Learn Machine Learning
Up to now, we have tried 3 different Kaggle journeys, the Titanic Survivors, the Iowa House Prices and the hand written digits recognition. Those journeys covered popular Machine Learning topics, such as classification, regression, deep learning, and so on. I would suggest fans of Machine Learning to start with those
Docker
We have just installed Docker on our Raspberry Pi, now it is time to bring into action. Do you remember the EZW, Easy Weather Forecast Flask app? Yes, we are going to deploy this EZW flask app. Of course, we do it in the Docker way! Prerequisites 1. Docker installed
devops
In this article, we will talk about Docker and Raspberry Pi. Docker is a platform you may probably hear it often recently. It is hot because it helps people simplify the DevOps process. It lets developers build light-weight applications. Then they can ship and run their applications "anywhere"
We code therefore we are
We have tried to code an Easy Weather Forecast Tool with Python. Yes, it is fast and easy, but it is just not that convenient to run anything on a command prompt. It is so.... 80s. It would be better if we can make a single-page Flask web app. Then
Learn Machine Learning
On last post, we tried our image recognition project with handwritten digits. We used a Convolutional Neural Network (CNN) to train our machine and it did pretty well with 99.47% accuracy. We learnt how a CNN works by actually implementing a model. Today, we move one step further to
Learn Machine Learning
On our data science journey, we have solved classification and regression problems. What's next? There is one popular machine learning territory we have not set feet on yet --- the image recognition. But now the wait is over, in this post we are going to teach our machine
Learn Machine Learning
When I have a data project in mind and have no idea on where to start modeling, I will always use the Random Forest model. It is not because of its catchy name and the fact that I always misspell it as Rain Forest , it is quick, convenient, easy to
We code therefore we are
We posted a post on top programming languages in 2017, six months ago. It's 2018, it's a New Year, yes it is! So we are going to do the same thing again. Last year, we found our top programming languages based on following criteria: * popularity -
We code therefore we are
When we go for data science projects, like the Titanic Survivors and Iowa House Prices projects, we need data sets to process our predictions. In above cases, those data sets have already been collected and prepared. We only need to download the data set files then start our projects. But
Learn Machine Learning
Previously on CodeAStar: The data alchemist wannabe opened the first door to "the room for improvement", where he made better prediction potions. His hunger for the ultimate potions became more and more. He then discovered another door inside the room. The label on the door said, "Ensemble
Learn Machine Learning
Previously on CodeAStar: A data alchemist wannabe tried to win big in real estate market, he then used Kaggle's Housing Regression data set, engineered the features and fit them in a bunch of models. Dang! Nothing fancy happened. But he then discovered "the room", the room
Learn Machine Learning
Okay, yes, once again, it is a catchy topic. BUT, this post is indeed trying to help people (including me) to gain an upper hand in real estate market, using data science. From our last post (2 months ago, I will try to update this blog more frequently :]] ), we learn