Machine Learning

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

Error creating thumbnail: File missingError creating thumbnail: File missing ( 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

• https://www.tensorflow.org/resources/learn-ml?

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

• Caffe deep learning framework

• SystemML- a Universal Translator for Big Data and Machine Learning
  • http://www.ibm.com/blogs/think/2015/11/24/introducing-a-universal-translator-for-big-data-and-machine-learning/
  • https://github.com/SparkTC/systemml/
  • http://researcher.watson.ibm.com/researcher/view_group.php?id=3174
  • https://developer.ibm.com/open/systemml/

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

• an good overview the the data science cycle in a general sense: https://cloud.google.com/ml-engine/docs/tensorflow/data-prep

• What a Deep Neural Network thinks about your #selfie

• Detecting tanks https://www.jefftk.com/p/detecting-tanks

• https://analyticsdefined.com/mining-enron-emails/

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

• https://opendatascience.com/blog/

• Kaggle competitions: https://www.kaggle.com/

• University of Toronto Machine Learning http://www.learning.cs.toronto.edu/theses.html

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

• Eight guidelines that will help you execute your data science initiatives with excellence - /8 Guidelines: Data Science Initiative Excellence - local copy + notes

Flight price prediction:

• https://github.com/dsrscientist/Data-Science-ML-Capstone-Projects
• https://medium.com/code-to-express/flight-price-prediction-7c83616a13bb

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

NIPS - Neural Information Processing Systems

• 2015 https://nips.cc/Conferences/2015
• 2016 https://nips.cc/Conferences/2016

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