First off, we will need to make a list with all sorts of numbers and include duplicate numbers.
list = [1,2,3,4,5,6,7,8,9,10,11,10,9,8,7,6,5,4,3,2,1]
Next we will need to start writing our for loop statement, however, before we begin writing a for loop, we want to include two empty variables outside of the for loop. One will be our temporary numbers and the second a unique numbers list.
temp = set()
unique = 
Set() is a builtin method in Python which returns an unordered collection data type that is iterable, mutable and has no duplicate elements. …
Using Python 3
Sum of Squares
Let’s start off by understanding what the Sum of Squares is actually. A square is ‘x’ to the second power (x²) or ‘x*x’. We want to know the sum of ‘x²’. So if we substitute 10 for ‘x’ and insert it into the ‘x²’ , the outcome will be 100.
Sum of Squares of Range
Now that was easy, right? I hope so, but that’s not what we are trying to write a function for. We want to spice things up a bit and find out the sum of squares of a specific range. For simplicity, let’s say that we want to find out the sum of squares until the number 10. …
As I delved deeper into artificial intelligence, past the machine learning and into something called Deep Learning. I realized that I was entering into a world that would change our current world with the capabilities it held.
With deep learning, you can basically teach a machine what images are and once the machine sees new information it can differentiate between the classifications of each image based on what you’ve taught it. Now, a classic dataset that almost every data scientist has attempted is the MNIST dataset.
The MNIST dataset consists of handwritten digits from 0 to 9 and with less than 20 lines of code we will train a deep learning neural network model using Keras. If you’d like you can follow along in a Google Colab. You won’t have to worry about obtaining the CSV file for the dataset as the MNIST dataset is already built into the Keras library. …
Don’t Be Intimidated It’s Only Machine Learning
What is Machine Learning?
Machine learning arose from a simple question: “Can a computer go beyond ‘what we know how to order it to perform’ and learn on its own how to perform a specified task?”
Before understanding how we got to machine learning we’ve got to understand how classical programming works. In classical programming, humans make up a set of rules and data. Humans input those rules and data and the output is ta-da answers.
With machine learning, it’s a tad bit different, humans input data as well as the anticipated answers from the data and the output are the rules. The advantage of these rules is that you can then apply it to new data to produce almost original answers (depending on your modeling accuracy). …
Hakuna Matata, what a wonderful phrase, it means no worries for the rest of your days unless you’re a citizen of Tanzania in need of fresh, accessible water. The beautiful country, Tanzania, has a population boasting 57.31 million people as of 2017. A country known for several things: it’s vast wilderness areas, home to the mighty Mount Kilimanjaro, home of the Makonde people that are well-known for their abstract ebony carvings, Zanzibar is also famous for the beautifully detailed carved Arab chests and doors. But this beautiful country has a big problem, ready and available clean water.
The focus behind this project week was to showcase what we’ve learned throughout four weeks of covering machine learning techniques. We got the opportunity to showcase our skillset using the Tanzania’s Ministry of Water dataset and asked to make sense of the data, derive an accuracy score using machine learning techniques and build visualizations that help explain the Tanzania water issue. Our cohort also held an internal Kaggle competition to provide a motivating factor for those inclined to find the highest accuracy score. We had a total of 4 days to work with this dataset, show our findings and beat two benchmark accuracy scores 0.60 and 0.70. …
“Wait! Mom! One more EPISODE! PLEASE! COME ON! I did all my chores and gave your pet Begonia a pep talk for 30 minutes!”
Yes, we’ve all been there, maybe not as far as giving an Al Pacino, Any Given Sunday, type of inspirational speech to the plant part, but hooked on a show nonetheless.
For nearly the last two decades there has been only one real dominant pioneer in the game of streaming and sitting on that digital iron throne has been none other than Netflix. With nearly two decades of a head start, unencumbered by any real threat to it’s digital throne, it looks as if the kingdom of video streaming will be heating up in 2019 with players such as Disney, Apple and AT&T really getting into the mix of things with similar video services. …