#!/usr/bin/env python
# coding: utf-8

# # What is Machine Learning, and how does it work? ([video #1](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1))
# 
# Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).

# ![Machine Learning](images/01_robot.png)

# ## Agenda
# 
# - What is Machine Learning?
# - What are the two main categories of Machine Learning?
# - What are some examples of Machine Learning?
# - How does Machine Learning "work"?

# ## What is Machine Learning?
# 
# One definition: "Machine Learning is the semi-automated extraction of knowledge from data"
# 
# - **Knowledge from data**: Starts with a question that might be answerable using data
# - **Automated extraction**: A computer provides the insight
# - **Semi-automated**: Requires many smart decisions by a human

# ## What are the two main categories of Machine Learning?
# 
# **Supervised learning**: Making predictions using data
#     
# - Example: Is a given email "spam" or "ham"?
# - There is an outcome we are trying to predict

# ![Spam filter](images/01_spam_filter.png)

# **Unsupervised learning**: Extracting structure from data
# 
# - Example: Segment grocery store shoppers into clusters that exhibit similar behaviors
# - There is no "right answer"

# ![Clustering](images/01_clustering.png)

# ## How does Machine Learning "work"?
# 
# High-level steps of supervised learning:
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# 1. First, train a **Machine Learning model** using **labeled data**
# 
#     - "Labeled data" has been labeled with the outcome
#     - "Machine Learning model" learns the relationship between the attributes of the data and its outcome
# 
# 2. Then, make **predictions** on **new data** for which the label is unknown

# ![Supervised learning](images/01_supervised_learning.png)

# The primary goal of supervised learning is to build a model that "generalizes": It accurately predicts the **future** rather than the **past**!

# ## Questions about Machine Learning
# 
# - How do I choose **which attributes** of my data to include in the model?
# - How do I choose **which model** to use?
# - How do I **optimize** this model for best performance?
# - How do I ensure that I'm building a model that will **generalize** to unseen data?
# - Can I **estimate** how well my model is likely to perform on unseen data?

# ## Resources
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# - Book: [An Introduction to Statistical Learning](https://www.statlearning.com/) (section 2.1, 14 pages)
# - Video: [Learning Paradigms](https://www.youtube.com/watch?v=mbyG85GZ0PI&t=2162s) (13 minutes, starting at 36:02)

# ## Comments or Questions?
# 
# - Email: <kevin@dataschool.io>
# - Website: https://www.dataschool.io
# - Twitter: [@justmarkham](https://twitter.com/justmarkham)
# 
# © 2021 [Data School](https://www.dataschool.io). All rights reserved.