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DSC_0013 Zach Doty Cover Photo for Introduction to Machine Learning

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.

DSC_0104 Zach Doty Cover Photo for Learning LIKE SQL Statement

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%’;

2017-03-30-001-LIKE-SQL-Statement-Example

 

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’;

2017-03-30-002-LIKE-SQL-Statement-Example

 

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%’;

2017-03-30-003-LIKE-SQL-Statement-Example

 

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%’;

2017-03-30-004-LIKE-SQL-Statement-Example

 

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!

DSC_0002 Zach Doty Cover Photo for Interactive Alexa Quiz Skill Development

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

 

2017-03-01-001-Alexa-Skill-Develpoment-Framework

 

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”.

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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!

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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!

2017-03-27-003-Alexa-Quiz-Skill-Configure-Alexa-Skills-Kit-Trigger

 

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.

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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.

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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”!

2017-03-27-006-Alexa-Quiz-Skill-Lambda-Function-Creation

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.

Share your experience, thoughts and feedback in the comments below. Don’t be a stranger, help your friends along in Alexa Skill development and share with them. Cheers!

 

 

 

 

 

 

The IN Statement: SQL Fundamentals

Howdy, budding SQL masters! It’s been a minute since the last SQL post! I’ve taken some time to devote to Alexa Skills, some other personal housekeeping, and adopting a dog with my wife!!

Last time we talked SQL, we learned the BETWEEN statement to hone in on the exact values we wanted for a SQL query. Prior to that, we looked at the LIMIT statement to get a small return data sample and also working with ORDER BY to take sorting and filtering into our own hands.

Today we’ll be working with the IN statement.

About the IN Statement

The IN SQL operator is a companion to the WHERE clause to see if a value matches any value in a list of values. Sounds something kind of like VLOOKUP’s, yeah? The syntax of an IN statement might look like:

value IN (value1, value2)

The expression in your SQL query will return true if the value(s) you’ve specified in the query match any value in the list you’re referencing.

Sub-queries: Using the IN Statement with SELECT

The list of values is not limited to a static number or list of strings. That is, you can be a bit more dynamic and free-flowing with your query by getting the value via a SELECT statement.

This is also known as a sub-query. To illustrate, the syntax might look like:

value IN (SELECT value FROM table)

NOT IN: Not Making Fetch Happen

The IN statement has a similar corollary as the BETWEEN statement- a NOT modifier. So, we can take the inverse of a statement. (Drawing on some experience in data analysis, this can just as, if not more useful than the original statement!)

value NOT IN (SELECT value FROM table)

 

Sample SQL Queries for the NOT IN Statement

Alright, let’s run some sample queries using the IN statement! Here’s our first go, working again with the ever-present dvdrental sample database. Below, we are selecting rental information for customers matching only a certain ID cohort (think perhaps loyalty group?), with a few columns and sorting by descending order for the return date:

SELECT customer_id,rental_id,return_date

FROM rental

WHERE customer_id IN (1,2)

ORDER BY return_date DESC;

Below, we see our results returned as intended!

2017-03-13-001-IN-SQL-Statement-Example 

How about our corollary? If we want to add the NOT IN modifier, it’s as straightforward as you would imagine.

FROM rental

WHERE customer_id NOT IN (1,2)

ORDER BY return_date DESC;

2017-03-13-002-IN-SQL-Statement-Example-2

Above, we again receive the desired result. I believe we also get a lesson in the importance of clean data here, as blank values are presented first. Let’s try one more sample query, just for kicks. We’ll switch it up ever so slightly, now working with the payment table.

SELECT city_id,city,last_update

FROM city

WHERE country_id IN (44,82,60)

ORDER BY city ASC;

2017-03-13-002-NOT-IN-SQL-Statement-Example

Above, we’ve demonstrated that we don’t necessarily need to display a column we use in the query (country_id) and have sorted by city name in ascending order.

 

Wrap Up

So why the IN statement? At face value, it seems rather simplistic among SQL clauses we’ve explored thus far. Let’s consider the following.

The IN statement allows us to avoid a messy swath of values courtesy of BETWEEN (if we’re working with a proper large data set), or daisy chain list of a bunch of equals OR statements. Here’s another plus, it’s thought by some that pgAdmin / PostgreSQL will execute the IN query faster than the list of OR statements.

Alright, that’s it for today’s article. If you’re like me and need a quick refresh, revisit our page on Learning SQL that contains all the topics we’ve covered so far.

