Machine Learning

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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

What is Data Science? ( IBM )

Data Science Orientation Issued by IBM

By IBM
August 2021
Beginner
1h 53m
Offered by: coursera
https://www.coursera.org/learn/what-is-datascience
Status: Completed
Grade Achieved 95.83%
https://coursera.org/share/70426ac18b9271d5b95a8d787d60c2b2

( https://www.credly.com/org/ibm/badge/data-science-orientation)

Take aways / Key points:

  • A data scientist uses data to find solutions to problems and tells stories to communicate their findings.
  • Data science is the study of large quantities of data, which can reveal insights that help organizations make strategic choices.
  • Qualities of an analyst as per Murtaza Haider ( Ryerson University / Ted Rogers School of business):
    • curious
    • judgemental
    • argumentative
  • Terms to know:
    • Overfitting.
    • In-sample forecast

Commentary:

This course was weak.

The course basically makes three points:

  1. "You should get into data science, it's cool and there is a need."
  2. "Here are some examples of problems solved with machine learning."
  3. "Here is how to make a report. Cover page, summary , conclusion etc."

All three are low value in my opinion.

I want to get to the meat and start "doing it" not talking about how great it is.

Tools for Data Science

Instructurs:
Aije Egwaikhide - Senior Data Scientist -IBM
Svetlana Levitan - Senior Developer Advocate with IBM Center for Open Data and AI Technologies
Romeo Kienzler - Chief Data Scientist, Course Lead - IBM Watson IoT
When: August 2021
level: ??
Time :
Offered by: coursera
https://www.coursera.org/learn/open-source-tools-for-data-science
Status: In progress


Understanding Machine Learning with Python

By Jerry Kurata
May 16, 2016
Beginner
This is rated 4.52821 (638)
1h 53m
Offered by: pluralsight
https://app.pluralsight.com/library/courses/python-understanding-machine-learning/table-of-contents
Status: Not started


Work Flow Guidelines:

1. Early Steps are most important. Each step depends o previous steps.

2. Expect to move backwards. Later knowledge effects previous steps.

3. Data is never as you need it. Data will have to be altered.

4. More data is better. More data leads to better results.

5. Don't pursue a bad solution. reevaluate, fix, or quit.

Building Machine Learning Models in SQL Using BigQuery ML

Building Machine Learning Models in SQL Using BigQuery ML
By Janani Ravi
Nov 19, 2018
Beginner
This is rated 4.92308 (13)
1h 27m
Offered by pluralsight
https://app.pluralsight.com/library/courses/sql-bigquery-ml-building-machine-learning-models/table-of-contents
Status: Not started


Preparing Data for Machine Learning

Preparing Data for Machine Learning By Janani Ravi Oct 28, 2019 Beginner This is rated 4.4375 (32) 3h 24m https://app.pluralsight.com/library/courses/preparing-data-machine-learning/table-of-contents


Preparing Data for Feature Engineering and Machine Learning

Preparing Data for Feature Engineering and Machine Learning By Janani Ravi Oct 28, 2019 Beginner This is rated 4.64 (25) 3h 17m https://app.pluralsight.com/library/courses/preparing-data-feature-engineering-machine-learning/table-of-contents


Building End-to-end Machine Learning Workflows with Kubeflow

Building End-to-end Machine Learning Workflows with Kubeflow By Abhishek Kumar Apr 23, 2020 Beginner No Rating 3h 30m https://app.pluralsight.com/library/courses/building-end-to-end-machine-learning-workflows-kubeflow/table-of-contents

Data Wrangling with Pandas for Machine Learning Engineers

Data Wrangling with Pandas for Machine Learning Engineers
By Mike West
Aug 08, 2018
Beginner
This is rated 3.82051 (39)
1h
https://app.pluralsight.com/library/courses/pandas-data-wrangling-machine-learning-engineers/table-of-contents


Building Your First scikit-learn Solution

Building Your First scikit-learn Solution
By Janani Ravi
May 01, 2019
Beginner
This is rated 4.7377 (61)
2h 7m
https://app.pluralsight.com/library/courses/building-first-scikit-learn-solution/table-of-contents


Build, Train, and Deploy Your First Neural Network with TensorFlow

Build, Train, and Deploy Your First Neural Network with TensorFlow
By Jerry Kurata
Jan 22, 2020
Beginner
This is rated 4.58333 (36)
2h 47m
https://app.pluralsight.com/library/courses/build-train-deploy-first-neural-network-tensorflow/table-of-contents


Network Analysis in Python: Getting Started

Network Analysis in Python: Getting Started
By Artur Krochin
Apr 09, 2019
Beginner
This is rated 4.92857 (14)
1h 58m
https://app.pluralsight.com/library/courses/python-network-analysis-getting-started/table-of-contents


Building Features from Numeric Data

Building Features from Numeric Data
By Janani Ravi
Apr 07, 2019
Beginner
This is rated 5 (15)
2h 25m
https://app.pluralsight.com/library/courses/building-features-numeric-data/table-of-contents

More

https://app.pluralsight.com/library/courses/spark-2-building-machine-learning-models/table-of-contents

https://app.pluralsight.com/library/courses/applying-machine-learning-data-gcp/table-of-contents

https://app.pluralsight.com/library/courses/demystifying-machine-learning-operations/table-of-contents

https://app.pluralsight.com/library/courses/managing-machine-learning-projects-google-cloud/table-of-contents

Data Analysis with Python

https://cognitiveclass.ai/courses/data-analysis-python

Data Visualization with Python

https://www.coursera.org/learn/python-for-data-visualization

https://cognitiveclass.ai/courses/data-visualization-python ( same course ? )

ThinkStats2 book

https://github.com/AllenDowney/ThinkStats2

of interest: brfss data processing ( https://www.cdc.gov/brfss/annual_data/annual_2020.html )


Learn ML with Tensor Flow

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/
1D concolution net for sequential data.
https://www.enterprisedb.com/blog/machine-learning-capacity-management

Concepts and Techniques

onehotencorder: TOREAD

https://towardsdatascience.com/choosing-the-right-encoding-method-label-vs-onehot-encoder-a4434493149b

scalers:

https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py

scaling features to a range
https://scikit-learn.org/stable/modules/preprocessing.html#scaling-features-to-a-range

tools

python + libs

image labeling

https://github.com/Labelbox/Labelbox

TensorFlow Playground

http://playground.tensorflow.org

visualize a tensor

reference: https://stackoverflow.com/questions/68510066/how-to-plot-a-3dimensional-tensor-as-a-tube-with-different-colors

import matplotlib.pyplot as plt
import numpy as np

axes = [16, 16, 16] # change to 64
traj = np.random.choice([-1,1], axes)

alpha = 0.9
colors = np.empty(axes + [4], dtype=np.float32)
colors[traj==1] = [1, 0, 0, alpha]  # red
colors[traj==-1] = [0, 0, 1, alpha]  # blue

fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.voxels(traj, facecolors=colors, edgecolors='black')
plt.show()

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

Light House Labs data challenge


https://github.com/a-martyn/ISL-python Introduction to statistical learning

Flight price prediction:

anovos feature engineering orkshop: https://www.crowdcast.io/e/feature-engineering-workshop

NIPS - Neural Information Processing Systems


Models to check out

from: https://shopify.engineering/introducing-linnet-using-rich-image-text-data-categorize-products

  • Multi-Lingual BERT for text
  • MobileNet-V2 for images

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

https://jakevdp.github.io/PythonDataScienceHandbook/

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