## Univariate Linear Regression Concepts

Howdy, machine learning compatriots! Welcome back to our foray into getting started with machine learning. Previously, we covered some core machine learning concepts, namely supervised machine learning algorithms and unsupervised / deep learning. (For the full series to date, here’s our Machine Learning for Beginners page.)

Today we’re learning the concepts behind supervised machine learning algorithms. Specifically, we examine univariate (one variable) linear regression. Univariate linear regression is the beginner’s playpen in supervised machine learning problems. We endeavor to understand the “footwork” behind the flashy name, without going too far into the linear algebra weeds.

## Quick Recap: Supervised Machine Learning Problems

If you’re just dropping into the series, we’ll quickly set today’s stage. Univariate linear regression falls under the category of regression algorithms, withing supervised learning machine learning problems.

• Supervised learning: we provide the algorithm with pre-cleaned, pre-labeled data. The algorithm learns off the data we provide to classify or predict new data.
• Regression: making a line of best fit.

When we first covered supervised machine learning concepts, regression was shown to make a line of best fit from existing data, so we could predict new data points. Below, we first used the example of an SEO team predicting how many unique linking domains a page would need to achieve a certain rank. (A supervised learning problem, using a regression algorithm for future predictions.)

Important note: our graphic above is similar to linear, but is not quite, linear regression. Details, details. At any rate, this should take us in nicely to examining the inner workings of univariate linear regression.

## A High Level Look at the Regression Problem Process

If I’m being brutally honest, the process of translating machine learning education to public-facing blog posts has been my toughest effort to date. In other words, I try to make my posts easy to follow, like dummy notes I’m taking as I learn.

That being said, 2 weeks into a machine learning course, and the content has already gone off the wheels, deep into linear algebra and so forth. So, instead of going into the weeds for publication, I’m trying to keep it snackable (buzzword bingo, drink!) and down to earth.

Let’s settle in slowly on the regression problem process. Also illustrated below, we need a few key steps:

1. A cleaned training data set with correct labels
2. A program (such as Matlab or Octave) with functionality and access to an appropriate univariate linear regression algorithm
3. A hypothesis and prediction of new values

Let’s peel back a layer and go slightly deeper. Since cleaning and correctly labeling training data is largely dependent on you & your domain, we’re skipping that step. ¯_(ツ)_/¯

Instead, let’s look closer at the algorithm & hypothesis portions! Our first stop is something called the cost function.

## The Cost Function in Linear Regression Learning Problems: Squared Error

Before we jump into cost function, let’s turn over a new leaf in visual examples. Instead of our SEO example, let’s look at a problem that could be more linear-friendly. Below, let’s assume we have some data on a customer’s lifetime value plotted against the number of marketing touchpoints they’ve interacted with.

Okay, with the housekeeping complete, let’s remember our goal for linear regression: find the line of best fit.

Let’s also tie this back to the real world. Perhaps we’re a marketing director or VP of marketing needing to convey the ideal number of marketing touchpoints to the CMO and CEO. Doing so could help guide budgeting, channel mix, and planning questions.

How do we find a line of best fit? Through linear algebra and programming, we can objectively determine the best fit by testing hypotheses and measure each hypothesis line against the actual data points for closeness of fit.

Being frank, the material up to this point is pretty humdrum. However, when we start making hypotheses such as the above, things get interesting. The program “makes a guess” as to the line of best fit, perhaps like the illustration above. I’m no “eggspert”, but that doesn’t look like a great line of fit.

But have no fear dear reader, math/science comes to the rescue. The next portion of the algorithm calculates the distance (cost function / squared error) from the training data to the predicted line of best fit in a process called squared error function. When you plot the hypothesis against the squared error sum, you may get a distribution something like the below.

Bare with me. Let’s say we plotted:

1. Our illustrated hypothesis (teal plus sign)
2. Other attempted hypotheses (tan x’s), and,
3. The best fit hypothesis (green outlined star)

This renders a convex parabolic distribution. To get the line of best fit, we want to get as low on the X axis as possible. (Known as the global minimum.) The further magic in machine learning is how we move from a lame hypothesis (teal plus sign) to solution (green outlined star). Now, meet a technique called gradient descent. Sidebar: if we’re being more mathematical and technical about it, this really plots to a 3D conic distribution, but the above explanation should suffice for now if we’re not getting bogged down in the math.

