Machine Learning

Created by Saif Abid, Chief Technology Officer at bitstrapped
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What everyone's up to

Studying Machine Learning
Working on ML-Zoomcamp Week 06 - Decision Trees

  • Decision Trees (DTs) are used as a supervised method to do Regression and Classification. 
  • DTs are based on the decisions taken based on features splits. For example: Number of Rooms in the house ( 0, 1, 2, >3) and based on this the tree will have decision nodes. 
  • Each leaf will represent a decision. For example: House is Expensive vs. House is not expensive. 
  • You can tune the parameters of the tree such as: the depth of the tree, ideally, you don't want to do a full depth, as you will end up overfitting. 
  • Last but not least, you can you ensemble methods, where basically you can have a number of estimators and the decision will be based on averaging the decision of all estimators. 
  • Yet another interesting technique is the use of XGBoost as an ensemble algorithm, which is recognized by its performance. It works using bootstrap technique for sampling from the original dataset. 

Wrote a Blog Post
KDnuggets blog
Used Machine Learning
Used an API
FastAPI API
+ 3
Effortless way to develop and deploy your machine learning API using #FastAPI and Deta.

KDnuggets 💖 Deepnote Cells

This looks so smooth and amazing. I just feel so good to see deepnote cell on another platform.


GitHub: https://lnkd.in/ebcqyrAT
Deepnote Project: https://lnkd.in/eefvCMrc


https://www.kdnuggets.com/2021/10/deploying-first-machine-learning-api.html#.YWkV0rHflbg.link

Shipped an Atomic Essay
Taught Machine Learning
It looks like @Tesla & friends are maturing technology.
I think they're a great case study to see how machine learning can contribute to a great solution.

In this atomic essay for #ship30for30 I talk about maturity of machine learning, AI, and if winter is coming.

It also lives here
Aug 01, 2019
Learned Matlab
Studying Data Science
Used Machine Learning
Shadowed a Graduate Student
+ 2
During my time as a lab technician in Sjulson Lab, I helped the lab optimize their data analysis by studying and analyzing various dimensional reduction algorithms, including PCA, gPCA, ePCA, lPCA, and NMF. I determined which of these algorithms effectively reduced the gigabytes of neuronal spike data into visual trends that the Dr. Sjulson could use in his research. I then presented these findings to the rest of the lab. Near the end of my term at the lab, I also began studying the MATLAB machine learning interface to classify theta and ripple cells by their firing patterns. 

This was my first professional programming job. Working as the only high schooler in a lab full of graduate students, I felt intimidated, yet my mentors were happy to help lead me in the right direction. The fundamental design patterns I learned from Dr. Sjulson still follow me into the work I do today. I am very grateful to Dr. Sjulson and the rest of his staff for giving me this opportunity and I wish them the best in their future endeavors. 

Repository: https://github.com/jakejack13/DimensionalReduction
Lab website: https://sjulsonlab.org/ 
Laboratory Technician, Sjulson Lab
Submitted Hacktoberfest PR
Published a dataset
Used NLP
Used Machine Learning
Wrote Documentation
+ 3
I have contributed into #hacktoberfest2021 uploading four audio #datasets on #DagsHub and #GitHub with complete documentation. You can now access all open-source dataset at one place.
GitHub: https://lnkd.in/dj8TD5ht
Public Domain Sounds: https://lnkd.in/dpvggTYi
Urdu Dataset: https://lnkd.in/dh8jenjh
EmoSynth: https://lnkd.in/dqc4piZq
Voice Gender Detection: https://lnkd.in/dZ3WNTGz
Shipped an Atomic Essay
Taught Machine Learning
You can tell how hard something will be by in #machinelearning.

How?

By how solved it already is.
Revolutionary, I know.

In this atomic essay for #ship30for30, I talk about the four categories of problems you will ever face.

How Hard is Your Machine Learning Problem?


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