The document provides an overview of machine learning concepts including:
1) It describes the three main components of machine learning - inputs (data), algorithms (models), and outputs (predictions or classifications).
2) It discusses different types of machine learning algorithms including supervised learning, unsupervised learning, and reinforced learning and provides examples.
3) It provides examples of applying machine learning algorithms like linear regression, logistic regression, Naive Bayes, and neural networks to problems like predicting housing prices, spam detection, and university acceptance.
23. Linear Regression
Size of the house (1000 ft squared)
70 lakhs
1.6 Crore
?
15
10
20
7
5 12
5
11
Source: https://www.youtube.com/watch?v=IpGxLWOIZy4
70. Supervised learning VS. PLUS
Unsupervised learning
Unsupervised learning as feature engineering
E.g.: clustering + KNN, Matrix Factorization
One of the “Tricks” in Deep Learning is how it combines unsupervised/supervised learning
Stacked Autoencoders, training of CNN
Source: Quora
75. The untold story of
Data Science vs. and ML engineering
Is ML at a point at which you don’t have to be a data scientist to take advantage of it?
There are good tools to get started, BUT
For state-of-art performance, one needs rigorous quantitative understanding
Source: Quora
76. The data-driven ML innovation funnel
Data Research
Data research & hypothesis
building ->Data Science
AB Testing
Online experimentation, AB Testing analysis->Data Science
ML Exploration– Product Design
ML solution building &
implementation ->ML Engineering
Source: Quora