## Parameter Learning & Gradient Descent

Gradient descent is the iterative mathematical process of working our way down the squared error plot from a lousy hypothesis to a line of best fit. Again, we’re not delving into calculations and derivatives – there’s a TON of math that goes behind this material.

Gradient descent systematically tests increments of hypotheses against a specified learning rate. The learning rate is essentially the magnitude or speed with which you which try to move along the convex function down to zero.

## Wrap Up

Did I mention this is one of the toughest posts for me to date? The other contender is my DIY Alexa Raspberry Pi article. It’s now 3:20 a.m. on a Saturday night/Sunday morning as I type this conclusion. (Insert horror emoji.)

So, if we were to break down all of the above into a short bulleted list:

1. Univariate linear regression takes sample data to make a line of best fit
2. “Best fit” is objectively measured by a squared error function, or the summed distances of the hypothesis line from the actual data points
3. The hypothesis and squared error function plot roughly a convex parabolic graph
4. Gradient descent is an algorithm that systematically reduces the squared error hypothesis, guided in part by the learning rate
5. The gradient descent iteratively seeks the global minimum on the convex function, AKA the line of best fit
6. The line of best fit is determined, (and Teh Lurd of Teh Rings finishes on your second monitor to Herb Alpert’s Spanish Flea.)

Next up, we’ll be installing some machine learning software (Matlab & Octave) and diving into multivariate regression. Look after each other.

## GROUP BY SQL Statement

Introducing the GROUP BY SQL Statement in PostgreSQL

‘Ello SQL geeks! Welcome back to our SQL learning journey. We left off with a beginner SQL skills challenge and the aggregate SQL functions: MIN, MAX, AVG and SUM. Today we’re looking at the GROUP BY statement. We’ll learn about this function in PostgreSQL and walk through usage of this handy SQL statement.

## About the GROUP BY SQL Statement/Clause

From my simple understanding, GROUP BY functions like a hybrid of the following:

• SELECT DISTINCT keyword (If used without an aggregate function like SUM), and,
• An Excel pivot table, rolling up aggregate figures (Count, Sum, Average, etc.) into unique rows

If you’re familiar with Excel Pivot tables, then you’ll recognize here the power of this function.

Let’s take a look at some examples to clarify.

## First Look at Using the GROUP BY Function

To better illustrate the power of GROUP BY, we’ll first show its usage without aggregate functions. Consider the following:

If we query the address table of our sample database with a generic SELECT * FROM address; we get back an atrocious 605 rows of data. Aggregate and useless!

In contrast, if we call the GROUP BY function, we’ll get back a cleaner output, with fewer rows – only unique values returned. While this is an incremental improvement to analyzing data, there’s much left to be desired.

What’s missing here? How about that pivot table-esque functionality? This is where the power of using GROUP BY with aggregate functions gets awesome.

## Using the GROUP BY SQL Statement with Aggregate Functions

As with most analysis, a single data point or data series rarely holds significant insight value on its own. Let’s drive home that point by leveraging the GROUP BY statement with the SUM aggregate function. Below, we compare the ratings of films in our sample database in aggregate by replacement cost. Perhaps this could serve in-store strategies for loss prevention.

SELECT rating, SUM(replacement_cost)
FROM film
GROUP BY rating;

If we extend this functionality to more real world examples, we could use the following for GROUP BY:

• Grouping page-level / URL data to roll up clickstream analytics data
• Large scale analysis of CRM data for customer segmentation analysis
• Analyzing returns for financial data

The list could (and I’m sure it does) go on.

Let’s take this one step further and reduce potential future workload, by building sort functionality into our query. Below, we add a line to get most expensive ratings to least.

SELECT rating, SUM(replacement_cost)
FROM film
GROUP BY rating
ORDER BY SUM(replacement_cost) DESC;

By the looks of this, no need to guard Land Before Time 8. 🙂

Extending our lesson: you can use the COUNT, AVG, and other aggregate functions to analyze as desired.

