This article features 19 data science projects for beginners, categorized into 7 full project tutorials, 5 places to come up with your own data science projects using data, and 7 skills-based data science projects.
Learn best practice guidelines for building AI solutions responsibly. Join AI experts from Microsoft and BCG at Put Responsible AI into Practice—a free Azure digital event on December 7.
In this article, we are going to compare the sentiment extraction performance between Sentiment Analysis engines and Custom Text classification engines. The idea is to show pros and cons of these two types of engines on a concrete dataset.
After 28+ years of publishing and editing KDnuggets, I am retiring and transitioning KDnuggets to Matthew Mayo, who will become the new editor-in-chief. I want to share with you my story of KDnuggets and highlight some of the useful nuggets of experience I learned along this amazing journey.
As a result of the efforts outlined in this article, we confirmed that clustering through crowdsourcing is indeed possible and works impressively well.
Take a moment to participate in the latest KDnuggets poll and let the community know what percentage of your machine learning models have been deployed.
The hiring run for data scientists continues along at a strong clip around the world. But, there are other emerging roles that are demonstrating key value to organizations that you should consider based on your existing or desired skill sets.
Also: 19 Data Science Project Ideas for Beginners; How to Build a Knowledge Graph with Neo4J and Transformers; Data Scientists: How to Sell Your Project and Yourself; Where NLP is heading
Find out the major differences between a Data Analyst and a Data Scientist, and read the author's pointers on what they would recommend you to do if you wish to make that transition from Data Analyst to Data Scientist.
Until November 29th, you can join over 1.5 million students around the globe and gain the skills of successful data science professionals with unlimited annual access to the 365 Data Science Program at 72% OFF. Read on to learn more!
Maintaining a centralized data repository can simplify your business intelligence initiatives. Here are four data integration tools that can make data more valuable for modern enterprises.
Companies are racing to use AI, but despite its vast potential, most AI projects fail. Examining and resolving operational issues upfront can help AI initiatives reach their full potential.
Also: How I Redesigned over 100 ETL into ELT Data Pipelines; Where NLP is heading; Don’t Waste Time Building Your Data Science Network; Data Scientists: How to Sell Your Project and Yourself
Sure, we all make mistakes -- which can be a bit more painful when we are trying to get hired -- so check out these typical errors applicants make while answering SQL questions during data science interviews.
PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.
This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The next article in the series is about parallelizing for loops, and other embarrassingly parallel operations with dask.delayed.
The terms ‘data science’ and ‘machine learning’ are often used interchangeably. But while they are related, there are some glaring differences, so let’s take a look at the differences between the two disciplines, specifically as it relates to programming.
To guide you in becoming a data-driven organization, AWS Data Exchange has created a new eBook, 101 Ways to Use Third-Party Data to Make Smarter Decisions. Learn how to transform the ‘currency’ of data into actionable business insights.
Natural language processing research and applications are moving forward rapidly. Several trends have emerged on this progress, and point to a future of more exciting possibilities and interesting opportunities in the field.
With the customer at its heart, modern augmented BI platforms no longer require scripting/coding skills or the knowledge to build the back-end data models, empowering even laymen to harness the power of raw data. As a user, here are the top AI capabilities that you need to look for in BI software.
By Zoho Analytics on Nov 17, 2021 in AI, BI, Platform
We describe types of recommender systems, more specifically, algorithms and methods for content-based systems, collaborative filtering, and hybrid systems.
Data is the lifeblood of any successful machine learning model, and machine translation models are no exception. Without relevant and properly labelled data, even the most sophisticated model will be unable to achieve reliable results.
The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.
The October blogs that won KDnuggets Rewards include: How I Tripled My Income With Data Science in 18 Months; What Google Recommends You do Before Taking Their Machine Learning or Data Science Course; How to Build Strong Data Science Portfolio as a Beginner; Data Scientist vs Data Engineer Salary.
NVIDIA, the pioneer in the GPU technologies and deep learning revolution, has come up with an excellent catalog of specialized containers that they call NGC Collections. In this article, we explore their basic usage and some variations.
Also: Data Scientist Career Path from Novice to First Job; Design Patterns for Machine Learning Pipelines; What Google Recommends You do Before Taking Their Machine Learning or Data Science Course; Salary Breakdown of the Top Data Science Jobs
The PyTorch Deep Learning framework has a C++ API for use on mobile platforms. This article shows an end-to-end demo of how to write a simple C++ application with Deep Learning capabilities using the PyTorch C++ API such that the same code can be built for use on mobile platforms (both Android and iOS).
