A Practical Introduction to Machine Learning
Bypass the academic theories. Learn how to integrate two basic machinelearning algorithms in your daily work, using programming best practices.
With the increasing popularity of Alexa, Xbox Kinect, Cortana, and Siri, machine learning and AI are fast becoming required components of the software developer’s toolkit. However, machine learning isn’t a silver bullet. It requires domain knowledge and intuition to solve problems.
Join Matthew Kirk for an introduction to machinelearning concepts. Instead of spending time focusing on the academic foundation of machine learning, you’ll delve into the kNearest Neighbors algorithm (kNN) and naive Bayes classifiers to learn how to apply the machinelearning thought process to any programmingcentric career. You’ll leave prepared to approach supervised learning problems with programming best practices and ready to implement these two algorithms in your daily work.
What you'll learnand how you can apply it
By the end of this live, online course, you’ll understand:
 Induction versus deduction and how this applies to data
 How to use the kNearest Neighbors algorithm to classify housing data
 How to utilize naive Bayes classifiers for simple yes or no answers
And you’ll be able to:
 Write crossvalidation tests for supervised learning algorithms
 Code a simple classifier using kNearest Neighbors
 Code a simple classifier using naive Bayes
This training course is for you because...
 You are a midlevel software developer who wants to become adept in machine learning.
 You are a data analyst with an academic background who wants to automate some of your tasks.
 You are a technical executive who wants to guide your organization to implementing more machinelearning projects.
Prerequisites
 Basic knowledge of coding principles, such as for loops, if conditions, and data structures
Materials and downloads needed:
 A machine with Python 3 installed
Recommended Preparation:
About your instructor

Matt Kirk is a data architect, software engineer, and entrepreneur based out of Seattle, WA.
For years, he struggled to piece together his quantitative finance background with his passion for building software.
Then he discovered his affinity for solving problems with data.
Now, he helps multimillion dollar companies with their data projects. From diamond recommendation engines to marketing automation tools, he loves educating engineering teams about methods to start their big data projects.
To learn more about how you can get started with your big data project (beyond taking this class), check out matthewkirk.com for tips.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Introduction (5 minutes)
Inductive reasoning: a supervised learning approach to machine learning (1 hour)
 Presentation (20 minutes)
 What is machine learning?
 Inductive versus deductive reasoning
 Supervised learning and other learning classes
 Testing supervised learning methods
 Coding principles and how they relate to machine learning (i.e., SOLID)
 Testdriven development
Quiz (5 minutes)
 What is the difference between induction and deduction?
 What is domain knowledge?
 What is the most common way to test supervised learning?
Discussion (20 minutes)
 The highinterest credit card debt of machine learning
 Why machine learning isn’t a silver bullet
Break (10 minutes)
Distancebased methods: kNearest Neighbors (1 hour)
 Presentation (20 minutes)
 How to calculate a house value (classification versus regression)
 Calculating a value based on relevancy or closeness
 What is distance? (triangle inequality)
 The kNearest Neighbors algorithm in a nutshell
 Tradeoff: Curse of dimensionality
 Quiz (5 minutes)
 What is an xample of a distance metric?
 What is the curse of dimensionality and how does it relate to distance?
 Why would you use Euclidean distance versus Manhattan distance?
 Demo (10 minutes)
 The problem of housing data using kNN
 Lab (20 minutes)
 Implementing a kNN classifier of housing data, using regression, with a downloadable examplee
Break (10 minutes)
Probabilistic methods: naive Bayes classifier (1 hour)
 Presentation (20 minutes)
 Likelihood estimate of spammy emails, based on keywords
 How to exploit posterior distributions
 Bayes’ theorem and inverse conditional probability
 How to test ROC curves
 Confusion matrices
 Quiz (5 minutes)
 What is the probability of X given A?
 What happens if posterior distributions don’t tell us much?
 Why is the naive Bayes classifier called “naive”?
 Demo (10 minutes)
 Introducing some data points, guiding principles, and what to expect
 Lab (20 minutes)
 Implementing a naive Bayes classifier using scikit learn
Conclusion and wrapup (10 minutes)