## Wrap Up

Alright, this was a relatively gentle introduction into more advanced SQL functions. GROUP BY is a rather critical function, so in our next article, we’ll be doing yet another skills challenge. Joy!

Feel free to catch up on our other articles that help you learn PostgreSQL. Also, check out some how-to’s on developing Amazon Alexa skills, and a new series on getting started with Machine Learning. As always, please share with your colleagues and share thoughts in the comments below. Cheers.

## SQL Aggregate Functions: Min, Max, Avg and Sum

Welcome back, SQL nerds! We’re back in action on the journey of learning SQL, after a beginner PostgreSQL skills challenge.

We’re reaching the end of basic functions and queries with this article. Based on what we’ve learned so far, we can do basic counting, filtering, sorting and pattern matching against PostgreSQL databases.

If you’re just tuning in, here’s the page on how to learn SQL, and the previous SQL article on the LIKE Statement.

Okay, enough jabber. Let’s jump in. The aggregate functions of MIN, MAX, AVG and SUM are our turning point into more complex SQL queries that involve concepts such as GROUP BY, among others.

At the same time, the functions on their own aren’t super complicated. Because we’re ass-u-ming you’re familiar with the general concepts of minimum, maximum, etc., we’re going to forgo conceptual and syntax explanations for demos.

## AVG Aggregate Function

As we level up in SQL, we’re going to do less and less explaining / handholding / screenshots. That said, we’re going to explore our DVD rental data set for a table with a nice numerical component that would make good use of the functions.

We did a SELECT * FROM film LIMIT 15; to get a peek at the columns. For the purposes of this exercise, the replacement_cost column will do nicely.

To get the AVG:

SELECT AVG(replacement_cost) FROM film;

## Using ROUND for Decimal Place Control

You’ll notice in the example above that we got 3 decimal places on what’s supposed to be a dollar amount. How do we fix that? Glad you asked. Meet the ROUND function.

We pass in the target value (average of replacement_cost) and mandate the number of decimal places we’d like returned. Below:

SELECT ROUND( AVG(replacement_cost), 2) FROM film;

## MIN Aggregate Function

To find the minimum value in a given column:

SELECT MIN(replacement_cost) FROM film;

## MAX Aggregate Function

To find the maximum value in a given column:

SELECT MAX(replacement_cost) FROM film;

## SUM Aggregate Function

To find the maximum value in a given column:

SELECT SUM(replacement_cost) FROM film;

## Wrap-Up

Alright, that was a relatively quick article! Hopefully this was a reprieve from more involved sections in the past. We should find that as we continue to strengthen our core PostgreSQL  capabilities, these articles and our SQL queries should be easier and easier. If you found this article interesting, you might enjoy a new section on how to get started in machine learning. Cheers.

## Beginner SQL Skills Challenge!

Howdy, SQL geeks. Hope this post finds you swell!

Over the past few months, we’ve gained a ton of ground in learning SQL, or at least I have. 🙂

Let’s take a moment to:

1. Test our knowledge of SQL skills learned thus far
2. Start seeing SQL queries less as statements of code, and more as real world business challenges

• Recap of the training database we’ve been working with
• General statements of each business problem
• Hints and thoughts about how to approach each problem
• Solutions to each problem

## Recap: Our Training Database

Our training database is a best/old faithful. We’ve been using the surprisingly popular DVD rental database in a .tar file for our practice database.

Contained within this databases are various tables with fictitious information, including: customer information, film production information, business/pricing information and so forth.

In our challenges, we’ll execute various SQL queries to extract pieces of insight for business tasks. It’s assumed in this article that you’ve installed PostgreSQL via pgAdmin and have followed this article series so far, using the DVD rental training database.

## The SQL Challenges

Alright, here we go:

1. How rentals were returned after July 17, 2005?
2. How many actors have a last name that starts with the letter A?
3. How many unique districts are represented in the customer database?
4. Can you return the actual list of districts from challenge #3?
5.  How many films have a rating of R and a replacement cost between \$5 and \$15?
6. How many films have the word Truman somewhere in the title?

