Machine Learning

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:

"You should get into data science, it's cool and there is a need."
"Here are some examples of problems solved with machine learning."
"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

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

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

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

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

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

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

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

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:

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

Machine Learning

getting started

Course Plan

What is Data Science? ( IBM )

Tools for Data Science

Understanding Machine Learning with Python

Building Machine Learning Models in SQL Using BigQuery ML

Preparing Data for Machine Learning

Preparing Data for Feature Engineering and Machine Learning

Building End-to-end Machine Learning Workflows with Kubeflow

Data Wrangling with Pandas for Machine Learning Engineers

Building Your First scikit-learn Solution

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

Network Analysis in Python: Getting Started

Building Features from Numeric Data

More

Data Analysis with Python

Data Visualization with Python

ThinkStats2 book

Learn ML with Tensor Flow

algorithms

Concepts and Techniques

tools

visualize a tensor

sample data

blogs

Cool Projects

Data leaks

Questions and Investigation

Reading Room

NIPS - Neural Information Processing Systems

Models to check out

Demos and Labs

Image processing

Chapter

linear regression in 6 lines of code

Conferences

Navigation menu

Search