 

The BETWEEN Statement: SQL Statement Fundamentals

Hey there, folks! Welcome back to our journey in learning SQL. Our last few of posts covered the ORDER BY clause, LIMIT statement and the COUNT function. (See full list of SQL tutorials here.)

Today, we’re going to cover the BETWEEN statement. This is the start of some deeper material. In addition to the BETWEEN statement, we’ll also soon be covering IN and LIKE statements.

 

About the BETWEEN Statement

The BETWEEN statement (rather, operator) is used to match a value against a specified range of values. Maybe we want to get transactions between a certain dollar amount.

For example, value BETWEEN low AND high;

More about the BETWEEN statement. If the value is greater than or equal to the low value and  less than or equal to the high value, the expression returns true, or vice versa. Also, the BETWEEN operator can be rewritten by using the greater than or equal to ( >=) or less than or equal to ( <=) operators as the following statement.

value >= low and value <= high;

One other way to think about the BETWEEN statement is that takes two WHERE statements and lumps them into one. Think back to the WHERE statement, as we would say first:

SELECT column1 FROM table WHERE column1 >= 2 AND column1 <= 7;

 

To BETWEEN or NOT BETWEEN

Conversely, you can extend the usage of the original BETWEEN operator to NOT BETWEEN. This is similar in concept, except working toward exclusion, instead of inclusion. So, if we want to check if a given value is outside of a range, we can use the NOT BETWEEN operator as below.

value NOT BETWEEN low AND high;

Again, similarly to the double WHERE statements, NOT BETWEEN simplifies the burden into a single statement. For sanity, we’ll skip the SELECT WHERE example.

 

Using BETWEEN in pgAdmin / PostgreSQL

Below, we’ve taken the BETWEEN statement for a road test on our address table.

SELECT address_id,address FROM address

WHERE address_id BETWEEN 10 AND 20;

2017-02-18-001-BETWEEN-SQL-Statement-Example

How sweet was that? Syonara, comparison operators. Conversely, if we convert the above query into a NOT BETWEEN operator, we should see the below statement return the following results.

SELECT address_id,address FROM address

WHERE address_id NOT BETWEEN 10 AND 20;

2017-02-18-002-NOT-BETWEEN-SQL-Statement-Example

Okay, that was cool. Let’s hold up for a quick second. What about non-integer columns, such as data? Caveat: there’s a lot more to data types than our humble example below. For the purposes of this article, we can take a YYYY-MM-DD date and place the values into strings.

SELECT rental_id,rental_date,inventory_id FROM rental

WHERE rental_date BETWEEN ‘2005-05-24’ AND ‘2005-05-27’;

2017-02-18-003-BETWEEN-Non-Integer-SQL-Statement-Example

Voila!

Wrap-Up

Alright, for those of you reading this later, it’s 2 a.m. on a Friday night / Saturday morning, and I’m about ready to call it a night. Sorry I’m not providing a better conclusion for you today. I’m excited to delve further into more complex SQL queries. Keep track of my shared journey of a beginner learning SQL. Cheers!

 

 

DSC_0163 Zach Doty Cover Photo for ORDER BY SQL Clause

ORDER BY Clause: SQL Statement Fundamentals

Welcome back to our SQL learning journey! It’s been a week and a half since my last post on using LIMIT. Work got crazy and I got the flu! 🙁 But we’re back in action. Today, we’ll delving deeper into SQL statements in PostgreSQL: ORDER BY.

Let’s jump in. Why would you need a statement that orders data? Whenever you query data from a table, PostgreSQL will by default return the rows in the order they were inserted into the table. (Read: not the order you want.)

To sort the result set from your SQL query, you can use the ORDER BY clause in the SELECT statement and specify a certain ascending or descending order.

Sample ORDER BY Clause Syntax

Here’s an example of the ORDER BY clause would look like within a SELECT statement.

SELECT column_1, column_2

FROM table_name

ORDER BY column_1 ASC / DESC;

Some important notes for the ORDER BY clause:

  • Specify the column you want to sort by with the ORDER BY clause
    • If you sort the results by multiple columns, use a comma to separate between the two columns
  • Use ASC to sort the results in ascending order
  • Use DESC to sort the results in descending order
  • Should you leave the ORDER BY clause blank, it will use ASC (ascending) by default

ORDER BY Clause Examples in SQL Statements

Alright, first up. We’ll do a basic SELECT statement, adding both ORDER BY and LIMIT clauses. Code and screen shot below. The query should select the first and last name columns from the customer table, ordered by last name ascending (A > Z), returning only the first 10 rows.