## How to Approach the Challenges

Right, then. In this section, we’ll add some color commentary (read: hints) to our challenges. This should help you understand the mechanics of the solution, while ensuring you can’t see the answers all in one screen. 😉

### Challenge 1: Rentals Returned After July 17, 2005

As with all challenges, a problem well stated is half (or more) solved. So let’s look at the high levels of the ask, and work our way down. We need information on rentals, so this means we’ll probably be querying the rentals table.

We would want to first examine the rentals table in a concise manner by doing:

SELECT * FROM rental LIMIT 5;

Once you’ve surveyed the table, we really only need one column returned (pun not intended) and we only want the sum figure of returns, where (HINT!!) the return date was after (think a logical operator here) July 17, 2005.

### Challenge 2: Actors That Have a Last Name Beginning with “A”

Like our first challenge, let’s work from the “top down”. We need actor information, so querying the actor table would be a great place to start. Similar to last time, we need a count of values matching a condition. The difference versus challenge #1  is we need to find match a pattern like or such as an actor’s last name that begins with the letter A.

### Challenge 3: Number of Unique Districts in the Customer Database

The title and description could cause some confusion here. You may need to do some basic SELECT * FROM table_name LIMIT X; queries to make sure you’ve got the right table. Once you do, we’re looking for an amount of distinct values in the database. Order of operations matters.

### Challenge 4: Returning the Actual Lists of Districts from Challenge #3

Not much to hint at here – getting challenge #3 is the key here. You’ll really only be simplifying the correct query in challenge #3 to get the correct answer here.

### Challenge 5: Cheap, (Mildly) Naughty Films

This one might be the longest query yet in this challenge. So we’re looking up film information, thus should know which table to query. We’re returning a value where a certain rating must be returned, and (HINT) we need to layer in one more lens of qualification. That lens dictates we specify a range between (cough, hint!) two values.

### Challenge 6: Where in the World are Films Containing “Truman”?

This challenge is more of a recency test than retention of older concepts. You’ll need to employ pattern matching again for this business challenge/query to find titles that have some match like Truman in the title.

## Challenge Solutions

Is that your final answer? Below are the queries, with screenshots of what I did.

### Solution 1: Rentals Returned After July 17, 2005

SELECT COUNT(return_date) FROM rental
WHERE return_date > ‘2005-07-17’;

### Solution 2: Actors That Have a Last Name Beginning with “A”

SELECT COUNT(*) FROM actor
WHERE last_name LIKE ‘A%’;

### Solution 5: Cheap, (Mildly) Naughty Films

SELECT COUNT(*) FROM film
WHERE rating = ‘R’
AND replacement_cost BETWEEN 5 AND 15;

### Solution 6: Where in the World are Films Containing “Truman”?

SELECT COUNT(*) FROM film
WHERE title LIKE ‘%Truman%’;

## Wrap Up

Well done for completing these challenges! You shall indeed pass. 🙂

Soon, we’ll be covering aggregate SQL functions, such as MIN, MAX, AVG and SUM.

If you’re just joining us, here’s a running list of our articles so to date (4/16/2017):

## Unsupervised Learning Introduction: Machine Learning Essentials

Howdy, machine learning students! Today we’re going to introduce the concept of unsupervised machine learning algorithms.

## Quick Recap: Supervised Learning

Before we jump in, let’s quickly recap our last article introducing supervised machine learning algorithms. This will give us the appropriate context for unsupervised learning.

In supervised machine learning problems, we supply pre-labeled data to the algorithm. By supplying data that’s already correctly labeled, we ask the algorithm to further predict (regression) or label (classification) new data.

## Unsupervised Machine Learning = Unlabeled Data

The most immediate and prominent difference  for unsupervised learning is the data. Above, we gave the algorithm “a boost” by supplying the intended “right” answers in the data. Below, in an unsupervised machine learning problem, there are no right answers…yet.

We’ve supplied the algorithm with data in the problem, but it’s provided without labels or “answers”. We are mandating that the algorithm discover structure and infer patterns/labels on its own. We could also compare the above example to a clustering problem.

