Machine Learning – Series Content

machine-learning/00_series-intro_outline.md

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+# Series Intro Outline
+
+Still looking at ideas more than syntax/diction at this point.
+
+## Code Rabbit Intro
+* I'm guessing we'll have some sort of standardized theme music/opening animation here.
+
+## What is machine learning?
+* Machine Learning is the process of using agorithms to automatically analyze data to
+  make predictions or identify interesting trends
+  * The term "data" used to refer to tables of facts and figures
+  * "Data" can now mean anything that has been recorded
+    * Pictures of cats, daily rainfall, the words in a novel, the number of views on a video
+* What sort of trends and predictions? (We'll probably only use a few of these)
+  * Companies use machine learning to identify which employees are most likely to quit their jobs
+  * Genetecists use machine learning to predict how genes in your DNA will express themselves
+  * advertisers use machine learning to choose which ad appeals most to you
+  * Real Estate companies use machine learning to predict the price of a house
+  * The NSA uses machine learning to identify potential threats to US citizens
+  * Meteorologists use machine learning to predict the weather.
+  * Youtube uses machine learning to idenify which videos you might want to watch
+  * Amazon uses machine learning to predict what products you are most interested in
+  * Personal assistants use machine learning to both understand commands and use those commands
+    to give the user useful information
+## Why is machine learning so important/powerful?
+* Machine Learning enables computers to do things that only humans could do up until recently
+  * Recognize faces and voices, drive cars, 
+* Machine Learning makes things previously done by computers faster, more accurate, and automatic
+* Machine Learning is continually becoming more powerful
+  * Computers are becoming faster and memory is becoming cheaper
+  * Companies are becoming more creative with what they consider data
+    * See points on pictures of cats, etc. above
+  * Companies have realized the importance of data and are now collecting more than ever.
+## Conclusion
+* A seasoned machine learning practitioner can get computers to do things that are impossible 
+  without machine learning
+* The high level of interest and talent involved the field of machine learning insures that
+  applications will continue to get more impressive in the future.

machine-learning/series-structure.md

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+# Proposed Series Structure
+
+## Intro
+1. What is machine learning?
+    * Define Machine Learning
+    * Why is it important?
+
+## Machine Learning Basics
+
+I'm actually not sure where we would like to start
+
+2. The anatomy of a dataset
+    * Tabular vs non-tabular data
+    * Independend and Dependent variables
+3. Supervised vs Unsupervised Learning
+4. Supervised Learning: Regression and Classification
+5. Interpreting Results
+    * Precision vs. Recall
+    * Standard Error
+    * Overfitting
+    * Correlation vs. Causation
+
+## The Algorithms
+
+8. Linear Regression
+9. Logistic Regression
+10. K Nearest Neighbors
+11. K Means Clustering
+12. Decision Trees and Random Forests
+13. Extreme Gradient Boosting Machines
+14. Neural Networks
+
+

machine-learning/series_brainstorm.md

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+# Machine Learning
+
+(!) = Definitely needs to be reworded or explained more
+
+Standardized CodeRabbit introduction
+
+Introduction
+------------------------------------------------------------------------------
+* Data is becoming more easily available
+* Data sets are becoming larger
+* Both the private companies and government institutions are becoming better at
+  generating, streaming, and storing data
+
+* We need algorithms/models (!) that can help us gain insight from increasingly 
+  large and complex datasets
+
+* Machine Learning is a discipline that studies how computers can automatically 
+  analyze and draw conclusions from data. (!)
+  * Even though technically it is a subfield of mathematics and does not 
+    necessarily need to use computers.
+
+* Examples (I'll probably choose three):
+  * Facebook and Snapchat use machine learning to build technology that can 
+    recognize faces
+  * Online advertisers use machine learning to offer you the products you are 
+    most likely to buy
+  * Large banks use machine learning to detect credit fraud
+  * Self-Driving cars use machine learning to saftely navagate through roads.
+  * Hedge Funds use machine learning to predict movements in the stock market.
+  * Netflix uses machine learning to recommend movies and TV shows you might 
+    like.
+
+Body
+------------------------------------------------------------------------------
+I'm actually not sure where to go with the body for the first video's transcript...
+
+Here are some potnetial options:
+
+1. How are institutions getting more data?
+    * Many free applications on both your browser and your phone are designed to
+      record and store your data
+      * retweets, likes, views, right swipes, your location, and even your tax 
+        return information are all saved by these applications
+    * Some companies store your credit card information, social security number,
+      and spending history
+      * This is why data breaches like those at Target and Experian are so
+        dangerous (!)
+    * Some countries have strong privacy laws that require companies to keep
+      your data secure and anonymous (!), others do not
+      * This is a very controversial issue
+
+2. The Inner workings of a machine learning model
+    * There are two types of models:
+      * Classification - We try to predict a category
+        * Is an email important or spam?
+        * Is this a picture of a cat or a dog?
+      * Regression - We try to predict a number
+        * How much does this house cost?
+        * What is this person's credit score?
+    * We can have algorithms generate these models for us
+      * Most algorithms use calculus and matrix algebra to create models
+      * luckily, most programming languages have libraries or packages that allow
+        us to use these algorithms while the pre-packaged code deals with the
+        heavy lifting in the background. (!)
+
+  3. What are the steps involved in training a machine learning model?
+      * Aquire and preprocess data
+        * Getting data
+          * Companies that provide servies or applications can record user
+            or client activity (See retweets, likes, etc. from earlier)
+          * Governments often get data through surveys
+          * Buy data from large companies
+          * There is even data available for free online
+            * [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/index.php)
+            * [Kaggle](https://www.kaggle.com/datasets)
+            * [US Government](https://www.data.gov/)
+            * Web Scraping
+        * Preprocessing Data
+          * Many models require data to be in a specific format before they can
+            work on a dataset.
+          * It is very common for models to require tabular data.
+            * Tabular data is data that can be stored in a table or spreadsheet
+              with one column for each variable and one row for each observation.
+            * Some specialized models can take in non-tabular data with very little
+              intervention
+              * Convolutional Neural Networks can train on pictures
+              * Recurrent Neural Networks can train on sounds
+      * Train a model
+        * See Regression vs Classification from earlier
+        * There is a wide array of models that are useful for different circumstance
+          * Linear Regression
+          * Logistic Regression
+          * K Nearest Neighbors
+          * Decision Trees
+          * Support Vector Machines
+          * Random Forests
+          * Extreme Gradient Boosting
+          * Neural Networks
+          * This does not include Unsupervised Learning Algorithms
+            * K Means Clustering
+            * Heirarchical Clustering
+            * Singular Value Decomposition
+            * Principle Components Analysis
+      * Interpret results and draw conclusions
+        * The two most common metrics machine learning researchers and engineers
+          use to evaluate the quality of a model's results are precision and
+          recall.
+          *  Precision 
+        * Models can suffer from overfitting
+          * Drawing conclusions that are either too complex or based on too
+            small an amout of data
+        * Models can be misinterpreted
+          * Correlation does not guarantee causation
+            * (I'm sure we could find some interesting correlation from the 
+              internet to post here)
+        * At the end of the data, the only purpose of machine learning is to
+          provide information to people or software so they can make better
+          decisions
+
+4. Something else you think would be better to talk about in an introductory
+   video.
+
+Conclusion
+-------------------------------------------------------------------------------
+Machine Learning is pretty cool.