The tryout

Hello and welcome back! Today we’ll see an example of AutoML and what it can do. I will be using Auto-Sklearn as an example and might explore others in the future.

Today we will start with installing Auto-Sklearn and I will explain some errors I came across and how I troubleshoot them. I am using Jupyter Notebook for all of this as well as pip and homebrew.

Firstly I started by installing auto-sklearn by using pip.

$ pip install auto-sklearn

This should work according to the auto-sklearn documentation. Unfortunately, that was not the case for me. …

The implementations

There are many implementations of AutoML that you can try. Some are paid services, and some are free source code. The lists below are by no means complete or final.

AutoML services

All of the big three cloud services have some kind of AutoML. Amazon SageMaker does hyperparameter tuning but doesn’t automatically try multiple models or perform feature engineering. Azure Machine Learninghas both AutoML, which sweeps through features and algorithms, and hyperparameter tuning, which you typically run on the best algorithm chosen by AutoML. …

The options

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Welcome back! Now that we looked at what AutoML is in the previous blog, let’s dive a little into it. Today we’ll look at some great AutoML tools and libraries that are useful and that you might be interested in.


The future of Machine Learning?

IMG src:

So, as we know, machine learning is one of the most complex parts of Data Science. But what if there was a more time efficient way, or a more simpler way to do models, or maybe an automatic way? Well, today we’ll explore what Auto ML is.

What is it?

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. …

The continuation

Hey! Welcome back. Today I’ll coclude my tips for Python series with the last ones that I have.

Lets start with:

Transpose a Matrix

So, when it comes to matrices, sometimes we need to make the columns into rows. In python we can achieve it by designing some loop structure to iterate through the elements in the matrix and change their places or we can use the following zip() function in conjunction with the * operator to unzip a list which becomes the transpose of the given matrix.

Let’s check it out!

x = [[31,17],
[40 ,51],
[13 ,12]]
print (zip(*x))

The one with simple tricks

Hey! This time I’m bringing you some cool pythonic tricks that can come in handy in just every day coding. Lets start with:

Reversing a List!

Bet you didn’t know there was an easy way! To do so, all you have to use is the reverse() function. This function can handle both numeric and string data types in a list.

Lets take a look!

names = ["Tom", "Lain","Sam" ]
['Sam', 'Lain', 'Tom']

Easy! now lets look at:

Print list elements in any order!

So, lets say you need to print the values of a list in different orders. Well, you can…

The one about the counter

Hey! I’m back with another tip for python that you can use to better your coding and speed up your process! Today we’ll talk about Python Counter.

Python Counter is a container that will hold the count of each of the elements present in the container. The counter is a sub-class available inside the dictionary class.

The counter is a sub-class available inside the dictionary class. Using the Python Counter tool, you can count the key-value pairs in an object, also called a hash table object.

Basically, Python Counter takes into input a list, tuple…

The one with the generators!

Hey! welcome back! I wanted to bring to show something very cool. Its called a generator. Basically a generator functions allow you to declare a function that behaves like an iterator, i.e. it can be used in a for loop.

I have introduced you to generators before in the article about yield functions. Today we will look another way to improve our code by using generators and substitute our list comprehensions.

The performance improvement from the use of generators is the result of the lazy (on demand) generation of values, which translates to lower memory usage. Furthermore, we do not…

The one with cash

Welcome back! Today we’re gonna talk about a decorator that can help you speed up processes or functions in python.

Usually, when we create a function, we rely on the computational power to complete the process that we ask of it, whether it’s a mathematical process or a classification process we normally just call the function as many times as needed even though it might output the same result every time.

Here I will show you an easier way to speed up those processes and also help your computer out! We will use the functools library…

The one about the lazy formatting!

Hey! welcome back, here I will show you a cool Jupyter notebook and Jupyter lab extension that will save you loads of time when you want to make your code look presentable.

Ok, here’s the setup. You are writing some code for a job application or for your job and you will have people look at your code but you want to make sure it looks presentable! Well, there’s an awesome extension for it!

I know it can take a long time and effort to make your code look presentable but not anymore. nb_black is an easy-to-use extension that works…

Ignacio Ruiz

A Data Scientist in the making!

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