So in unsupervised learning, we supply a large amount of unlabeled data, without explicitly identified form or structure. We ask the algorithm to come up with ideas of structure and segmentation on its own.

Some additional applications of unsupervised learning could include:

• Market segmentation of massive transaction data
• Large scale social networking data
• Astronomical data analysis
• Large scale market data
• Mass audio/voice analysis
• Large scale gene clustering

## Wrap Up

That was a bit of a quick one! The challenge with some these technical subject matter areas is sometimes we have limited room to run before going off into the technical weeds. This is one of those areas. Next, we’ll be covering some key concepts in the areas of machine learning model representation, cost function and parameter learning. Don’t worry too much about those yet, we’ll take it step by step. 🙂
As always, feel free to follow my other journeys of learning PostgreSQL, learning how to develop Amazon Alexa Skills, learning how to get started in algorithmic trading, JavaScript for beginners…and more to come soon! Cheers.

## Supervised Learning & Its Types: Machine Learning Essentials

Welcome back, machine learning geeks! Let’s delve deeper into our journey of mastering machine learning. In the previous article, we looked at both informal and technical definitions of machine learning.

We also looked at the two major types of machine learning algorithms, A) supervised machine learning algorithms, and B) unsupervised machine learning algorithms. We also mentioned reinforcement learning and recommender systems, but won’t spend as much time there.

Let’s jump in!

## Introduction to Supervised Learning

Supervised machine learning algorithms are used when you:

1. Have a set of known, correctly labeled data
2. Are looking to predict a continuous value output

Let’s visualize by looking at a digital marketing example.

Perhaps we are digital marketers looking to forecast or predict how much time and effort we’ll need to spend on outreach and content promotion for a particular webpage and target ranking.

Say we’ve gathered some data about website pages with:

• Their rank for a given keyword
• The amount of unique linking domains pointing to each page

Such a distribution of data might look like the below. It demonstrates a trend, but right now, we don’t have a single linear function that will “connect all the dots”.

This is a great example for the first major subdivision of supervised machine learning algorithms:

## Regression Learning Problem

Off the cuff, there are a couple of different ways in which we might try to solve this problem. Both solutions involve using the “labeled” data to predict a line of best fit, which, on the whole, minimizes the distance between the line and all the points. If we have a simple slope, predictions could be precarious at best, and misrepresentative on the other end of the spectrum.

We could also instruct our programs to fit a quadratic equation to the data (read: not a straight line.) In our slightly altered example here, the difference could be significant.

At this point in time, we won’t focus on whether we should pick a linear or quadratic line for the regression output. However, it is worth noting that the two different methods could yield widely varying results.

Say we wanted to get a webpage ranking in position 5 for this given study, a linear example would have us preparing to secure links from ~180 unique domains. If we decided on the quadratic solution, we could be looking at significantly less effort, perhaps ~125 unique linking domains?

## Classification Learning Problem

Insert smooth segue here and please forgive my lazy writing at this time. 🙂

The next major subdivision of supervised machine learning algorithms is known as a classification problem. Let’s use another example.

We are analyzing a large user study of an Amazon Alexa Skill in development. Perhaps we are classifying a particular interaction with the skill by success or failure (1 or 0), and plotted against the measured spoken word count for the given interaction.

Visualized, this data might look like the below.

In this example with (shockingly 🙂 ) clean data, we might want to guide development efforts in providing the best sample phrases/interactions for the skill. Perhaps, we would want to measure the probability an interaction four (4) spoken words long will be successful. This is known as a classification learning problem.

Above, we examined only one factor in determining a probability. However, we aren’t limited to examining just one parameter.

Let’s consider the following, perhaps we are an e-commerce retailer or digital business. A frustration for many marketers is the “one and done” (self explanatory) customer that represents minimal customer lifetime value for the brand.

It would certainly behoove us to identify these customers and provide them with specialized messaging or a compelling promotion offer to keep them engaged and transacting with the brand.

Below, we could have a sample data set to which we fit a line, and thereby predict based on a certain age and AOV (average order value) profile whether a particular transaction is likely or not to be a “one and done” consumer.

