings nearly common pitfalls. A linear algebra refresher is provided and Ng highlights the aspects of calculus most relevant to auto learning.
Evaluation is automatic and is done via multiple selection quizzes that follow each lesson and programming assignments. The assignments (at that place are eight of them) can be completed in MATLAB or Octave, which is an open up-source version of MATLAB. Ng explains his language option:
In the by, I've tried to teach machine learning using a large multifariousness of dissimilar programming languages including C++, Java, Python, NumPy, and likewise Octave … And what I've seen after having taught machine learning for almost a decade is that you larn much faster if you lot apply Octave as your programming environment.
Though Python and R are probable more compelling choices in 2017 with the increased popularity of those languages, reviewers note that that shouldn't stop y'all from taking the form.
A few prominent reviewers noted the post-obit:
Of longstanding renown in the MOOC globe, Stanford's machine learning course really is the definitive introduction to this topic. The course broadly covers all of the major areas of auto 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 fashion, including the math backside all concepts. Highly recommended.
The only problem I run across with this form 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 offer 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. Information technology has a 4.eight-star weighted average rating over 10 reviews.
The form as well 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 terminal exam are the modes of evaluation. Students tin use either Python, Octave, or MATLAB to complete the assignments. The grade'southward total estimated timeline is eight to ten hours per week over twelve weeks. It is costless with a verified certificate available for buy.
Below are a few of the aforementioned sparkling reviews:
Over all my years of [existence a] pupil I've come across professors who aren't brilliant, professors who are vivid but they don't know how to explain the stuff conspicuously, and professors who are brilliant and know how explain the stuff clearly. Dr. Paisley belongs to the third grouping.This is a keen grade … The instructor's language is precise and that is, to my heed, one of the strongest points of the course. The lectures are of high quality and the slides are great besides.
Dr. Paisley and his supervisor are … students of Michael Hashemite kingdom of jordan, the male parent of auto learning. [Dr. Paisley] is the best ML professor at Columbia because of his ability to explain stuff clearly. Upwardly to 240 students have selected his grade this semester, the largest number amidst all professors [education] machine learning at Columbia.
A practical intro in Python & R from manufacture experts
- Auto Learning A-Z™: Easily-On Python & R In Information Science (Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Squad via Udemy)
Machine Learning A-Z™ on Udemy is an impressively detailed offering that provides teaching in both Python and R, which is rare and can't be said for whatever of the other top courses. It has a 4.v-star weighted average rating over 8,119 reviews, which makes it the most reviewed course of the ones considered.
It covers the entire motorcar 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 ii 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 split videos for both Python and R.
Equally a "bonus," the course includes Python and R lawmaking templates for students to download and utilise 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 circuitous uncomplicated." Too, the prerequisites listed are "just some high school mathematics," so this grade might be a better option for those daunted past the Stanford and Columbia offerings.
A few prominent reviewers noted the following:
The class is professionally produced, the sound quality is excellent, and the explanations are articulate and concise … It's an incredible value for your financial and time investment.Information technology was spectacular to be able to follow the course in 2 unlike programming languages simultaneously.
Kirill is one of the absolute best instructors on Udemy (if non the Internet) and I recommend taking whatever grade he teaches. … This class has a ton of content, similar a ton!
The competition
Our #1 pick had a weighted average rating of 4.seven out of 5 stars over 422 reviews. Let'south look at the other alternatives, sorted by descending rating. A reminder that deep learning-simply courses are not included in this guide — you can notice those hither.
The Analytics Edge (Massachusetts Constitute of Engineering/edX): More than focused on analytics in full general, though it does cover several machine learning topics. Uses R. Strong narrative that leverages familiar real-earth examples. Challenging. X to fifteen hours per week over twelve weeks. Gratuitous with a verified document bachelor for purchase. Information technology has a 4.9-star weighted average rating over 214 reviews.
Python for Data Science and Auto 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 non ideal for the scope of this guide. 21.five hours of on-need video. Cost varies depending on Udemy discounts, which are frequent. It has a four.6-star weighted average rating over 3316 reviews.
Information Scientific discipline and Motorcar Learning Bootcamp with R (Jose Portilla/Udemy): The comments for Portilla's above grade utilize here likewise, 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 Developer Inc./Udemy): Taught by a data scientist/big data engineer/total stack software engineer with an impressive resume, Lazy Programmer currently has a serial of 16 motorcar learning-focused courses on Udemy. In total, the courses have 5000+ ratings and almost all of them have 4.vi stars. A useful form ordering is provided in each individual course's description. Uses Python. Cost varies depending on Udemy discounts, which are frequent.
Motorcar 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'southward Degree (OMS). Seize with teeth-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 information-friendly tools, including Apache Spark, Scala, and Hadoop. Uses both Python and R. Iv hours per week over six weeks. Gratis with a verified certificate available for purchase. It has a 4.five-star weighted average rating over 6 reviews.
