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1/11 Wanna learn machine learning?
Stop following "influencers" who sell snake oil for self promotion. There are plenty of incredible scientists, engineers, and professors who've made a lot of materials available for free.
#MachineLearning #DataScience #Learn
Stop following "influencers" who sell snake oil for self promotion. There are plenty of incredible scientists, engineers, and professors who've made a lot of materials available for free.
#MachineLearning #DataScience #Learn
2/11 MIT OpenCourseWare and Khan Academy both have incredible materials for free. There are tons of free textbooks in the public domain as well. I'll start posting links in this thread.
#learning #MachineLearning #DataScience
#learning #MachineLearning #DataScience
3/11 Start with the basics of both the "math" part of Data Science and the CS part of Data Science. Again, plenty of resources for both.
#learning #MachineLearning #DataScience #math #cs
#learning #MachineLearning #DataScience #math #cs
4/11 Let's start with the math part. The first thing I recommend learning is an intro to probability and stats. A couple options:
https://www.khanacademy.org/math/statistics-probability)
https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/
Good free textbooks:
https://open.umn.edu/opentextbooks/textbooks/287
http://www.utstat.toronto.edu/mikevans/jeffrosenthal/book.pdf
#learn #stats #datascience
https://www.khanacademy.org/math/statistics-probability)
https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/
Good free textbooks:
https://open.umn.edu/opentextbooks/textbooks/287
http://www.utstat.toronto.edu/mikevans/jeffrosenthal/book.pdf
#learn #stats #datascience
5/11 The second thing I recommend is learning some Linear Algebra. Again, an introductory course should suffice. Again, the options:
https://www.khanacademy.org/math/linear-algebra
https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
If you prefer textbooks, DM me and I can send you some links.
#learn #linearalgebra #datascience
https://www.khanacademy.org/math/linear-algebra
https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
If you prefer textbooks, DM me and I can send you some links.
#learn #linearalgebra #datascience
6/11 Onto the CS fundaments.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/lecture-slides-and-files/index.htm
#learn #computerscience #cs #datascience
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/lecture-slides-and-files/index.htm
#learn #computerscience #cs #datascience
7/11 One of the key aspects of being a data scientist is to be able to pull data. They almost always use SQL (or SQL like languages to do this). If I had to quantify the amount of time spent on each task as a Data Scientist, SQL would win, hands down.
#learn #SQL #data
#learn #SQL #data
8/11 The language of SQL itself is straightforward, but it's important to understand things like cardinality, the level of the data, etc.
So I recommend the following:
https://www.udacity.com/course/intro-to-relational-databases--ud197
https://www.khanacademy.org/computing/computer-programming/sql
and
https://www.w3schools.com/sql/ for more practice
#learn #sql #ds
So I recommend the following:
https://www.udacity.com/course/intro-to-relational-databases--ud197
https://www.khanacademy.org/computing/computer-programming/sql
and
https://www.w3schools.com/sql/ for more practice
#learn #sql #ds
9/11 Onto the meatier part of ML.
The best ML course, hands down is Prof. Andrew Ng's course on Coursera.
https://www.coursera.org/learn/machine-learning
There are also some incredible textbooks available for free, the best among them is:
https://web.stanford.edu/~hastie/Papers/ESLII.pdf
#learn #machinelearning #datascience
The best ML course, hands down is Prof. Andrew Ng's course on Coursera.
https://www.coursera.org/learn/machine-learning
There are also some incredible textbooks available for free, the best among them is:
https://web.stanford.edu/~hastie/Papers/ESLII.pdf
#learn #machinelearning #datascience
10/11 More often than not, you're going to be using cloud tools to build and deploy your models, so I would recommend going through the ML courses made available by the industry stalwarts like AWS, Azure, and IBM.
#learn #machinelearning #cloud #datascience
#learn #machinelearning #cloud #datascience
11/11 Here are the links:
https://aws.amazon.com/training/learning-paths/machine-learning/
https://aischool.microsoft.com/en-us/machine-learning/learning-paths
https://www.ibm.com/services/learning/ailearning
A great textbook to accompany this part of your journey is (unfortunately not free):
https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
FIN
#learn #datascience #machinelearning #ds #ml
https://aws.amazon.com/training/learning-paths/machine-learning/
https://aischool.microsoft.com/en-us/machine-learning/learning-paths
https://www.ibm.com/services/learning/ailearning
A great textbook to accompany this part of your journey is (unfortunately not free):
https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
FIN
#learn #datascience #machinelearning #ds #ml