In practice, we could potentially use a number of inputs to help solve machine learning problems. There are even methods to use an “unlimited” number of inputs- support vector machines. But only a tease for now!

## Wrap-Up

Our first major classification of machine learning algorithms is supervised learning! In supervised learning, we assist the program by supplying the correct answers in part, and then mandating the program supply correct values via regression or classification, the two major categories of supervised machine learning problems.

Moving forward, we’ll dive deeper into one variable linear regression (dare we say the hello world of machine learning?) as well as fleshing other key concepts and methods. If you’re interested in this, you might also be interested in learning PostgreSQL, how to develop Alexa skills, or algorithmic trading. Take care.

## What is Machine Learning?

Hello there, fellow Machine Learning (ML) students! Welcome back to our crash course in starting machine learning from an absolute beginner’s perspective.

In our previous article, we covered an introduction to Machine Learning, answering several key questions:

• Where is machine learning used in our lives?
• Where did machine learning come from?
• Where is machine learning headed?

Forging ahead in our learning journey, we’ll introduce some definitions of machine learning and look at the major types of machine learning applications.

## Machine Learning (ML), A Casual Definition by Arthur Samuel

Our first definition, teased in the last article, follows:

Machine learning is the practice of giving computers the ability to learn without being explicitly programmed to do so.

## More on Arthur Samuel & Why His Definition on ML Matters

If you’re like me, you might not have heard of Arthur Samuel. Who is he, and why does his opinion matter in the fields of artificial intelligence and machine learning?

Arthur Samuel was a pioneer in artificial intelligence and computer gaming fields. In 1959, he coined the term “machine learning” as a founding father in the field. That’s why he’s important! Let’s also look at a more formal / scientific definition.

## A More Formal Machine Learning Definition

Tom Mitchell, of Carnegie Mellon, offers a definition with more structure.

• A well defined learning problem follows
• E * T = P
• Note: His definition does not include mathematical operators. I’m taking a large liberty to insert them myself. ¯\_(ツ)_/¯
• Experience (E) placed against Task (T) is measured by Performance (P)

Here’s a further example:

Example: playing Go.

E = the experience of playing many games of Go.

T = the task of playing and winning Go.

P = the probability that the program will win the next game.

## Major Categories of Machine Learning Algorithms

If you judge by press coverage of ML as I have, it appears to be a nebulous field. (In all fairness, it may still be.) However, there is structure we can take in learning ML. There are two types of machine learning algorithms:

• Supervised learning algorithms
• Unsupervised learning algorithms

There are a couple of other prominent types of machine learning algorithms as well: reinforcement learning and recommender systems.

## Wrap-Up

Congratulations, we’ve cleared a very gentle introduction to machine learning, and it’s novice/high level definitions. I look forward to learning more with you, dear reader! Our next articles will cover a bit more detail about the two major ML algorithm types: supervised learning, and unsupervised learning. Until then, look after each other.

## An Introduction to Machine Learning

Howdy! This is an abrupt interruption to our regularly scheduled programming of SQL lessons, Amazon Alexa Skill development and Algorithmic trading. For those readers who don’t personally know me, I’m on a quest/streak to level up as a technologist.

Getting around to the point, I’ve been taking self-paced courses in varying forms to learn, apply and share new skills. However, I’ve heard the Coursera Stanford class in Machine Learning taught by Andrew Ng recommended so widely, I’m just going to doggedly sprint a marathon. I’m starting almost a week behind, working a busy job, trying to have a social life, and many other things…but darnit, I’m going to give this class my best shot. Hopefully I finish. 🙂

Okay, over-sharing complete. Let’s jump into a brief introduction of Machine Learning.

Machine learning, according to Andrew Ng (Chief Scientist at Baidu), is the science of getting computers to learn without being explicitly programmed.

