Trending December 2023 # The 25 Best Data Science And Machine Learning Github Repositories From 2023 # Suggested January 2024 # Top 19 Popular

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What’s the best platform for hosting your code, collaborating with team members, and also acts as an online resume to showcase your coding skills? Ask any data scientist, and they’ll point you towards GitHub. It has been a truly revolutionary platform in recent years and has changed the landscape of how we host and even do coding.

But that’s not all. It acts as a learning tool as well. How, you ask? I’ll give you a hint – open source!

The world’s leading tech companies open source their projects on GitHub by releasing the code behind their popular algorithms. 2023 saw a huge spike in such releases, with the likes of Google and Facebook leading the way. The best part about these releases is that the researchers behind the code also provide pretrained models so folks like you and I don’t have to waste time building difficult models from scratch.

2023 was a transcendent one in a lot of data science sub-fields, as we will shortly see. Natural Language Processing (NLP) was easily the most talked about domain within the community with the likes of ULMFiT and BERT being open-sourced. In my quest to bring the best to our awesome community, I ran a monthly series throughout the year where I hand-picked the top 5 projects every data scientist should know about. You can check out the entire collection below:

There will be some overlap here with my article covering the biggest breakthroughs in AI and ML in 2023. Do check out that article as well – it is essentially a list of all the major developments I feel everyone in this field needs to know about. As a bonus, there are predictions from experts as well – not something you want to miss. 🙂

Topics we will cover in this article

Tools and Frameworks

Computer Vision

Generative Adversarial Networks (GANs)

Other Deep Learning Projects

Natural Language Processing (NLP)

Automated Machine Learning (AutoML)

Reinforcement Learning

Tools and Frameworks

Let’s get the ball rolling with a look at the top projects in terms of tools, libraries and frameworks. Since we are speaking about a software repository platform, it feels right to open things with this section.

How about all you .NET developers wanting to learn a bit of machine learning to complement your existing skills? Here’s the perfect repository to get that idea started! chúng tôi a Microsoft project, is an open-source machine learning framework that allows you design and develop models in .NET.

You can even integrate existing ML models into your application, all without requiring explicit knowledge of how ML models are developed. chúng tôi is actually used in multiple Microsoft products, like Windows, Bing Search, MS Office, among others.

ML.NET runs on Windows, Linux and MacOS.

Machine learning in the browser! A fictional thought a few years back, a stunning reality now. A lot of us in this field are welded to our favorite IDEs, but chúng tôi has the potential to change your habits. It’s become a very popular release since it’s release earlier this year and continues to amaze with its flexibility.

As the repository states, there are primarily three major features of TensorFlow.js:

Develop machine learning and deep learning models in your browser itself

Run pre-existing TensorFlow models within the browser

Retrain or fine-tune these pre-existing models as well

If you’re familiar with Keras, the high-level layers API will seem quite familiar. There are plenty of examples available on the GitHub repository, so check those out to quicken your learning curve.

What a year it has been for PyTorch. It has won the hearts and now projects of data scientists and ML researchers around the globe. It is easy to grasp, flexible, and is already being implemented across high profile researches (as you’ll see later in this article). The latest version (v1.0) already powers many Facebook products and services at scale, including performing 6 billion text translations a day. If you’ve been wondering when to start dabbling with PyTorch, the time is NOW.

If you’re new to this field, ensure you check out Faizan Shaikh’s guide to getting started with PyTorch.

While not strictly a tool or framework, this repository is a gold mine for all data scientists. Most of us struggle with reading through a paper and then implementing it (at least I do). There are a lot of moving parts that don’t seem to work on our machines.

And that’s where ‘Papers with Code’ comes in. As the name suggests, they have a code implementation of all the major papers that have been released in the last 6 years or so. It is a mind-blowing collection that you will find yourself fawning over. They have even added code from papers presented at NIPS (NeurIPS) 2023. Get yourself over there now!

Computer Vision

Thanks to falling computational costs and a surge of breakthroughs from the top researchers (something tells me those two might be linked), deep learning is accessible to more people than ever before. And within deep learning, computer vision projects are ubiquitous – most of the repositories you’ll see in this section will cover one computer vision technique or another.

It is simply the hottest field in deep learning right now and will continue to be so for the foreseeable future. Whether it’s object detection or pose estimation, there’s a repository for seemingly all computer vision tasks. Never a better time to get acquainted with these developments – a lot of job openings might come your way soon.

Detectron made a HUGE splash when it was launched in early 2023. Developed by Facebook’s AI Research team (FAIR), it implements state-of-the-art object detection frameworks. It is (surprise, surprise) written in Python and has helped enable multiple projects, including DensePose (which we will talk about soon).

This repository contains the code and over 70 pretrained models. Too good an opportunity pass up, would’t you agree?

Object detection in images is awesome, but what about doing it in videos? And not just that, can we extend this concept and translate the style of one video to another? Yes, we can! It is a really cool concept and NVIDIA have been generous enough to release the PyTorch implementation for you to play around with.

