Introductory talk on Machine Learning for about an hour

1. ML 101
SAMEER MAHAJAN

3. ML Paradigm Shift
Insight
Intelligence
Model
f
input
Traditional
Programming
output
ML
input / data / features /x
output / labels / y
f

5. Types of problems and solutions
• Regression: real valued output, predicting house prices
• Classification: product reviews
• Clustering: unsupervised learning, document retrieval
• Recommender systems: product recommendation
• Deep learning: neural networks
• Time series: forecasting
• NLP / NLU: GPT3
• Image Recognition – Computer Vision
• Acoustics

6. Regression
• Predicting house prices
• Linear regression
• Logistic regression
• Multi variate
• Polynomial Regression
• Ridge
• Lasso

7. y = f(x) = w0 + w1 * x
Linear regression

8. Cost / Loss Function
w0
w1
RSS(w0, w1)

9. Gradient Descent
x
y
x0, y0
x1, y1
step
x2, y2
xmin, ymin
…
yn = yn – 1 – alpha * d yn – 1 /d xn – 1
gradient
gradient = 0
alpha = learning rate = xn - xn – 1
Gradient / slope at point (xn,yn) = d yn /d xn

11. Tools & Technologies
• Jupyter notebook
• Python
• Numpy
• Pandas
• Matplotlib
• Scikit learn, Tensorflow, Pytorch

12. Solving Linear Regression
import pandas as pd
sales = pd.read_csv('seattle_house_sales.csv')
from sklearn import linear_model
regr = linear_model.LinearRegression()
sqft_model = regr.fit(train.sqft_living.values.reshape(-1, 1),
train.price.values.reshape(-1, 1))
import matplotlib.pyplot as
plt plt.scatter(train['sqft_living'], train['price'], color='black')
plt.plot(train['sqft_living'], sqft_model.predict(train['sqft_living'].
values.reshape(-1, 1)), color='blue', linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()

13. Classification
• Sentiment analysis
• Analyze restaurant reviews

16. Clustering
• Unsupervised learning
• Document retrieval

17. #occurrences of a word in a document

18. Document Vectors
word1 word2 word3 …… wordn
Document1 ( N11 N12 N13 ….. N1n)
Document2 ( N21 N22 N23 ….. N2n)
.
.
.
Dcoumentm ( Nm1 Nm2 Nm3 ….. Nmn)

20. Recommenders
• Netflix movie recommendations based on user ratings
• Song recommender based on user listen count
• Facebook friend recommender
• Popularity based: not personalized
• Classification based: features may not be available
• Co-occurrence based: who bought this also bought…

26. Time series - fbprophet
• Semi supervised
• Time as feature
• Data as y
• Components
• Trend : upward / downward
• Seasonality : day of the week
• Cycle : every 5 years
• Noise
• Usually a combination of above components
• Forecasting

27. Future
• Acoustics - Speech recognition
• Video processing
• Robotics
• Alpha Go Zero
• Self driving cars

28. Challenges
• Model selection
• Feature engineering
• Scaling
• Data
• Model
• Special architectures
• Parallel processing
• GPUs

29. Next steps – Online courses
• https://github.com/sameermahajan/MLWorkshop
• Coursera
• Machine learning specialization
• Machine learning by Andrew Ng, Stanford
• Deep learning specialization
• Udemy
• Machine Learning A to Z
• Deep Learning A to Z
• Udacity
• Machine Learning Engineer
• Deep Learning Foundation Nanodegree Program

30. Next steps - contd
• Online competitions
• kaggle
• Online datasets to play with
• https://www.kaggle.com/datasets
• http://mldata.org/repository/data/
• http://archive.ics.uci.edu/ml/index.php
• http://deeplearning.net/datasets/
• https://deeplearning4j.org/opendata
• https://catalog.data.gov/dataset
• Formulate your own problem, gather data, model, evaluate and keep
refining it further

31. Q&A