In this post, I’m going to be discussing with you guys some of the tools that have been quite helpful to me: “Machine Learning Beginners Tools for Coders”……….
Switching from coding to machine learning has been one of the best decisions, I’d make which comes with several advantages earned in the transitioning process for me.
The intent to switch from coding which involves being able to deal with quite a number of programming languages to machine learning its an individual thing that comes with a lot of factors in the decision process.
But the catch is, as a coder it’s quite easy to adapt to your new found something because switching from one tech to another techy stuff doesn’t come with much of a hassle, since you’re already familiar with the pretty basics stuffs.
For me, I just had that inner spark. Came like a trigger, saw myself researching on my new found interest.
On the flip side, making a decision and trying out new things comes alongside few or more challenges encountered during any level changing transitioning.
It’s then, you can feel the growth, the new course to plunge into to challenge oneself and eventually learn few things that add up later on.
Thus, I’d like to use this platform, to share with you guys, some of the tools and quick starters……or rather cheat sheets, in a nutshell, which you’d want to use to quickly delve into the transition process: from Coding to Machine Learning.
Machine Learning Tools
So without further ado, let’s delve into the various tools, you’d need whether you just beginning or a professional in the Machine Learning Space.
This is an open source machine learning library- free for all, where you get access to myriads of resources of which you can use to deploy your projects in the natural language and computer vision.
This is a text editor like the ones like, Sublime, VS Code or other Dev environments. With this tool, you can write individual codes in cells which make up a whole and execute each to test the results.
This tool is comprised of machine learning library that encapsulates several complex computational analysis of which you can deploy to your machine learning model. Usually featuring several regression, classification, support vectors machines and clusters algorithms.
This is another simplistic tools which enables users to achieve task by simply storing data into a spreadsheet and deploying it into Pandas to enable users make data frames.
Pandas comprises a python library. This python library, allows users to read – write data from spreadsheets in a .csv file format. This is achieved in structures called the Dataframes.
Keras is another tools which has been implemented in TensorFlow v2 when you get Tensor; has discussed earlier. This tools allows users to make neural networks. Unarguably, the main aggregate in a machine learning model. What the neural networks or nets does, is that it allows you to train your machine learning model.
What NumPy does, is that it replaces its lists with Python lists. So you’d ask, why that? Because,simply, the NumPy list processes is more faster as when compared to the latter, thus, its deployment.
Spark is a tool which offers users real time computation using the PySpark framework.
This tool is framework that comes with support compatible for various other programming languages, thus, an easy escape for processing large chunks of data. The framework comes with several other advantages to it, like fast caching for disk, fast processing and seamless run on the RDDS.
The tool is a Python library and optimization compiler tool that is used for tinkering and evaluation of mathematical equations and expressions, matrixes and much more. The tool adapts the NumPy syntax to run the various programs on the computer system.
Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks.
NLTK means the Natural Language Toolkit. This tool lets users use their natural language in English in the processing of data programs in the Python environment.
This tools is simply a library in the machine learning space as the name implies, lets users plot and make all kinds of graphs to better illustrate their data.
The data can be deployed into pie charts, instagrams, bar charts and lots more.
Let me know if you have further questions in the comment section below. Thanks for reading this blog. Catch you in the next post.
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