The repository contains videos of how the technique looks, the full research paper, and of course the code. The Cityscapes dataset, available publicly post registration, is used in NVIDIA’s examples. One of my favorite projects from 2023.

Training a deep learning model in 18 minutes? While not having access to high-end computational resources? Believe me, it’s already been done.’s Jeremy Howard and his team of students built a model on the popular ImageNet dataset that even outperformed Google’s approach.

I encourage you to at least go through this project to get a sense of how these researchers structured their code. Not everyone has access to multiple GPUs (or even one) so this was quite a win for the minnows.

Another research paper collection repository! It’s always helpful to know how your subject of choice has evolved over a span of multiple years, and this one-stop shop will help you do just that for object detection. It’s a comprehensive collection of papers from 2014 till date, and even include code wherever possible.

Let’s turn our attention to the field of pose detection. I came across this concept this year itself and have been fascinated with it ever since. That above image captures the essence of this repository – dense human pose estimation in the wild.

The code to train and evaluate your own DensePose-RCNN model is included here. There are notebooks available as well to visualize the DensePose COCO dataset. Pretty good place to kick off your pose estimation learning.

The above image (taken from a video) really piqued my interest. I covered the release of the research paper back in August and have continued to be in awe of this technique. This technique enables us to transfer the motion between human objects in different videos. The video I mentioned is available within the repository – it will blow your mind!

This repository further contains the PyTorch implementation of this approach. The amount of intricate details this approach is capable of picking up and replicating is incredible.


I’m sure most of you must have come across a GAN application (even if you perhaps didn’t realize it at the time). GANs, or Generative Adversarial Networks, were introduced by Ian Goodfellow back in 2014 and have caught fire since. They specilize in performing creative tasks, especially artistic ones. Check out this amazing introductory guide by Faizan Shaikh to the world of GANs, along with an implementation in Python.

We saw a plethora of GAN based projects in 2023 and hence I wanted to create a separate section for this.

Let’s start off with one of my favorites. I want you to take a moment to just admire the above images. Can you tell which one was done by a human and which one by a machine? I certainly couldn’t. Here, the first frame is the input image (original) and the third frame has been generated by this technique.

Amazing, right? The algorithm adds an external object of your choosing to any image and manages to make it look like nothing touched it. Make sure you check out the code and try to implement it on a different set of images yourself. It’s really, really fun.

What if I gave you an image and asked you to extend the boundaries by imagining what it would look like when the entire scene was captured? You would understandably turn to some image editing software. But here’s the awesome news – you can achieve it in a few lines of code!

This project is a Keras implementation of Stanford’s Image Outpainting paper (incredibly cool and illustrated paper – this is how most research papers should be!). You can either build a model from scratch or use the one provided by this repository’s author. Deep learning wonders never cease to amaze.

If you haven’t got a handle on GANs yet, try out this project. Pioneered by researchers from MIT’s CSAIL division, it helped you visualize and understand GANs. You can explore what your GAN model has learned by inspecting and manipulating it’s neurons.

I would like to point you towards the official MIT project page, which has plenty of resources to get you familiar with the concept, including a video demo.

This algorithm enables you to change the facial expression of any person in an image. It’s as exciting as it is concerning. The images above inside the green border at the originals, the rest have been generated by GANimation.

The link contains a beginner’s guide, data preparation resources, prerequisites, and the Python code. As the author mentioned, do NOT use it for immoral purposes.

This project is quite similar to the Deep Painterly Harmonization one we saw earlier. But it deserved a mention given it came from NVIDIA themselves. As you can see in the image above, the FastPhotoStyle algorithm requires two inputs – a style photo and a content photo. The algorithm then works in one of two ways to generate the output – it either uses photorealistic image stylization code or uses semantic label maps.

Other Deep Learning Projects

The computer vision field has the potential to overshadow other work in deep learning but I wanted to highlight a few projects outside it.

Audio processing is another field where deep learning has started to make it’s mark. It’s not just limited to generating music, you can do tasks like audio classification, fingerprinting, segmentation, tagging, etc. There is a lot that’s still yet to be explored and who knows, perhaps you could use these projects to pioneer your way to the top.

Here are two intuitive articles to help you get acquainted with this line of work:

And here comes NVIDIA again. WaveGlow is a flow-based network capable of generating really high quality audio. It is essentially a single network for speech synthesis.

This repository includes a PyTorch implementation of WaveGlow along with a pre-trained model which you can download. The researchers have also listed down the steps you can follow if you want to train your own model from scratch.

Want to discover your own planet? That might perhaps be overstating things a bit, but this AstroNet repository will definitely get you close. The Google Brain team discovered two new planets in December 2023 by applying AstroNet. It’s a deep neural network meant for working with astronomical data. It goes to show the far-ranging applications of machine learning and was a truly monumental development.

And now the team behind the technology has open sourced the entire code (hint: the model is based on CNNs!) that powers AstroNet.

Who doesn’t love visualizations? But it can get a tad bit intimidating to imagine how a deep learning model works – there are too many moving parts involved. But VisualDL does a great job mitigating those challenges by designing specific deep learning jobs.

