Linear Algebra Refresher Course
Below are the top discussions from Reddit that mention this online Udacity course.
Learn linear algebra by doing: you will code your own library of linear algebra functions.
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Reddit Posts and Comments
0 posts • 11 mentions • top 8 shown below
9 points • iwanttobeindev
How much linear algebra prep should I do for harder courses?
Newly admitted student here. I haven't taken linear algebra in almost 8 years and I'm considering eventually taking courses like CV, HPC, AI, ML, all of which seem to have some element of LA.
I want to brush up on my knowledge in this area and am looking for good resources. Do you think the Linear Algebra Refresher Course on Udacity is sufficient or should I dive into Linear Algebra through MIT OCW?
I'm willing to go through the latter if people think it's worth it, but I'm afraid it will take a lot of time when I'm already trying to study discrete math and proofs.
8 points • MysticMania
Like the other commenter mentioned, there are a ton of college courses that you can take online.
Looks like Coursera has a Linear Algebra course that happens to start today :D https://www.coursera.org/learn/linearalgebramachinelearning
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I also like Udacity because most of their courses are free, the videos are literally hosted on youtube, and they like to go indepth on a lot of topics. I did their DS/Algos course a while back, it was a good starting point for my last job search since it covered the basics well.
Looks like they has a free Linear Algebra Refresher course although I haven't taken this course myself: https://www.udacity.com/course/linearalgebrarefreshercourseud953
1 points • java568
Course ends May 31, 2018 for anyone wondering.
Did you know MATLAB going in? I don't really want to relearn MATLAB just to refresh my linear algebra knowledge. I wonder how it compares to something like this: https://www.udacity.com/course/linearalgebrarefreshercourseud953
1 points • neveryield
There is a linear algebra refresher with Python course on udacity which should provide you adequate background:
https://www.udacity.com/course/linearalgebrarefreshercourseud953
2 points • ghjm
There's Chris Pryby's "Linear Algebra Refresher" course that was at one time being recommended to OMSCS students (and maybe still is): https://www.udacity.com/course/linearalgebrarefreshercourseud953
I took this and didn't think it was very good. I've had my eye on this: https://www.coursera.org/learn/linearalgebramachinelearning but have not taken it so can't comment on its quality.
If you mean accredited courses for credit, I haven't looked.
15 points • _SleepyOwl
Help critique my ML curriculum!
Hi everyone! I've been doing some research on how to get started on machine learning as a beginner and have come up with the curriculum below for myself to follow. It would be great if those proficient in ML, or even those currently learning, help to critique or provide suggestions. Additional courses, literature, programs, or advice would be greatly appreciated! It may be difficult to critique without having firsthand experience of each course or program, but I'm more concerned the with subject matter included than the courses themselves. As I go through each course, I may decide to change them if I feel that it is not pertinent to the goal or lacking in quality. There's no way I will become an expert after this, but I'm simply looking for a good starting point. I hope this will help others as well.
As a point of reference, I've graduated with a B.S. in Mechanical Engineering, have taken several math courses (calculus, linear algebra, differential equations), and have taken a few programming courses (CS50, Intro to Comp Sci on eDX, CS 101 at University). I'm currently working parttime as a mechanical designer / energy analyst and have 4050 hours a week to spare.
For the curriculum, I've broken it up into 5 sections (programming, statistics, linear algebra, data science, and machine learning) which will be overlapping. I plan on taking 3 courses at a time starting with Udacity's Intro to programming, Stanford's probability and stats, and Udacity's linear algebra refresher while reading "An Introduction to Statistical Learning."
Here it is!
Curriculum:
Programming
 Udacity Introduction to programming https://www.udacity.com/course/introtoprogrammingnanodegreend000
 Learn the basics of programming through HTML, CSS, and Python.
Statistics
 Stanford Online: Probability and Statistics https://lagunita.stanford.edu/courses/coursev1:OLI+ProbStat+Open_Jan2017/about

Broken into four sections: exploratory data analysis, producing data, probability, and inference.

Stanford Online: Statistical Learning http://online.stanford.edu/course/statisticallearningwinter2014
 Introductorylevel course in supervised learning, with a focus on regression and classification methods.
 Lectures cover all the material in An Introduction to Statistical Learning, with Applications in R.
Linear Algebra

Udacity Linear Algebra Refresher Course with Python: https://www.udacity.com/course/linearalgebrarefreshercourseud953

UAustinX Linear Algebra  Foundations to Frontiers edx.org/course/linearalgebra
 Connections between linear transformations, matrices, and systems of linear equations.
 Partitioned matrices and characteristics of special matrices.
 Algorithms for matrix computations and solving systems of equations. Vector spaces, subspaces, and characterizations of linear independence. *Orthogonality, linear leastsquares, eigenvalues and eigenvectors
Data Science
 Udacity Introduction to Data Science https://www.udacity.com/course/introtodatascienceud359

Focuses on Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, and Data at Scale  Working with Big Data

Udacity Data Science Nanodegree https://www.udacity.com/course/dataanalystnanodegreend002

Learn to organize data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.

(optional) UCSD Data Science Micromasters Program (4 course program) https://www.edx.org/micromasters/datascience
 Four courses: Python for Data Science, Statistics and Probability in Data Science using Python, Machine Learning for Data Science, and Big Data Analytics Using Spark.
Machine Learning
 Udacity Intro to Machine Learning https://www.udacity.com/course/introtomachinelearningud120

How to extract and identify useful features that best represent data, learn a few of the most important machine learning algorithms, and how to evaluate the performance machine learning algorithms.

Coursera Machine Learning by Andrew Ng https://www.coursera.org/learn/machinelearning

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.

(with job guarantee?) Udacity Machine Learning Nanodegree https://www.udacity.com/course/machinelearningengineernanodegreend009
 Apply predictive models to massive data sets in fields like finance, healthcare, education, and more.
Literature:

An Introduction to Statistical Learning http://wwwbcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf

The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf
Other (after curriculum):
https://www.reddit.com/r/MachineLearning/wiki/index
Let me know your thoughts! Have you taken one of these courses before? Think there's too little programming or know of a better course/program? Chime in! Would be really grateful for any feedback.
Big Thanks!
sleepyowl
1 points • FirstBabyChancellor
I guess the refresher course on linear algebra did come to fruition:
https://www.udacity.com/course/linearalgebrarefreshercourseud953
1 points • dinorocket
If you plan on doing ML stuff I highly recommend this (it's very quick) and you can spend more time where you feel you lack understanding: https://www.udacity.com/course/linearalgebrarefreshercourseud953