Also: How to Build Strong Data Science Portfolio as a Beginner; Data Science Portfolio Project Ideas That Can Get You Hired (Or Not); Exclusive: OpenAI summarizes KDnuggets
Join Caserta and fellow data and analytics leaders, Nov 17, as they help guide you on how, what and why you need to transform your data ecosystem to cloud-based modern analytics.
The notion of self-service BI tools caught an expectation that they could provide a magic formula for easily helping everyone understand all the data. But, such an end-result isn't occurring in practice. To identify a better approach, we need to take a step back and determine what problem is actually trying to be solved.
Join this webinar, Nov 11, to learn how leveraging third-party financial services data can facilitate faster, intelligence-based decision-making that propels your company's business outcomes and digital transformation.
After a pause, we will be resuming KDnuggets Top Blog Rewards Program, starting with blogs published on KDnuggets in December. The program will be bigger, with $3,000 (USD) divided among top 8 most viewed guest blogs. Original blogs rewarded at the rate of 3X of reposts. Submit your original blog to KDnuggets first !
By Gregory Piatetsky on Nov 9, 2021 in Blog Rewards
Now, SAS Analytics Pro includes a new option for containerized cloud-native deployment. This makes SAS Analytics Pro a perfect entry point into SAS Viya.
Beginners in the field can often have many misconceptions about machine learning that sometimes can be a make-it-or-break-it moment for the individual switching careers or starting fresh. This article clearly describes the ground truth realities about learning new ML skills and eventually working professionally as a machine learning engineer.
In this article we are discussing that HDF5 is one of the most popular and reliable formats for non-tabular, numerical data. But this format is not optimized for deep learning work. This article suggests what kind of ML native data format should be to truly serve the needs of modern data scientists.
Also: Design Patterns for Machine Learning Pipelines; Data Scientist Career Path from Novice to First Job; Salary Breakdown of the Top Data Science Jobs; ORDAINED: The Python Project Template
With a lot of excitement and research around NLP, there are growing opportunities to apply these technologies to real-world scenarios. It's not trivial to become familiar with NLP and these open-source data sets can help you increase your skills.
There remain critical challenges in machine learning that, if left resolved, could lead to unintended consequences and unsafe use of AI in the future. As an important and active area of research, roadmaps are being developed to help guide continued ML research and use toward meaningful and robust applications.
Toloka is a crowdsourced data labeling platform that handles data collection and annotation projects for machine learning at any scale. In this Nov 11 Live Demo, Learn how to get reliable training data for machine learning.
Productizing AI is an infrastructure orchestration problem. In planning your solution design, you should use continuous monitoring, retraining, and feedback to ensure stability and sustainability.
This article is a brief summary of our observations on some common client misperceptions with respect to recent developments in NLP, especially the use of large-scale models and datasets.
At our upcoming event this November 16th-18th in San Francisco, ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on machine learning topics, deep learning, NLP, MLOps, and so on. You can register now for 20% off all ticket types, or register for a free AI Expo Pass to see what some big names in AI are doing now.
If you are beginning your data science journey, then you must be prepared to plan it out as a step-by-step process that will guide you from being a total newbie to getting your first job as a data scientist. These tips and educational resources should be useful for you and add confidence as you take that first big step.
Recently I decided to take the time to better understand the Python packaging ecosystem and create a project boilerplate template as an improvement over copying a directory tree and doing find and replace.
ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.
Also: What Google Recommends You do Before Taking Their Machine Learning or Data Science Course; Learn To Reproduce Papers: Beginner’s Guide; 365 Data Science courses free until 18 November; A Guide to 14 Different Data Science Jobs
In this tutorial we will be diving deeper into two additional tools you should be using: TorchMetrics and Lightning Flash. TorchMetrics unsurprisingly provides a modular approach to define and track useful metrics across batches and devices, while Lightning Flash offers a suite of functionality facilitating more efficient transfer learning and data handling, and a recipe book of state-of-the-art approaches to typical deep learning problems.
Data that varies in time can offer powerful applications and use cases for data scientists to analyze. This overview considers the top techniques you can learn to understand and gain insight from time-series data.
The modern data stack narrative is largely dominated by analytics engineering. Where does that leave data engineers? Discover the difference between the MDS for data engineers & analytics engineers.
Why Machine Learning Engineers are Replacing Data Scientists
Most Common SQL Mistakes on Data Science Interviews
Where NLP is heading
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