SELECT first_name,last_name  FROM customer

ORDER BY last_name ASC

LIMIT 10;

2017-02-16-001-ORDER-BY-SQL-Statement-Example

 

Let’s try another example. What if we want to do an advanced / multiple sort? Let’s try it out, and change it up ever so slightly from the first sample query.

SELECT first_name,last_name

FROM customer

ORDER BY first_name ASC,

last_name DESC;

2017-02-16-002-ORDER-BY-SQL-Statement-Example-Multiple

If we scroll down to the first set of duplicate first names, we’ll see the last name has been presented in descending order (Z > A).

More Details About the ORDER BY Clause

Did you know? In PostgreSQL, you can ORDER BY columns that aren’t explicitly selected within the SELECT statement? E.g. We could only select first and last name, but order by their address_id value. Interesting, yes? Important caveat, other SQL programs, such as MySQL, may not let you do this.

Below:

SELECT first_name FROM customer

ORDER BY last_name ASC;

2017-02-16-003-ORDER-BY-SQL-Statement-Example-Other-Sort

Wrap-Up

Thanks for joining me and hopefully you’ve learned the essentials for the ORDER BY clause. Feel free to check out the entire journey on how to learn SQL, thanks!

Getting Started with Alexa Development 02: Signing Up to Alexa Development Portal

Welcome back to our journey in learning how to program Amazon Alexa Skills via Echo voice search. In the previous article, we walked through the process of setting up an Amazon Web Services (AWS) account. Today, we’ll set up an account at the Alexa Development portal, a distinct entity from the AWS portal.

 

Without further ado, let’s jump in. Go to https://developer.amazon.com/public/solutions/alexa.

2017-02-03-001-Alexa-Developer-Home-Screen

You should land on something like the above screen. Click on the “Sign In” button, you can create a new account from this screen if you need.

Important Note: If you already have an Amazon.com account (regular old Amazon shopping account), use those credentials to log in.

Obviously, if you’re a returning Alexa Development Portal user, you can skip the account creation process shown below. If you’re creating a new account, you’ll need to fill out a screen that will likely resemble the below, and click “Save and Continue” when you’ve finished.

2017-02-03-002-Alexa-Developer-Account-Creation

Next, you should be presented with an App Distribution and Services Agreement screen. Be sure to give it a quick read. If you want to use the services, then you’ll need to agree by clicking save and continue. 🙂

The final registration step addresses payments and whether you plan to monetize the apps you develop. For the purposes of my usage, and this learning, I checked “No” to both options before proceeding.

2017-02-03-003-Alexa-Developer-Account-Creation-Monetization

Once you finish that step, you should find yourself in the Amazon Developer Console! Good thing we got the hard material out of the way first, huh?

This should wrap up a pretty quick introductory section for setups. Feel free to visit my previous article on getting set up with AWS, or go to my learning home page on how to start developing Alexa Skills. Thanks and see you at the next article!

bluebonnets Zach Doty cover photo for SQL COUNT Function

SQL Statement Fundamentals: The COUNT Function

 

Hello, hello, hello SQL fans! (Or gracious friends and family perusing the site. 🙂 ) The journey into learning SQL continues, and today we’ll cover the COUNT function. Jumping right into it, here’s our working definition of the SQL COUNT function:

The COUNT function should return the number of input rows that match a specific condition of a query.

Rather, this would appear to work similarly in concept to the COUNTIF(s) formula(s) in Excel.

COUNT Statement Syntax Examples

Here’s what a simple COUNT SQL statement might look like:

1. Basic COUNT (*) FROM SQL Statement

SELECT COUNT (*) FROM table;

Breaking it down a bit, the COUNT () function returns the number of rows returned by a SELECT clause. When you apply the COUNT () statement to the entire table, pgAdmin/PostgreSQL will scan the entire table in a sequential manner.

Additionally, you can specify a certain column count in your COUNT statement for better readability:

2. COUNT (column) FROM SQL Statement

SELECT COUNT(column) FROM table;

Similar to the COUNT(*) function, the COUNT(column) function returns the number of rows returned by a SELECT clause. However, if you have empty or NULL values, the COUNT function will not take those into account.

