Curious about AI? Find out how you can get started today! We’ll walk through several tools that are open source and commonly used in data science. I’ll also provide information about the algorithms being used and we’ll walk through a few different use-cases so you’ll better understand them.

1. 1
GETTING STARTED
WITH AI FOR FREE
A Primer
Dan Barker
Chief Architect - Archer
@barkerd427

2. 2
Why AI?
@barkerd427

3. 3
Leverage what you have
What you have:
▪ User data
▪ System data
▪ Free data!
What you solve:
▪ Fraud
▪ Disk space
▪ Pattern Discovery
@barkerd427

4. 4
Free data and research!!!
▪ https://toolbox.google.com/datasetsearch
▪ https://www.kaggle.com/
▪ https://scholar.google.com/
▪ https://arxiv.org/
@barkerd427

5. 5
Jupyter Notebooks and
Google Colab
Data Science for everyone
@barkerd427

6. 6
How to learn for free
▪ Jupyter Notebook
− Runs Locally
− Uses your local resources
▪ Google Colab
− Uses cloud resources
− Has GPUs and TPUs
− Can use local runtime
@barkerd427

7. 7
Go to Google Colab:
ai.danbarker.codes

8. 8
What is AI?
@barkerd427

9. 9
AI is like “Digital”
▪ Natural Language Processing
▪ Machine Learning
▪ Deep Learning
▪ Vision
▪ Robotics
@barkerd427

10. 10
Natural Language
Processing - NLP
@barkerd427

11. 11
What is NLP?
▪ Natural Language Understanding
− Sentiment Analysis
▪ Natural Language Generation
@barkerd427

12. 12
NLP Tools
▪ Python
− Natural Language Toolkit (NLTK)
− TextBlob
− SpaCy
− Textacy
− PyTorch-NLP
▪ Node
− Retext
− Compromise
− Natural
− Nlp.js
@barkerd427 https://opensource.com/article/19/3/natural-language-processing-tools

13. 13
Just starting?
▪ Python
− TextBlob
− Textacy
@barkerd427

14. 14
Let’s look at Textacy and TextBlob

15. 15
Machine Learning
@barkerd427

16. 16
What is Machine Learning?
▪ Supervised learning
− predictive
− labeled data
− regression and classification
− Nearest Neighbor, Naive Bayes, Decision Trees, Linear
Regression
▪ Unsupervised Learning
− descriptive
− clustering and association
− k-means, Agglomerative Hierarchical Clustering
@barkerd427

17. 17
ML Tools
▪ numpy
▪ pandas
▪ matplotlib
▪ SciPy
▪ scikit-learn
▪ PyTorch
@barkerd427

18. 18
Pandas, SciPy, and Boston
Housing Data

19. 19
Deep Learning
@barkerd427

20. 20
What is Deep Learning?
▪ Type of Machine Learning
▪ Hierarchical feature learning
▪ Deep Neural Networks
▪ Convolutional Neural Networks
▪ Generative Adversarial Networks
@barkerd427

21. 21
Deep Learning Tools
▪ TensorFlow
▪ PyTorch
▪ Keras
▪ Caffe
▪ Microsoft Cognitive Toolkit
▪ MXNet
▪ Chainer
@barkerd427

22. 22
The Bias
@barkerd427

23. 23 @barkerd427

24. 24
It’s a brand new world
▪ cognitive biases have been studied for years
− we still can’t avoid them
▪ algorithmic biases are barely understood
− we don’t know what we don’t know
− first covered in 1976
− not again for 30 years
− and nothing significant until recently
@barkerd427

25. 25
Complex Systems Theory
▪ we don’t actually understand
cognitive biases
▪ we don’t accurately attribute
success/failure
▪ move forward, but mindfully!
@barkerd427

26. 26
Resources
▪ https://www.kdnuggets.com/
▪ https://www.coursera.org/learn/machine-learning
▪ https://towardsdatascience.com/
▪ https://opensource.com/
▪ https://colab.research.google.com
▪ https://www.kaggle.com/
▪ https://toolbox.google.com/datasetsearch
▪ https://scholar.google.com/
▪ https://arxiv.org/
▪ https://weaponsofmathdestructionbook.com/
@barkerd427

27. 27
Dan Barker
dan@danbarker.codes
danbarker.codes
dan.barker@rsa.com
rsa.com
@barkerd427