VisualDL currently supports the below components for visualizing jobs (you can see examples of each in the repository):






high dimensional

Natural Language Processing (NLP)

Surprised to see NLP so down in this list? That’s primarily because I covered almost all the major open source releases in this article. I highly recommend checking out that list to stay on top of your NLP game. The frameworks I have mentioned here include ULMFiT, Google’s BERT, ELMo, and Facebook’s PyText. I will briefly mention BERT and a couple of other respositories here as I found them very helpful.

I couldn’t possibly let this section pass by without mentioning BERT. Google AI’s release has smashed records on it’s way to winning the hearts of NLP enthusiasts and experts alike. Following ULMFiT and ELMo, BERT really blew away the competition with it’s performance. It obtained state-of-the-art results on 11 NLP tasks.

Apart from the official Google repository I have linked to above, a PyTorch implementation of BERT is worth checking out. Whether it marks a new era of not in NLP we will soon find out.

It often helps to know how well your model is performing against a certain benchmark. For NLP, and specifically deep text matching models, I have found the MatchZoo toolkit quite reliable. Potential tasks related to MatchZoo include:


Question Answer

Textual Entailment

Information Retrieval

Paraphrase Identification

MatchZoo 2.0 is currently under development so expect to see a lot more being added to this already useful toolkit.

This repository was created by none other than Sebastian Ruder. The aim of this project is to track the latest progress in NLP. This includes both datasets and state-of-the-art models.

Automated Machine Learning (AutoML)

What an year for AutoML. With industries look to integrate machine learning into their core mission, the need to data science specialists continues to grow. There is currently a massive gap between the demand and the supply. This gap could potentially be filled by AutoML tools.

These tools are designed for those people who do not have data science expertise. While there are certainly some incredible tools out there, most of them are priced significantly higher than most individuals can afford. So our amazing open source community came to the rescue in 2023, with two high profile releases.

This made quite a splash upon it’s release a few months ago. And why wouldn’t it? Deep learning has been long considered a very specialist field, so a library that can automate most tasks came as a welcome sign. Quoting from their official site, “The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background”.

You can install this library from pip:

pip install autokeras

The repository contains a simple example to give you a sense of how the whole thing works. You’re welcome, deep learning enthusiasts. 🙂

AdaNet is a framework for automatically learning high-quality models without requiring programming expertise. Since it’s a Google invention, the framework is based on TensorFlow. You can build ensemble models using AdaNet, and even extend it’s use to training a neural network.

The GitHub page contains the code, an example, the API documentation, and other things to get your hands dirty. Trust me, AutoML is the next big thing in our field.

Reinforcement Learning

Since I already covered a few reinforcement learning releases in my 2023 overview article, I will keep this section fairly brief. My hope in including a RL section where I can is to foster a discussion among our community and to hopefully accelerate research in this field.

First, make sure you check out OpenAI’s Spinning Up repository, an exhaustive educational resource for beginners. Then head over to Google’s Dopamine page. It is a research framework for accelerating research in this still nascent field. Now let’s look at a couple of other resources as well.

If you follow a few researchers on social media, you must have come across the above images in video form. A stick human running across a terrain, or trying to stand up, or some such sort. That, dear reader, is reinforcement learning in action.

Here is a signature example of it – a framework to train a simulated humanoid to imitate multiple motion skills. You can get the code, examples, and a step-by-step run-through on the above link.

This repository is a collection of reinforcement learning algorithms from Richard Sutton and Andrew Barto’s book and other research papers. These algorithms are presented in the form of Python notebooks.

As the author of this repo mentioned, you will only truly learn if you implement the learning as you go along. It’s a complex topic, and giving up or reading the resources like a storybook will lead you nowhere.

End Notes

And that bring us to the end of our journey for 2023. What a year! It was a joyful ride putting this article together and I learned a lot of new stuff along the way.


You're reading The 25 Best Data Science And Machine Learning Github Repositories From 2023

15 Trending Data Science Github Repositories You Can Not Miss In 2023


GitHub is much more than a software versioning tool, which it was originally meant to be. Now people from different backgrounds and not just software engineers are using it to share their tools / libraries they developed on their own, or even share resources that might be helpful for the community.

Following the best repos on GitHub can be an immense learning experience. You not only see what are the best open contributions, but also see how their code was written and implemented.

Being an avid data science enthusiast, I have curated a list of repositories that have been particularly famous in the year 2023. Enjoy and Keep learning!

Table of Contents

Repositories for Learning Resources

Awesome Data Science

Machine Learning / Deep Learning Cheat Sheet

Oxford Deep Natural Language Processing Course Lectures

PyTorch – Tutorial

Resources of NIPS 2023

Open Source Softwares


TuriCreate – A Simplified Machine Learning Library



Mobile Deep Learning


Deep Photo Style Transfer




1. Learning Resources 1.1 Awesome Data Science

This GitHub repository is an ultimate resource guide to data science. It is built upon multiple contributions over the years with links to resources ranging from getting-started guides, infographics to people to follow on social networking sites like twitter, facebook, Instagram etc. There are plenty of resources waiting to be viewed, irrespective of whether you are a beginner or a veteran.