3. COUNT (DISTINCT column) FROM SQL Statement

If we do a bit of application from our past learnings, we can make a COUNT with DISTINCT SQL statement:

SELECT COUNT(DISTINCT column) FROM table;

Applying the COUNT SQL Function

Alright, so let’s work our way toward applying what we’ve learned. To start, let’s do the traditional probe of the table before diving in, to familiarize ourselves. Below, we’ve done a basic,

SELECT * FROM address;

2017-02-01-001-SELECT-ALL-Starting-SELECT-COUNT

As we get familiar with the table, we can scroll down and see this particular table has 605 rows in it. This will be a reference point as we continue.

Moving forward, we’ll execute a basic SELECT COUNT (*) FROM address; SQL query. Below, you’ll see a slightly different result was returned. Inst3ead of 605 rows, 603 was returned. At this point, kindly reference our note about empty / NULL  values being excluded from the COUNT function.

2017-02-01-002-SELECT-ALL-COUNT-1

We’ve established a general proof of concept for the SELECT COUNT statement, considering the reduced load on the server and yourself, for a quick count. Let’s now try calling specific columns. In our first exploration of the address table, we saw a number of columns, including the district. Let’s say we want to get a count for how many districts/states our customer base covers.

2017-02-01-003-SELECT-COUNT-DISTINCT-1

Above, a count of 378 distinct district values has been returned. Wow, what coverage!

A quick aside for future usage, you can also nest the column reference in its own set of parentheses, as shown and returned below.

2017-02-01-004-SELECT-COUNT-DISTINCT-Nested

Wrap-Up

There you have it! We’ve learned a bit about the COUNT function, what it does and how to use it. It will likely come in handy for future articles, particularly when we delve into group-by excercises. If you missed it, here’s the previous article on learning how to use SELECT WHERE and another recent article on learning how to use SELECT DISTINCT. Also, to start from the beginning, here’s my running list of articles on how to learn SQL. Cheers!

DSC_0071 Zach Doty Cover Photo for Amazon Alexa Skill Development AWS Account Setup

Getting Started with Amazon Alexa Development: Signing Up To Amazon Web Services (AWS)

Welcome back, folks, to our foray into Amazon Alexa Skill Development. If you’ve visited the blog recently, you’ll notice I’ve been juggling a few subjects for a minute, including SQL. For context, here’s our first article, starting Amazon Alexa skill development from the absolute beginning. (Following my haphazard skill development of Silly Marketing Strategies at the beginning of the year.)

In the previous article, we prepared ourselves for skill development by downloading public sample materials from Amazon’s Alexa Skills Kit Github page. Now, we’ll look at Amazon Web Services, one of the world’s largest (is it the largest?) public cloud computing platforms.

First, navigate to https://aws.amazon.com/. Click on “Create an AWS Account”, or equivalent, if you’re seeing something different. Note: this sign-up shouldn’t incur cost for you today, unless you choose otherwise. (Quick disclaimer, I’ll obviously try to supply the most accurate information possible. However, I cannot ultimately guarantee its accuracy. That you must do for yourself.)

2017-01-30-001-AWS-Home-Screen

Here, you’ll sign up and create a new account or log in. Note: if you have a regular Amazon login, I believe you can use that here. Because I have an existing Amazon account, most of my steps will follow accordingly, however, I’ll try to recreate where I can, like below.

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If you’re creating a new account, then you’ll be prompted to choose between a company account and a personal account. Below is a preview of what the personal account signup page might look like.

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Next, you’ll be prompted to set up payment options. (Obviously, we’re s electing a free account for the purposes of this educational exploration.)

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You’ll next be asked to verify your phone number via a call requesting a PIN shown on screen. From there, you should be able to proceed  to the support plan selection screen, upon which I recommend choosing the free Basic version. After selecting all the appropriate options, you should be able to create your account!

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Now that you’ve created an account, click “Sign In to the Console” or “Complete Sign Up”. You’ll re-enter your login credentials and proceed. You should now land on the Developer Console root page. (Note: Amazon, like Google, runs a ton of UI tests, so what you see may be slightly different than the below.)

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The first order of business in our new account is to secure it. Click on your name in the top right and in the resulting dropdown menu, select “My Security Credentials”.

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Manage / Activate your MFA, and select a virtual device. This means you’ll need to perform some setup so you can scan a QR code with your phone (via Google QR code scanner app). I’ve skipped some illustrations and details here, because I’m not sharing my details, nor should you. What a somber ending to the article! In the next post, we’ll cover signing up for the Alexa Developer Portal, as we get move toward becoming proficient Amazon Alexa Skill Developers.