Data Science and Machine Learning with Python — Hands On! (Frank Kane/Udemy): Uses Python. Kane has 9 years of experience at Amazon and IMDb. Nine hours of on-need video. Cost varies depending on Udemy discounts, which are frequent. It has a four.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. X hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.v-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 gratis. Co-created by Kaggle. Estimated timeline of half dozen months. Currently costs $199 USD per month with a l% tuition refund bachelor for those who graduate within 12 months. It has a 4.5-star weighted average rating over 2 reviews.
Learning From Information (Introductory Motorcar Learning) (California Establish of Engineering/edX): Enrollment is currently airtight on edX, simply is also available via CalTech'due south independent platform (run across below). Information technology has a four.49-star weighted average rating over 42 reviews.
Learning From Information (Introductory Machine Learning) (Yaser Abu-Mostafa/California Institute of Engineering science): "A real Caltech form, not a watered-downwards version." Reviews note information technology is excellent for agreement automobile learning theory. The professor, Yaser Abu-Mostafa, is popular amongst students and also wrote the textbook upon which this form is based. Videos are taped lectures (with lectures slides picture-in-film) uploaded to YouTube. Homework assignments are .pdf files. The class 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): Car learning with a focus on "big data." Introduces modernistic distributed file systems and MapReduce. Ten hours per week over seven weeks. Free. It has a four.4-star weighted boilerplate rating over 30 reviews.
AWS Machine Learning: A Complete Guide With Python (Chandra Lingam/Udemy): A unique focus on cloud-based car learning and specifically Amazon Web Services. Uses Python. Nine hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. Information technology has a 4.4-star weighted boilerplate rating over 62 reviews.
Introduction to Machine Learning & Confront Detection in Python (Holczer Balazs/Udemy): Uses Python. Eight hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. Information technology has a 4.four-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 past the professors who wrote it. Reviewers note that the MOOC isn't equally skilful as the book, citing "thin" exercises and mediocre videos. Five hours per calendar week over nine weeks. Gratuitous. It has a iv.35-star weighted average rating over 84 reviews.
Car Learning Specialization (University of Washington/Coursera): Great courses, simply last 2 classes (including the capstone project) were canceled. Reviewers annotation that this series is more digestable (read: easier for those without strong technical backgrounds) than other top machine learning courses (east.yard. Stanford's or Caltech's). Be enlightened that the series is incomplete with recommender systems, deep learning, and a summary missing. Free and paid options available. Information technology has a 4.31-star weighted average rating over 80 reviews.
From 0 to 1: Auto Learning, NLP & Python-Cut to the Hunt (Loony Corn/Udemy): "A down-to-earth, shy merely confident take on machine learning techniques." Taught by four-person team with decades of industry feel 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 vi weeks. Costless with a verified certificate available for buy. It has a four.09-star weighted average rating over 11 reviews.
Big Data: Statistical Inference and Machine Learning (Queensland Academy 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 elapsing at a recommended two hours per week, but one reviewer noted that 6 hours per week would be more appropriate. Complimentary and paid options available. It has a 4-star weighted average rating over four reviews.
Genomic Data Science and Clustering (Bioinformatics 5) (University of California, San Diego/Coursera): For those interested in the intersection of information science and biology and how it represents an of import frontier in modern science. Focuses on clustering and dimensionality reduction. Office of UCSD'southward Bioinformatics Specialization. Gratis and paid options available. It has a four-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 and then 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 10 weeks. Gratis. It has a 3.95-star weighted boilerplate 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. Office of Wesleyan's Data Analysis and Interpretation Specialization. Estimated timeline of four weeks. Free and paid options available. Information technology has a three.6-star weighted average rating over 5 reviews.
Programming with Python for Data Science (Microsoft/edX): Produced past Microsoft in partnership with Coding Dojo. Uses Python. Eight hours per calendar week over six weeks. Free and paid options available. It has a three.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. Office of Udacity's Machine Learning Engineer Nanodegree and Georgia Tech'southward Online Master'southward Degree (OMS). Estimated timeline of four months. Free. It has a 3.29-star weighted boilerplate rating over 14 reviews.
Applied Auto Learning (Johns Hopkins University/Coursera): A cursory, practical introduction to a number of machine learning algorithms. Several ane/two-star reviews expressing a variety of concerns. Role of JHU's Information Science Specialization. Four to nine hours per week over four weeks. Free and paid options available. It has a three.11-star weighted boilerplate rating over 37 reviews.
Machine Learning for Data Scientific discipline and Analytics (Columbia University/edX): Introduces a broad range of automobile 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 bachelor for purchase. It has a two.74-star weighted average rating over 36 reviews.
Recommender Systems Specialization (Academy of Minnesota/Coursera): Strong focus 1 specific type of motorcar learning — recommender systems. A iv course specialization plus a capstone projection, which is a case study. Taught using LensKit (an open up-source toolkit for recommender systems). Free and paid options available. It has a 2-star weighted boilerplate rating over 2 reviews.
Machine Learning With Large Information (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. Function of UCSD'south Large Information Specialization. Free and paid options available. It has a one.86-star weighted average rating over 14 reviews.
Practical Predictive Analytics: Models and Methods (University of Washington/Coursera): A cursory intro to core machine learning concepts. One reviewer noted that in that location was a lack of quizzes and that the assignments were not challenging. Part of UW's Information Science at Scale Specialization. Half-dozen to eight hours per calendar week over four weeks. Free and paid options bachelor. It has a 1.75-star weighted boilerplate rating over 4 reviews.