A look at table of contents of the repo says it all about the depth of resources in the repository:

1.2 Machine Learning / Deep Learning Cheat Sheet

This repository consists of the commonly used tools and techniques compiled in the form of cheatsheets. The cheatsheets range from very simple tools like pandas to techniques like Deep Learning. After giving a star or forking the repository, you won’t need to google the most commonly used tips and tricks.

To give you a glimpse, the different types of cheatsheets are pandas, numpy, scikit learn, matplotlib, ggplot, dplyr, tidyr, pySpark and Neural Networks .

1.3 Oxford Deep Natural Language Processing Course Lectures 1.4 PyTorch – Tutorial

As of now, PyTorch is the sole competitor to Tensorflow and it is doing a good job of maintaining its reputation. With the ease of Pythonic style coding, Dynamic Computations, and faster prototyping, PyTorch has garnered enough attention of the Deep Learning Community.

This repository contains codes for Deep Learning tasks ranging from learning basic of creating a Neural Network in PyTorch to coding RNNs, GANs and Neural Style Transfers. Most of the models have been implemented with as few as 30 lines of code. This speaks volume about the abstraction provided by PyTorch so that researchers may focus on finding the right model quickly rather than getting entangled in the nitty gritty of programming language or tool choice.

1.5 Resources of NIPS 2023

This repository is a list of resources and slides of all invited talks, tutorials, and workshops in NIPS 2023 conference. For those who do not know what NIPS is, it is an annual conference specifically for Machine learning and Computational Neuroscience.

Most of the breakthrough research that has happened in the data science industry in the last couple of years has been a result of the research that has been presented at this conference. If you want to stay ahead of the curve, this is the right resource to follow!

2. Open Source Softwares 2.1 TensorFlow

It has been 2 years since the official release of TensorFlow, but it has maintained the status of being the top Machine Learning / Deep Learning library. Google Brain and the community behind the development of TensorFlow has been actively contributing and keeping it abreast with the latest developments especially in Deep Learning domain.

TensorFlow was originally built as a library for numerical computation using data flow graphs. But looking at its current state, it can be pretty much said to be a complete library for building Deep Learning models. Although TensorFlow majorly supports Python, it also provides support for languages such as C, C++, Java and many more. And a cherry on the cake, it can also be run on a mobile platform!

2.2 TuriCreate – A Simplified Machine Learning Library

A recent open source contribution by Apple, TuriCreate is the talk of the day. It boasts of easy-to-use creation and deployment of machine learning models for complex tasks such as object detection, activity classification, and recommendation systems.

Being a data science enthusiast for some time now, I remember that void that had been created when Turi (the company that created GraphLab Create – an amazing machine learning library) was acquired by Apple. Everyone in the data science industry had been waiting for this kind of explosion to happen!

TuriCreate is developed specially for python. One of the best features that TuriCreate provides is its easy deployability of machine learning models to Core ML (another open source software by Apple) for use in iOS, macOS, watchOS, and tvOS apps

2.3 OpenPose

OpenPose is a multi-person keypoint detection library which helps you to detect positions of a person in an image or video at real-time speed. Developed by CMU’s perceptual computing lab, OpenPose is a fine example of how open sourced research can be easily inculcated in the industry.

One of the use cases that OpenPose helps to solve is activity detection. For example, an activity done by an actor can be captured in real time. Then these key points and their motions can be used to create animated films.

OpenPose has a C++ API which can be used to access the library. But it also has a simple command line interface to process images or videos.

2.4 DeepSpeech

DeepSpeech library is an open source implementation of the state-of-the-art technique for Speech-to-Text synthesis by Baidu Research. It is based on TensorFlow and can be used specifically for Python, but it also has bindings for NodeJS and can be used on the command line too.

Mozilla has been one of the main workforces for building DeepSpeech from scratch and open sourcing the library. “There are only a few commercial quality speech recognition services available, dominated by a small number of large companies. This reduces user choice and available features for startups, researchers or even larger companies that want to speech-enable their products and services, Together with a community of like-minded developers, companies, and researchers, we have applied sophisticated machine learning techniques and a variety of innovations to build a speech-to-text engine …” Sean White, vice president of technology strategy at Mozilla, wrote in a blog post.

2.5 Mobile Deep Learning

This repository brings about the state-of-the-art technique in data science to the mobile platform. Developed by Baidu Research, the repository aims to deploy Deep Learning models on mobile devices such as Android and IOS with low complexity and high speed.

A simple use case as explained in the repository itself is object detection. It can identify the exact location of an object such as mobile in an image. Pretty cool right?

2.6 Visdom

Visdom is a library that supports broadcasting of plots, images, and text among collaborators. You can organize your visualization space programmatically or through the UI to create dashboards for live data, inspect results of experiments, or debug experimental code.

The exact inputs into the plotting functions vary, although most of them take as input a tensor X than contains the data and an (optional) tensor Y that contains optional data variables (such as labels or timestamps). It supports all basic plot types to create visualizations that are powered by Plotly.

