Machine Learning: Difference between revisions

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[http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf understanding machine learning theory algorithms]
[http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf understanding machine learning theory algorithms]
== Course Plan ==
=== Understanding Machine Learning with Python ===
:By Jerry Kurata
:May 16, 2016
:Beginner
:This is rated 4.52821 (638)
:1h 53m
:( pluralsight )


== algorithms ==
== algorithms ==

Revision as of 18:02, 2 February 2021

getting started

google://getting started with machine learning

https://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience - in progress

https://www.quora.com/I-want-to-learn-machine-learning-Where-should-I-start

http://thunderboltlabs.com/blog/2013/11/09/getting-started-with-machine-learning/

http://machinelearningmastery.com/machine-learning-for-programmers/

https://www.kaggle.com/dfernig/reddit-comments-may-2015/the-biannual-reddit-sarcasm-hunt/code

course: at coursera https://www.coursera.org/learn/machine-learning/home/week/1

understanding machine learning theory algorithms

Course Plan

Understanding Machine Learning with Python

By Jerry Kurata
May 16, 2016
Beginner
This is rated 4.52821 (638)
1h 53m
( pluralsight )

algorithms

random forest
https://medium.com/rants-on-machine-learning/the-unreasonable-effectiveness-of-random-forests-f33c3ce28883
Nearest Neighbors Classification
http://scikit-learn.org/stable/modules/neighbors.html
lstm
http://blog.echen.me/2017/05/30/exploring-lstms/

tools

python + libs

image labeling

https://github.com/Labelbox/Labelbox

TensorFlow Playground

http://playground.tensorflow.org

sample data

http://archive.ics.uci.edu/ml/datasets/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions

blogs

http://blog.datumbox.com/

Cool Projects

https://github.com/aficnar/slackpolice


Aerospace Controls Lab
http://acl.mit.edu/
https://www.youtube.com/channel/UCVTxuaJsdMrk3UEcHVll9Yg


https://qz.ai/spotting-circling-helicopters/

Data leaks

When data associated iwth the data set gives away the target data.

Primarily of concern in competition.

Unexpected data.

refrence: https://www.coursera.org/learn/competitive-data-science/lecture/5w9Gy/basic-data-leaks

Future peaking - using time series data that's not in the target time period, for example in the future.

Meta data leaks - for example file meta data, zip file meta data, image file meta data.

information hidden in ID and hashes,

and information hidden in row order and possibly duplicate rows

Questions and Investigation

What are "ground truths"?

corteges - what is this word

/Courera's Competitive Data Science Course

Reading Room

Past solutions

http://ndres.me/kaggle-past-solutions/
https://www.kaggle.com/wiki/PastSolutions
http://www.chioka.in/kaggle-competition-solutions/
https://github.com/ShuaiW/kaggle-classification/

https://towardsdatascience.com/how-to-use-dataset-in-tensorflow-c758ef9e4428

https://towardsdatascience.com/how-to-train-neural-network-faster-with-optimizers-d297730b3713

NIPS - Neural Information Processing Systems

Demos and Labs

https://codelabs.developers.google.com/codelabs/scd-babyweight2/index.html#0

https://github.com/GoogleCloudPlatform/training-data-analyst

Jaz Quick start
use your GPU / TPU for ML:
https://jax.readthedocs.io/en/latest/notebooks/quickstart.html
https://github.com/cbrownley/foundations-for-analytics-with-python

Image processing

Christopheraburns / gluoncv-yolo-playing_cards
https://github.com/Christopheraburns/gluoncv-yolo-playing_cards/blob/master/Yolov3.ipynb

Chapter

https://github.com/FlorianMuellerklein/Machine-Learning

Improving our neural network (96% MNIST) https://databoys.github.io/ImprovingNN/

https://iamtrask.github.io/2015/07/12/basic-python-network/

https://plot.ly/python/create-online-dashboard/

https://www.anaconda.com/download/

http://jupyter.org/install.html

https://medium.com/towards-data-science/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464

linear regression in 6 lines of code

source: https://towardsdatascience.com/linear-regression-in-6-lines-of-python-5e1d0cd05b8d

pip install scikit-learn
import numpy as np
import matplotlib.pyplot as plt  # To visualize
import pandas as pd  # To read data
from sklearn.linear_model import LinearRegression
data = pd.read_csv('data.csv')  # load data set
X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array
Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column
linear_regressor = LinearRegression()  # create object for the class
linear_regressor.fit(X, Y)  # perform linear regression
Y_pred = linear_regressor.predict(X)  # make predictions
plt.scatter(X, Y)
plt.plot(X, Y_pred, color='red')
plt.show()

Conferences

  • TMLS2020 - Toronto Machine Learning Summit 2020

/scratch notes