The following courses had ane 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 auto learning best practices to brand sense of human gesture, musical audio, and other real-time data. 7 sessions in length. Audit (gratis) and premium ($x USD per month) options available. Information technology has one 5-star review.
Applied Machine Learning in Python (Academy of Michigan/Coursera): Taught using Python and the scikit learn toolkit. Part of the Applied Data Scientific discipline with Python Specialization. Scheduled to outset May 29th. Complimentary and paid options available.
Applied Machine Learning (Microsoft/edX): Taught using various tools, including Python, R, and Microsoft Azure Motorcar Learning (note: Microsoft produces the course). Includes hands-on labs to reinforce the lecture content. Iii to four hours per week over six weeks. Free with a verified certificate available for purchase.
Motorcar Learning with Python (Large Data University): Taught using Python. Targeted towards beginners. Estimated completion fourth dimension of 4 hours. Big Information University is affiliated with IBM. Gratuitous.
Machine Learning with Apache SystemML (Big Data Academy): Taught using Apache SystemML, which is a declarative style language designed for large-scale motorcar 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 10 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. 5 to ten hours per week over ten weeks. Free with a verified certificate available for purchase.
Predictive Analytics: Gaining Insights from Large Data (Queensland Academy of Technology/FutureLearn): Cursory overview of a few algorithms. Uses Hewlett Packard Enterprise's Vertica Analytics platform as an practical tool. Beginning engagement to exist announced. 2 hours per week over 4 weeks. Free with a Document of Achievement available for purchase.
Introducción al Machine Learning (Universitas Telefónica/Miríada X): Taught in Castilian. An introduction to car learning that covers supervised and unsupervised learning. A total of twenty estimated hours over iv weeks.
Auto Learning Path Step (Dataquest): Taught in Python using Dataquest's interactive in-browser platform. Multiple guided projects and a "plus" project where yous build your own machine learning system using your own information. Subscription required.
The post-obit 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 total access to each course.
Introduction to Motorcar Learning (DataCamp): Covers classification, regression, and clustering algorithms. Uses R. 15 videos and 81 exercises with an estimated timeline of six hours.
Supervised Learning with scikit-larn (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. 16 videos and 49 exercises with an estimated timeline of iv hours.
Machine Learning Toolbox (DataCamp): Teaches the "big ideas" in car learning. Uses R. 24 videos and 88 exercises with an estimated timeline of iv hours.
Motorcar Learning with the Experts: School Budgets (DataCamp): A case report from a auto 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. 15 videos and 51 exercises with an estimated timeline of four hours.
Unsupervised Learning in Python (DataCamp): Covers a diversity 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 4 hours.
Motorcar Learning (Tom Mitchell/Carnegie Mellon University): Carnegie Mellon'southward graduate introductory car learning class. A prerequisite to their second graduate level form, "Statistical Machine Learning." Taped academy lectures with practise problems, homework assignments, and a midterm (all with solutions) posted online. A 2011 version of the course besides exists. CMU is 1 of the best graduate schools for studying automobile learning and has a whole department dedicated to ML. Gratuitous.
Statistical Car Learning (Larry Wasserman/Carnegie Mellon University): Probable the well-nigh advanced course in this guide. A follow-upwards to Carnegie Mellon's Auto Learning course. Taped university lectures with do problems, homework assignments, and a midterm (all with solutions) posted online. Free.
Undergraduate Motorcar 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 grade website. The course assignments are posted too (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 bachelor (see below).
Machine Learning (Nando de Freitas/University of British Columbia): A graduate auto learning form. The comments in de Freitas' undergraduate course (above) apply hither too.
Wrapping it Upward
This is the fifth of a vi-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 commodity, intros to data scientific discipline in the tertiary commodity, and data visualization in the fourth.
I ranked every Intro to Data Scientific discipline form on the internet, based on thousands of data points
A twelvemonth ago, I dropped out of 1 of the best informatics programs in Canada. I started creating my ain information…
The final piece will be a summary of those articles, plus the best online courses for other cardinal topics such equally data wrangling, databases, and even software engineering.
If y'all're looking for a complete list of Information Science online courses, you tin can discover them on Class Central'due south Information Science and Large Data bailiwick page.
If you enjoyed reading this, bank check out some of Class Key's other pieces:
Here are 250 Ivy League courses y'all tin take online right at present for free
250 MOOCs from Brown, Columbia, Cornell, Dartmouth, Harvard, Penn, Princeton, and Yale.
The 50 best gratuitous online university courses according to information
When I launched Class Primal dorsum in Nov 2011, there were around eighteen or then free online courses, and well-nigh all of…
If you lot have suggestions for courses I missed, let me know in the responses!
If y'all institute this helpful, click the ? so more people will come across it hither on Medium.
This is a condensed version of my original commodity published on Course Central, where I've included detailed course syllabi.
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Source: https://www.freecodecamp.org/news/every-single-machine-learning-course-on-the-internet-ranked-by-your-reviews-3c4a7b8026c0/
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