Visdom supports Torch and Numpy within Python.

2.7 Deep Photo Style Transfer

This repository is based on a research paper that introduces a deep learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. The approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including the transfer of the time of day, weather, season, and artistic edits. This code is based on torch.

2.8 CycleGAN

CycleGAN is a fun but powerful library which shows the potential of the state-of-the-art technique.  Just to give an example, the image below is a glimpse of what the library can do – adjusting the depth perception of the image. The catch here is that you haven’t told the algorithm which part of the image to focus upon. It does this on its own!

The library is currently written in Lua, but it can be used in command line too.

2.9 Seq2seq

Seq2seq was initially built for Machine Translation, but have since been developed to be used for a variety of other tasks, including Summarization, Conversational Modeling, and Image Captioning. As long as a problem can be moulded as encoding input data in one format and decoding it into another format, this framework can be used. It is programmed using the all popular Tensorflow library for Python.

2.10 Pix2code

This one is a really exciting project using deep learning that attempts to automatically generate code for a given GUI. When building a website or a mobile interface, front-end engineers typically have to write repetitive code that is time consuming and non-productive. This essentially prevent developers from dedicating the majority of their time to implement the actual functionality and logic of the software they are building. Pix2code intends to remedy this by automating the process. It is based on a novel approach allowing the generation of computer tokens from a single GUI screenshot as input.

Here is a video explaining the use case of pix2code.

Pix2code is written in python and can be used to convert image captures of both mobile and web interfaces to code. The project can be accessed in the link below.

End Notes


Data Scientist Vs Machine Learning

Differences Between Data Scientist vs Machine Learning

Hadoop, Data Science, Statistics & others

Data Scientist

Standard tasks:

Allocate, aggregate, and synthesize data from various structured and unstructured sources.

Explore, develop, and apply intelligent learning to real-world data and provide essential findings and successful actions based on them.

Analyze and provide data collected in the organization.

Design and build new processes for modeling, data mining, and implementation.

Develop prototypes, algorithms, predictive models, and prototypes.

Carry out requests for data analysis and communicate their findings and decisions.

In addition, there are more specific tasks depending on the domain in which the employer works, or the project is being implemented.

Machine Learning

The Machine Learning Engineer position is more “technical.” ML Engineer has more in common with classical Software Engineering than Data Scientists. It helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

The standard tasks of ML Engineers are generally like Data scientists. You also need to be able to work with data, experiment with various Machine Learning algorithms that will solve the task, and create prototypes and ready-made solutions.

Strong programming skills in one or more popular languages (usually Python and Java) and databases.

Less emphasis on the ability to work in data analysis environments but more emphasis on Machine Learning algorithms.

R and Python for modeling are preferable to Matlab, SPSS, and SAS.

Ability to use ready-made libraries for various stacks in the application, for example, Mahout, Lucene for Java, and NumPy / SciPy for Python.

Ability to create distributed applications using Hadoop and other solutions.

As you can see, the position of ML Engineer (or narrower) requires more knowledge in Software Engineering and, accordingly, is well suited for experienced developers. The case often works when the usual developer must solve the ML task for his duty, and he starts to understand the necessary algorithms and libraries.

Head-to-Head Comparison Between Data Scientist and Machine Learning (Infographics)

Below are the top 5 differences between Data scientists and Machine Learning:

Key Difference Between Data Scientist and Machine Learning

Below are the lists of points that describe the key Differences Between Data Scientist and Machine Learning:

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on data used as a training set to fine-tune some model or algorithm parameters. This encompasses many techniques, such as regression, naive Bayes, or supervised clustering. But not all styles fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-prior knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit into this category.

Data science is much more than machine learning, though. Data in data science may or may not come from a machine or mechanical process (survey data could be manually collected, and clinical trials involve a specific type of small data), and it might have nothing to do with learning, as I have just discussed. But the main difference is that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. Data science also covers data integration, distributed architecture, automated machine learning, data visualization, dashboards, and Big data engineering.

Data Scientist and Machine Learning Comparison Table

Feature Data Scientist Machine Learning

Data It mainly focuses on extracting details of data in tabular or images. It mainly focuses on algorithms, polynomial structures, and word adding.

Complexity It handles unstructured data, and it works with a scheduler. It uses Algorithms and mathematical concepts, statistics, and spatial analysis.

Hardware Requirement Systems are Horizontally scalable and have High Disk and RAM storage. It requires Graphic processors and Tensor Processors, that is very high-level hardware.

Skills Data Profiling, ETL, NoSQL, Reporting. Python, R, Maths, Stats, SQL Model.

Focus Focuses on abilities to handle the data. Algorithms are used to gain knowledge from huge amounts of data.


Machine learning helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

A Data scientist does a lot of data exploration and arrives at a broad strategy for tackling it. He is responsible for asking questions about the data and finding what answers one can reasonably draw from the data. Feature engineering belongs to the realm of Data scientists. Creativity also plays a role here, and An Machine Learning engineer knows more tools and can build models given a set of features and data – as per directions from the Data Scientist. The realm of Data preprocessing and feature extraction belongs to ML engineers.

