Machine Learning, Data Science and Deep Learning with Python

A refresher on mean, median, and mode - and when it's appropriate to use each.

We'll use mean, median, and mode in some real Python code, and set you loose to write some code of your own.

Introducing the concepts of probability density functions (PDF's) and probability mass functions (PMF's).

We'll show examples of continuous, normal, exponential, binomial, and poisson distributions using iPython.

We'll look at some examples of percentiles and quartiles in data distributions, and then move on to the concept of the first four moments of data sets.

An overview of different tricks in matplotlib for creating graphs of your data, using different graph types and styles.

The concepts of covariance and correlation used to look for relationships between different sets of attributes, and some examples in Python.

We cover the concepts and equations behind conditional probability, and use it to try and find a relationship between age and purchases in some fabricated data using Python.

Here we'll go over my solution to the exercise I challenged you with in the previous lecture - changing our fabricated data to have no real correlation between ages and purchases, and seeing if you can detect that using conditional probability.

Multivariate models let us predict some value given more than one attribute. We cover the concept, then use it to build a model in Python to predict car prices based on their number of doors, mileage, and number of cylinders. We'll also get our first look at the statsmodels library in Python.

We'll just cover the concept of multi-level modeling, as it is a very advanced topic. But you'll get the ideas and challenges behind it.

The concepts of supervised and unsupervised machine learning, and how to evaluate the ability of a machine learning model to predict new values using the train/test technique.

We'll apply train test to a real example using Python.

We'll introduce the concept of Naive Bayes and how we might apply it to the problem of building a spam classifier.

K-Means is a way to identify things that are similar to each other. It's a case of unsupervised learning, which could result in clusters you never expected!

We'll apply K-Means clustering to find interesting groupings of people based on their age and income.

Entropy is a measure of the disorder in a data set - we'll learn what that means, and how to compute it mathematically.

We'll create a decision tree and an entire "random forest" to predict hiring decisions for job candidates.

Random Forests was an example of ensemble learning; we'll cover over techniques for combining the results of many models to create a better result than any one could produce on its own.

XGBoost is perhaps the most powerful machine learning algorithm today, and it's really easy to use. We'll cover how it works, how to tune it, and run an example on the Iris data set showing how powerful XGBoost is.

Support Vector Machines are an advanced technique for classifying data that has multiple features. It treats those features as dimensions, and partitions this higher-dimensional space using "support vectors."

We'll use scikit-learn to easily classify people using a C-Support Vector Classifier.

The shortcomings of user-based collaborative filtering can be solved by flipping it on its head, and instead looking at relationships between items instead of relationships between people.

We'll use the real-world MovieLens data set of movie ratings to take a first crack at finding movies that are similar to each other, which is the first step in item-based collaborative filtering.

Our initial results for movies similar to Star Wars weren't very good. Let's figure out why, and fix it.

As a student exercise, try some of my ideas - or some ideas of your own - to make the results of our item-based collaborative filter even better.

KNN is a very simple supervised machine learning technique; we'll quickly cover the concept here.

We'll use the simple KNN technique and apply it to a more complicated problem: finding the most similar movies to a given movie just given its genre and rating information, and then using those "nearest neighbors" to predict the movie's rating.

Data that includes many features or many different vectors can be thought of as having many dimensions. Often it's useful to reduce those dimensions down to something more easily visualized, for compression, or to just distill the most important information from a data set (that is, information that contributes the most to the data's variance.) Principal Component Analysis and Singular Value Decomposition do that.

We'll use sckikit-learn's built-in PCA system to reduce the 4-dimensions Iris data set down to 2 dimensions, while still preserving most of its variance.

Cloud-based data storage and analysis systems like Hadoop, Hive, Spark, and MapReduce are turning the field of data warehousing on its head. Instead of extracting, transforming, and then loading data into a data warehouse, the transformation step is now more efficiently done using a cluster after it's already been loaded. With computing and storage resources so cheap, this new approach now makes sense.

What's a confusion matrix, and how do I read it?

Bias and Variance both contribute to overall error; understand these components of error and how they relate to each other.

We'll introduce the concept of K-Fold Cross-Validation to make train/test even more robust, and apply it to a real model.

In this example, we'll try to find the top-viewed web pages on a web site - and see how much data pollution makes that into a very difficult task!

A brief reminder: some models require input data to be normalized, or within the same range, of each other. Always read the documentation on the techniques you are using.

A review of how outliers can affect your results, and how to identify and deal with them in a principled manner.

We'll present an overview of the steps needed to install Apache Spark on your desktop in standalone mode, and get started by getting a Java Development Kit installed on your system.

We'll install Spark itself, along with all the associated environment variables and ancillary files and settings needed for it to function properly.

A high-level overview of Apache Spark, what it is, and how it works.

We'll go in more depth on the core of Spark - the RDD object, and what you can do with it.

A quick overview of MLLib's capabilities, and the new data types it introduces to Spark.

We'll take the same example of clustering people by age and income from our earlier K-Means lecture - but solve it in Spark!

Let's use TF-IDF, Spark, and MLLib to create a rudimentary search engine for real Wikipedia pages!

Spark 2.0 introduced a new API for MLLib based on DataFrame objects; we'll look at an example of using this to create and use a linear regression model.

High-level thoughts on various ways to deploy your trained models to production systems including apps and websites.

Running controlled experiments on your website usually involves a technique called the A/B test. We'll learn how they work.

How to determine significance of an A/B tests results, and measure the probability of the results being just from random chance, using T-Tests, the T-statistic, and the P-value.

We'll fabricate A/B test data from several scenarios, and measure the T-statistic and P-Value for each using Python.

Some A/B tests just don't affect customer behavior one way or another. How do you know how long to let an experiment run for before giving up?

If you skipped ahead, I'll show you where to get the course materials for just this section. And we'll cover some pre-requisite concepts for understanding how neural networks operate: gradient descent, autodiff, and softmax.

Google's Tensorflow Playground lets you experiment with deep neural networks and understand them - without writing a line of code!

Let's dive into the details on how modern multi-level perceptrons are trained and tuned.

We'll cover Google's open-source Tensorflow Python library, and see how it can help you create and train neural networks.

We'll use Tensorflow to create a neural network that classifies handwritten numerals from the MNIST data set. Part 1 of 2.

We'll use Tensorflow to create a neural network that classifies handwritten numerals from the MNIST data set. Part 2 of 2.

The Tensorflow 1.9 offers a higher-level API called Keras, and makes it easier to construct your neural networks. We'll use Keras to solve the same handwriting recognition problem - but with much less code.

As another hands-on example, we'll use Keras to build a neural network that learns how to determine if a politician is Republican on Democrat just based on their votes.

CNN's mimic your visual cortex, and can find features in one, two, or three-dimensional data even if you're not sure where exactly that feature is.

CNN's are better suited to image data, and we'll prove that by using a CNN in Keras on the MNIST data.

RNN's can handle sequences of data, like events over time or words in a sentence. Learn what's different about how they work, how they are trained, and ways to optimize them.

Let's implement a RNN in Keras to determine positive or negative sentiments for real movie reviews from IMDb!

We'll see how transfer learning makes it trivially easy to use pre-trained models for common AI tasks.

Some suggested resources for continuing your education on deep learning, artificial intelligence, and neural networks.