## Where is machine learning used in our lives?

Machine learning is employed a large number of actors. Here are a few examples:

• Search engines, such as Bing, use machine learning to process MASSIVE amounts of data to quickly rank web pages in order of relevance, with limited human intervention.
• Social networks, such as Facebook, use machine learning to recognize your friend’s faces for auto-tagging capabilities.
• Email providers such as Apple Mail may employ spam filters that continuously learn to protect your inbox, your computer, and most importantly, your sanity.
• Tech companies such as Amazon use natural language processing (NLP) to create conversational experiences and transactions with skills and services.
• Entertainment companies such as Netflix use self-learning algorithms to recommend compelling new films and TV shows for those of you who binge watch

## Where did machine learning come from?

Machine learning originated from a computer science field known as artificial intelligence. Long seen as a pipe dream from Star Trek (crass and careless reference, I know), machine learning is a practical and attainable segue to artificial intelligence, or machines and programs that contain some degree of self awareness.

This capability is a relatively new, yet a rapidly exploding field that grows as mathematical, statistical, hardware and software capabilities continue to compound and improve.

What follows is a better question still.

## Where is machine learning headed?

Machine Learning in the future could look like a few different things (but not limited to this list, obviously!):

• Predictive and preventative applications in engineering, medicine, and security
• “Load bearing” performance in complex tasks, such as architecting, coding and programming self-driving cars
• Coordinate machines and programs that study our behavior at our request and perform tasks, such as performing spring cleaning
• Assistants or programs that are intelligent – able to optimize and independently solve problems on our behalf

## Wrap-Up on the Machine Learning Introduction

That wasn’t so bad was it? We’ll follow soon with a more formal definition of Machine Learning and its various tranches of study. Cheers all.

## The LIKE Statement: SQL Statement Fundamentals

Howdy folks! We are overdue for another installation of SQL learning. I’ve slept a few times since the past couple of articles on how to learn SQL. Previously, we talked about the IN Statement, BETWEEN statement, and ORDER BY clause.

In this article, we’ll learn how to execute the LIKE statement in SQL queries. Let’s jump in.

## About the LIKE Statement & Why it’s Important

Have you ever worked with a data set that’s overwhelmingly large  or complex? Or overwhelmingly large and complex? Sometimes, you need to find data, but can’t recall the exact string or values for a lookup. Or, perhaps, you’re working with a messy data output from say, Google Keyword Planner that groups a range of close variants into one value?

Say you’re looking for values related to designer clothing and designer clothes. Without a better solution, the most probable solution for you is to do a bunch of sorting, filtering, classifying and other data sleuthing at great expense to time and sanity.

The LIKE statement exists to help with the debacle of only having / knowing  part of the lookup criteria you need, courtesy of pattern matching.

PostgreSQL and many other SQL engines/platforms support the LIKE statement, which functions a bit like the below. (Pun not intended.)

SELECT keyword, search volume

FROM table

WHERE keyword LIKE ‘cloth%’

The above tells pgAdmin / PostgreSQL to get the keyword and search volume columns from table, where the keyword values match values that begin with ‘cloth’ and are followed by anything else, the percent sign. The combination of calling out a text string with an operate is known as a pattern.

When you execute the LIKE statement in a SQL query, pgAdmin will begin reading through the table rows to see if the pattern you’ve specified returns any matches. For the season marketing technology folks, this functionality sure does resemble regex in some ways.

However, there are some differences.

• Here, instead of the * character being wildcard, the % sign serves as a wildcard matching all characters.
• If you want to match a single character, the underscore character is used.

## LIKE Statement Syntax & Examples

Let’s try some examples. Below, we’ll call on the faithful DVD rental practice database, and run a query for customers that have first names like Jen. (Jennifer, Jenny, etc.) Our below code produces the following result.

SELECT first_name,last_name

FROM customer

WHERE first_name LIKE ‘Jen%’;

That being considered, there are other ways we can use the like statement. Above, we used a wildcard to match any endings to a particular string.

Conversely, we could execute a SQL query that specifies a certain ending value, with the wildcard preceding. If we extend that example to such a query below, we should see the following result:

SELECT first_name,last_name

FROM customer

WHERE first_name LIKE ‘%y’;

Above, we’ve flipped the tables so we capture every possible beginning condition under this pattern. Also important to note, the patterns aren’t limited beginnings or ends. You can use this wildcard in the middle, etc. Consider the following:

SELECT first_name,last_name

FROM customer

WHERE first_name LIKE ‘%er%’;

Now, on top of all this awesomeness, realize we’ve been using it as a matching filter. We can also employ the NOT LIKE syntax to exclude values meeting the pattern we’ve specified.