Data science and examination utilize machine learning for this archetypal validation and creation. It is vital to note that all the algorithms in this model creation may not come from machine learning. They can arrive from numerous other fields. The model desires to be kept relevant always. If the situations change, the model we created earlier may become immaterial. The model must be checked for certainty at different times and adapted if its confidence reduces.

Data science is a whole extensive domain. If we try to put it in a pipeline, it would have data acquisition, data storage, data preprocessing or cleaning, learning patterns in data (via machine learning), and using knowledge for predictions. This is one way to understand how machine learning fits into data science.

Recommended Articles

This is a guide to Data Scientist vs Machine Learning. Here we have discussed Data Scientist vs Machine Learning head-to-head comparison, key differences, infographics, and comparison table. You may also look at the following articles to learn more –

The Difference Between Data Scientist Vs Machine Learning Scientist

Detail analysis of the career differences among data scientists and machine-learning scientists. You can learn AI skills now, whether you are just starting out or recently laid off.

Today we’ll learn more about the differences in career between Data Scientists & Machine Learning Scientists

What is Data Science?

Data science is the in-depth analysis of huge amounts of data stored in an organization’s or company’s archive. This includes analyzing the data’s origin and quality, as well as determining if it can be used for future corporate development.

Data scientists specialize in the transformation of unstructured data into business information. These experts are familiar with algorithms, data processing, artificial intelligence, statistics, and other forms of programming.

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What’s Machine Learning?

Machine learning is a branch of computer science that allows computers to learn by themselves without needing to be programmed.

Machine learning is the use of algorithms to analyze data and make predictions, without the involvement of humans. Machine Learning relies on a series of instructions, information, or observations as inputs. Machine learning is used extensively by companies like Facebook, Google, etc.

The Difference between Data Scientists & Machine Learning Scientists

These jobs may seem similar to recruiters. However, if you’re a specialist in one of these areas, you will know that there’s a difference. Both professions depend on machine learning algorithms but their day-to-day tasks may be quite different.

Machine learning scientists specialize in use cases such as signal processing, object identification, automobile/self-driving, and robots, whereas data scientists work on use factors like fraud detection, product categorization, or customer segmentation.

Data Scientists

Data scientists may have more standard job descriptions. They might also be required to learn the skills and education that they need.

A data scientist is expected to identify a problem and create a dataset. Then, they will evaluate machine learning algorithms, produce results, analyze those results, and then communicate the results with stakeholders. Data scientists are focused on business and stakeholder collaboration.

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Data scientists can expect to get the following education and skills.


BS or MS degree oriented

Data Science


Business Analytics


Python or R

Data Analysis


Jupyter Notebook



Model Building

Data scientists are often able to use code in Python or R to automate projections using machine-learning tools.

There may be a different path to becoming either a data scientist or a machine-learning scientist. For example, a data scientist may have worked as a statistician, business analyst, data analyst, or business intelligence analyst before becoming one.

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Machine Learning Scientists

Machine learning scientists are, however, more focused on algorithms and the software engineering involved in implementing them. Machine learning scientists often use the term “research” in their titles.

This means that you need to spend more time learning algorithms before creating a simpler method. These positions might be identical at different companies, so it is up to you to spot the differences when you read job descriptions.

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The following are some of the variations in education and abilities required:


degree oriented

Machine Learning

Computer Science






Signals & Distributed Systems


C++ or C

Quality Assurance


Model Deployment


Artificial Intelligence


Data science is an interdisciplinary field that draws insights from large amounts of data and high computing power. Machine learning is one of the most exciting developments in modern data science.

Machine learning allows machines to learn from large amounts of data and operate independently. Although these technologies have many applications, they do not come without their limitations. Data science is powerful but can only be used to its full extent if there are skilled workers and high-quality data.

Comprehensive Learning Path – Data Science In Python

Journey from a Python noob to a Kaggler on Python

So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to Python for data science. This path provides a comprehensive overview of steps you need to learn to use Python for data science. If you already have some background, or don’t need all the components, feel free to adapt your own paths and let us know how you made changes in the path.

Reading this in 2023? We have designed an updated learning path for you! Check it out on our courses portal and start your data science journey today.

Step 0: Warming up

Before starting your journey, the first question to answer is:

Why use Python?


How would Python be useful?

Watch the first 30 minutes of this talk from Jeremy, Founder of DataRobot at PyCon 2014, Ukraine to get an idea of how useful Python could be.

Step 1: Setting up your machine

Now that you have made up your mind, it is time to set up your machine. The easiest way to proceed is to just download Anaconda from chúng tôi . It comes packaged with most of the things you will need ever. The major downside of taking this route is that you will need to wait for Continuum to update their packages, even when there might be an update available to the underlying libraries. If you are a starter, that should hardly matter.

If you face any challenges in installing, you can find more detailed instructions for various OS here.

