Machine Learning, Data Science and Generative AI with Python

In a crash course on Python and what's different about it, we'll cover the importance of whitespace in Python scripts, and how to import Python modules.

In part 2 of our Python crash course, we'll cover Python data structures including lists, tuples, and dictionaries.

In this lesson, we'll see how functions work in Python.

We'll wrap up our Python crash course covering Boolean expressions and looping constructs.

Pandas is a library we'll use throughout the course for loading, examining, and manipulating data. Let's see how it works with some examples, and you'll have an exercise at the end too.

We cover the differences between continuous and discrete numerical data, categorical data, and ordinal data.

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.

We'll cover how to compute the variation and standard deviation of a data distribution, and how to do it using some examples in Python.

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.

An overview of Bayes' Theorem, and an example of using it to uncover misleading statistics surrounding the accuracy of drug testing.

We introduce the concept of linear regression and how it works, and use it to fit a line to some sample data using Python.

We cover the concepts of polynomial regression, and use it to fit a more complex page speed - purchase relationship in Python.

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.

We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!

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.

Decision trees can automatically create a flow chart for making some decision, based on machine learning! Let's learn how they work.

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.

One way to recommend items is to look for other people similar to you based on their behavior, and recommend stuff they liked that you haven't seen yet.

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.

We'll implement a complete item-based collaborative filtering system that uses real-world movie ratings data to recommend movies to any user.

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.

We'll describe the concept of reinforcement learning - including Markov Decision Processes, Q-Learning, and Dynamic Programming - all using a simple example of developing an intelligent Pac-Man.

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.

Cleaning your raw input data is often the most important, and time-consuming, part of your job as a data scientist!

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.

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 walk through an example of coding up and running a decision tree using Apache Spark's MLLib! In this exercise, we try to predict if a job candidate will be hired based on their work and educational history, using a decision tree that can be distributed across an entire cluster with 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!

We'll introduce the concept of TF-IDF (Term Frequency / Inverse Document Frequency) and how it applies to search problems, in preparation for using it with MLLib.

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

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?

There are many limitations associated with running short-term A/B tests - novelty effects, seasonal effects, and more can lead you to the wrong decisions. We'll discuss the forces that may result in misleading A/B test results so you can watch out for them.

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.

We'll cover the evolution of artificial neural networks from 1943 to modern-day architectures, which is a great way to understand how they work.

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