Let’s mix things up a bit. We mentioned a second type of pattern matching character that we haven’t used yet: the underscore.

SELECT first_name,last_name

FROM customer

WHERE first_name LIKE ‘_her%’;

Above, we’ve made a similar ask. However, instead of requesting all possible matches, we’ve mandated SQL only return the ‘er’ string that begins with ‘h’.

To throw you a quick curveball, it’s worth noting the LIKE statement is case sensitive in its matching. Could be bad, could be good, could be neither. However, there’s a way for you to force case insensitivy on the queries. The difference in your statement appears relatively minor. Instead of using a function that calls LIKE or NOT LIKE, you’ll use ILIKE.

## Wrap-Up

Hope you found this useful! Stay tuned for more SQL learnings and application. If you’re new here, visit the page on how to learn SQL. If you’re interested in more educational material, check out our ongoing series of how to develop Amazon Alexa voice search skills, and getting started with algorithmic trading. Cheers!

## Building an Interactive Quiz Alexa Skill, Part 1

Hello Alexa geeks! Welcome back to our journey of learning how to develop Amazon Alexa Skills for the Echo and more. Last time, we completed the build process for our first simple “fact-dispensing” Alexa Skill.

In this article, we’ll start the process for a skill that accepts user input in the form of a quiz, fun! If you recall from our first skill, there are two parts to skill development:

1. The Skill service development, in AWS (Lambda)
2. The Alexa Skill interface details through the Amazon / Alexa Developer Console

## Getting Started in AWS Lambda

You’ll notice as we progress from our early articles, there will be less detail paid to more basic instructions, such as our first! First, log in to the Amazon AWS portal.

Navigate to the Lambda service. If you’re the casual developer just working in this course, odds are the Lambda link will be near the top of screen under “Recently Visited Services”. Once you’ve clicked through, click, “Create a Lambda Function”.

On the next screen, you should see something like “Select blueprint” (Note: at the rate of change Amazon has been pursuing, this screen could change, even in a matter of weeks!) Click the “Blank Function” option, we’re starting this one from scratch!

The next screen should be, “Configure triggers”. Click inside the gray dash-outlined box, and select, “Alexa Skills Kit” from the dropdown menu. Click next!

## AWS Lambda Function Configuration for Alexa

Now we should be able to configure the basics of our function. Enter the following:

• Function name
• Description

The default runtime environment should be Node.js 4.3. If not, change it to Node.js 4.3.

(Note: Amazon just introduced support for Node.js 6.10, so that may be the preferred format going forward!) Will try to provide an article update, should that be the case.

Onward! Now, we need to upload some code to this burgeoning success. Throwback time, do you remember the files we downloaded in one of the first articles? Time to go back to them again. In your folder of numbered skill templates, go to “2-reindeerGames”, “src” folder and safely open the index.js file in your text editor of choice.

Copy and paste (replacing all previous code) into the code window that should appear.  This assumes you’ve selected the Code entry type of “Edit code inline” for the Lambda function code. As we work on more increasingly more advanced skills, we will likely use the zip upload feature to accommodate additional code resources. The astute will note we’ve merely copied and pasted code here. Yes, we’ll go back and customize soon. 🙂

Beneath the code window, leave the index.handler intact, select an existing role option in the Role dropdown menu, and use the role we previously created. Leave the other settings as-is, click the “Next” button to review details, and click, “Create Function”!

Be sure you take note / record the ARN in the upper right-hand corner, as we’ll need that in our forthcoming Skill Interface development section.

## Wrap-Up

That’s the first part! I don’t know about you, but this is getting easier as I go. We’ll next cover the skill interface and customization to make it your own skill. If this is your first article, be sure to check out the running stable of articles on how to learn Amazon Alexa skill development. Also, there’s a growing body of work on how to learn PostgreSQL, and some fledgling articles on learning algorithmic trading, for good measure.