Step 2: Learn the basics of Python language

You should start by understanding the basics of the language, libraries and data structure. The free course by Analytics Vidhya on Python is one of the best places to start your journey. This course focuses on how to get started with Python for data science and by the end you should be comfortable with the basic concepts of the language.

Assignment: Take the awesome free Python course by Analytics Vidhya

Alternate resources: If interactive coding is not your style of learning, you can also look at The Google Class for Python. It is a 2 day class series and also covers some of the parts discussed later.

Step 3: Learn Regular Expressions in Python

You will need to use them a lot for data cleansing, especially if you are working on text data. The best way to learn Regular expressions is to go through the Google class and keep this cheat sheet handy.

Assignment: Do the baby names exercise

If you still need more practice, follow this tutorial for text cleaning. It will challenge you on various steps involved in data wrangling.

Step 4: Learn Scientific libraries in Python – NumPy, SciPy, Matplotlib and Pandas

This is where fun begins! Here is a brief introduction to various libraries. Let’s start practicing some common operations.

Practice the NumPy tutorial thoroughly, especially NumPy arrays. This will form a good foundation for things to come.

Next, look at the SciPy tutorials. Go through the introduction and the basics and do the remaining ones basis your needs.

If you guessed Matplotlib tutorials next, you are wrong! They are too comprehensive for our need here. Instead look at this ipython notebook till Line 68 (i.e. till animations)

Finally, let us look at Pandas. Pandas provide DataFrame functionality (like R) for Python. This is also where you should spend good time practicing. Pandas would become the most effective tool for all mid-size data analysis. Start with a short introduction, 10 minutes to pandas. Then move on to a more detailed tutorial on pandas.

You can also look at Exploratory Data Analysis with Pandas and Data munging with Pandas

Additional Resources:

If you need a book on Pandas and NumPy, “Python for Data Analysis by Wes McKinney”

There are a lot of tutorials as part of Pandas documentation. You can have a look at them here

Assignment: Solve this assignment from CS109 course from Harvard.

Step 5: Effective Data Visualization

Go through this lecture form CS109. You can ignore the initial 2 minutes, but what follows after that is awesome! Follow this lecture up with this assignment.

Step 6: Learn Scikit-learn and Machine Learning

Now, we come to the meat of this entire process. Scikit-learn is the most useful library on python for machine learning. Here is a brief overview of the library. Go through lecture 10 to lecture 18 from CS109 course from Harvard. You will go through an overview of machine learning, Supervised learning algorithms like regressions, decision trees, ensemble modeling and non-supervised learning algorithms like clustering. Follow individual lectures with the assignments from those lectures.

You should also check out the ‘Introduction to Data Science‘ course to give yourself a big boost in your quest to land a data scientist role.

Additional Resources:

If there is one book, you must read, it is Programming Collective Intelligence – a classic, but still one of the best books on the subject.

Additionally, you can also follow one of the best courses on Machine Learning course from Yaser Abu-Mostafa. If you need more lucid explanation for the techniques, you can opt for the Machine learning course from Andrew Ng and follow the exercises on Python.

Tutorials on Scikit learn

Step 7: Practice, practice and Practice

Congratulations, you made it!

You now have all what you need in technical skills. It is a matter of practice and what better place to practice than compete with fellow Data Scientists on the DataHack platform. Go, dive into one of the live competitions currently running on DataHack and Kaggle and give all what you have learnt a try!

Step 8: Deep Learning

Now that you have learnt most of machine learning techniques, it is time to give Deep Learning a shot. There is a good chance that you already know what is Deep Learning, but if you still need a brief intro, here it is.

I am myself new to deep learning, so please take these suggestions with a pinch of salt. The most comprehensive resource is chúng tôi You will find everything here – lectures, datasets, challenges, tutorials. You can also try the course from Geoff Hinton a try in a bid to understand the basics of Neural Networks.

Get Started with Python: A Complete Tutorial To Learn Data Science with Python From Scratch

P.S. In case you need to use Big Data libraries, give Pydoop and PyMongo a try. They are not included here as Big Data learning path is an entire topic in itself.

Google Colab For Machine Learning And Deep Learning

“Memory Error” – that all too familiar dreaded message in Jupyter notebooks when we try to execute a machine learning or deep learning algorithm on a large dataset. Most of us do not have access to unlimited computational power on our machines. And let’s face it, it costs an arm and a leg to get a decent GPU from existing cloud providers. So how do we build large deep learning models without burning a hole in our pockets? Step up – Google Colab!

It’s an incredible online browser-based platform that allows us to train our models on machines for free! Sounds too good to be true, but thanks to Google, we can now work with large datasets, build complex models, and even share our work seamlessly with others. That’s the power of Google Colab.

What is Google Colab?

Google Colaboratory is a free online cloud-based Jupyter notebook environment that allows us to train our machine learning and deep learning models on CPUs, GPUs, and TPUs.

Here’s what I truly love about Colab. It does not matter which computer you have, what it’s configuration is, and how ancient it might be. You can still use Google Colab! All you need is a Google account and a web browser. And here’s the cherry on top – you get access to GPUs like Tesla K80 and even a TPU, for free!

TPUs are much more expensive than a GPU, and you can use it for free on Colab. It’s worth repeating again and again – it’s an offering like no other.

Are you are still using that same old Jupyter notebook on your system for training models? Trust me, you’re going to love Google Colab.

What is a Notebook in Google Colab? Google Colab Features

Colab provides users free access to GPUs and TPUs, which can significantly speed up the training and inference of machine learning and deep learning models.

Colab’s interface is web-based, so installing any software on your local machine is unnecessary. The interface is also intuitive and user-friendly, making it easy to get started with coding.

Colab allows multiple users to work on the same notebook simultaneously, making collaborating with team members easy. Colab also integrates with other Google services, such as Google Drive and GitHub, making it easy to share your work.

Colab notebooks support markdown, which allows you to include formatted text, equations, and images alongside your code. This makes it easier to document your work and communicate your ideas.

Colab comes pre-installed with many popular libraries and tools for machine learning and deep learning, such as TensorFlow and PyTorch. This saves time and eliminates the need to manually install and configure these tools.

GPUs and TPUs on Google Colab

Ask anyone who uses Colab why they love it. The answer is unanimous – the availability of free GPUs and TPUs. Training models, especially deep learning ones, takes numerous hours on a CPU. We’ve all faced this issue on our local machines. GPUs and TPUs, on the other hand, can train these models in a matter of minutes or seconds.

If you still need a reason to work with GPUs, check out this excellent explanation by Faizan Shaikh.

It gives you a decent GPU for free, which you can continuously run for 12 hours. For most data science folks, this is sufficient to meet their computation needs. Especially if you are a beginner, then I would highly recommend you start using Google Colab.

Google Colab gives us three types of runtime for our notebooks:


GPUs, and


As I mentioned, Colab gives us 12 hours of continuous execution time. After that, the whole virtual machine is cleared and we have to start again. We can run multiple CPU, GPU, and TPU instances simultaneously, but our resources are shared between these instances.

Let’s take a look at the specifications of different runtimes offered by Google Colab:

It will cost you A LOT to buy a GPU or TPU from the market. Why not save that money and use Google Colab from the comfort of your own machine?

How to Use Google Colab?

You can go to Google Colab using this link. This is the screen you’ll get when you open Colab:

You can also import your notebook from Google Drive or GitHub, but they require an authentication process.

Google Colab Runtimes – Choosing the GPU or TPU Option

The ability to choose different types of runtimes is what makes Colab so popular and powerful. Here are the steps to change the runtime of your notebook:

Step 2: Here you can change the runtime according to your need:

A wise man once said, “With great power comes great responsibility.” I implore you to shut down your notebook after you have completed your work so that others can use these resources because various users share them. You can terminate your notebook like this:

Using Terminal Commands on Google Colab

You can use the Colab cell for running terminal commands. Most of the popular libraries come installed by default on Google Colab. Yes, Python libraries like Pandas, NumPy, scikit-learn are all pre-installed.

If you want to run a different Python library, you can always install it inside your Colab notebook like this:

!pip install 


Pretty easy, right? Everything is similar to how it works in a regular terminal. We just you have to put an exclamation(!) before writing each command like:




Cloning Repositories in Google Colab

You can also clone a Git repo inside Google Colaboratory. Just go to your GitHub repository and copy the clone link of the repository:

Then, simply run:

And there you go!

Uploading Files and Datasets

Here’s a must-know aspect for any data scientist. The ability to import your dataset into Colab is the first step in your data analysis journey.

The most basic approach is to upload your dataset to Colab directly:

You can also upload your dataset to any other platform and access it using its link. I tend to go with the second approach more often than not (when feasible).

Saving Your Notebook

All the notebooks on Colab are stored on your Google Drive. The best thing about Colab is that your notebook is automatically saved after a certain time period and you don’t lose your progress.

If you want, you can export and save your notebook in both *.py and *.ipynb formats:

Not just that, you can also save a copy of your notebook directly on GitHub, or you can create a GitHub Gist:

I love the variety of options we get.

Exporting Data/Files from Google Colab

You can export your files directly to Google Drive, or you can export it to the VM instance and download it by yourself:

Exporting directly to the Drive is a better option when you have bigger files or more than one file. You’ll pick up these nuances as you work on bigger projects in Colab.

Sharing Your Notebook

Google Colab also gives us an easy way of sharing our work with others. This is one of the best things about Colab:

What’s Next?

Google Colab now also provides a paid platform called Google Colab Pro, priced at $9.99 a month. In this plan, you can get the Tesla T4 or Tesla P100 GPU, and an option of selecting an instance with a high RAM of around 27 GB. Also, your maximum computation time is doubled from 12 hours to 24 hours. How cool is that?

You can consider this plan if you need high computation power because it is still quite cheap when compared to other cloud GPU providers like AWS, Azure, and even GCP.


If you’re new to the world of Deep Learning, I have some excellent resources to help you get started in a comprehensive and structured manner:


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