-
Investing In AI? Here Is What To Consider
Everything you need to know about investing in AI initiatives.
-
5 Free Books to Help You Master Python
From the basics of Python to clean architecture and more, here are five free books to level up your Python skills.
-
Deploying Your First Machine Learning Model
With just 3 simple steps, you can build & deploy a glass classification model faster than you can say...glass classification model!
-
KDnuggets News, September 27: ChatGPT Projects Cheat Sheet • Introduction to PyTorch & Lightning AI
10 ChatGPT Projects Cheat Sheet • Introduction to Deep Learning Libraries: PyTorch and Lightning AI • Top 5 Free Alternatives to GPT-4 • Machine Learning Evaluation Metrics: Theory and Overview • Kick Ass Midjourney Prompts with Poe
-
Generative Agent Research Papers You Should Read
Research paper in the exciting field that you don’t want to miss.
-
Introduction to Natural Language Processing
An overview of Natural Language Processing (NLP) and its applications.
-
The Data Maturity Pyramid: From Reporting to a Proactive Intelligent Data Platform
This article describes the data maturity pyramid and its various levels, from simple reporting to AI-ready data platforms. It emphasizes the importance of data for business and illustrates how data platforms serve as the driving force behind AI.
-
Using SQL to Understand Data Science Career Trends
Reveal the Secrets of the Data Science Job Market with SQL.
-
Building a Convolutional Neural Network with PyTorch
This blog post provides a tutorial on constructing a convolutional neural network for image classification in PyTorch, leveraging convolutional and pooling layers for feature extraction as well as fully-connected layers for prediction.
-
Gartner Hype Cycle for AI in 2023
Let’s dive into how the AI landscape has rapidly evolved with the advent of new Generative AI technologies.
-
-
-
Top 5 Free Alternatives to GPT-4
Think GPT-4 is a big deal? These Generative AI newbies are already stealing the show!
-
Effective Small Language Models: Microsoft’s 1.3 Billion Parameter phi-1.5
Learn about Microsoft’s 1.3 billion parameter model that has outperformed Llama 2’s 7-billion parameters model on several benchmarks.
-
Optimizing Data Storage: Exploring Data Types and Normalization in SQL
Learn about the data types and normalization techniques in SQL, which will be very helpful for optimizing your data storage.
-
Exploring Neural Networks
Unlocking the power of AI: a suide to neural networks and their applications.
-
How Generative AI is disrupting data practices
The release of Language Learning Model (LLM) ChatGPT by OpenAI in November of last year opened the floodgates leading to alternatives including Google Bard and Microsoft Bing and Gen AI has proved massively disruptive, with businesses seeking to explore how they can apply the technology.
-
Traditional AI vs Generative AI
Helping beginners understand the difference between traditional AI and generative AI.
-
Your Features Are Important? It Doesn’t Mean They Are Good
“Feature Importance” is not enough. You also need to look at “Error Contribution” if you want to know which features are beneficial for your model.
-
Machine Learning Evaluation Metrics: Theory and Overview
High-level exploration of evaluation metrics in machine learning and their importance.
-
Kick Ass Midjourney Prompts with Poe
Try out this Poe chatbot to refine your Midjourney prompts, and (hopefully?) get some kick ass image generation results!
-
Feature Store Summit 2023: Practical Strategies for Deploying ML Models in Production Environments
On October 11th, 2023 the Feature Store Summit will bring together leading ML companies, such as Uber, WeChat and more, for in-depth discussions about data and AI.
-
10 ChatGPT Projects Cheat Sheet
KDnuggets' latest cheat sheet covers 10 curated hands-on projects to boost data science workflows with ChatGPT across ML, NLP, and full stack dev, including links to full project details.
-
KDnuggets News, September 20: Python in Excel: This Will Change Data Science Forever • New KDnuggets Survey!
Python in Excel: This Will Change Data Science Forever • KDnuggets Survey: Benchmark With Your Peers On Data Science Spend & Trends 2023 H2 • 5 Best AI Tools For Maximizing Productivity • And much more!
-
Fine Tuning LLAMAv2 with QLora on Google Colab for Free
Learn how to fine-tune one of the most influential open-source models for free on Google Colab.
-
Hands-On with Unsupervised Learning: K-Means Clustering
This tutorial provides hands-on experience with the key concepts and implementation of K-Means clustering, a popular unsupervised learning algorithm, for customer segmentation and targeted advertising applications.
-
Don’t Miss Out! Enroll in FREE Courses Before 2023 Ends
Complete the last quarter of the year and improve your skills to get you kickstarted for 2024’s self-development plan with these FREE courses.
-
Unveiling Neural Magic: A Dive into Activation Functions
Cracking the code of activation functions: Demystifying their purpose, selection, and timing.
-
Unveiling Unsupervised Learning
Explore the unsupervised learning paradigm. Familiarize yourself with the key concepts, techniques, and popular unsupervised learning algorithms.
-
-
Python in Excel: This Will Change Data Science Forever
You can now run Python code in Excel to analyze data, build machine learning models, and create visualizations.
-
How to Identify Missing Data in Time-Series Datasets
Using exploratory data analysis to wnderstand missing data gaps.
-
Hands-On with Supervised Learning: Linear Regression
If you're looking for a hands-on experience with a detailed yet beginner-friendly tutorial on implementing Linear Regression using Scikit-learn, you're in for an engaging journey.
-
Understanding Supervised Learning: Theory and Overview
This article covers a high-level overview of popular supervised learning algorithms and is curated specially for beginners.
-
Getting Started with Scikit-learn in 5 Steps
This tutorial offers a comprehensive hands-on walkthrough of machine learning with Scikit-learn. Readers will learn key concepts and techniques including data preprocessing, model training and evaluation, hyperparameter tuning, and compiling ensemble models for enhanced performance.
-
5 Amazing & Free LLMs Playgrounds You Need to Try in 2023
Explore the top 5 user-friendly platforms that provide free access to large language models, enabling you to experience the latest AI models firsthand.
-
How to Get a Job in Data Science as a Student
Learn how to increase your possibilities of getting your first data science job while you are a student.
-
Demystifying Machine Learning
This article is intended to familiarize you with the essence of Machine learning, the basic concepts, and the high-level machine learning process.
-
Pursue A Master’s In Data Science With The 3rd Best Online Program
Flexible schedules designed for working professionals. Enrolling now for October 2023 and March 2024.
-
The 5 Best AI Tools For Maximizing Productivity
KDnuggets reviews a diverse set of 5 AI tools to help maximize your productivity. Have a look and see what our recommendations include.
-
Linear Regression from Scratch with NumPy
Mastering the Basics of Linear Regression and Fundamentals of Gradient Descent and Loss Minimization.
-
Hypothesis Testing and A/B Testing
The pillars of data-driven decisions.
-
Scikit-learn for Machine Learning Cheat Sheet
The latest KDnuggets exclusive cheatsheet covers the essentials of machine learning with Scikit-learn.
-
KDnuggets News, September 13: Getting Started with SQL in 5 Steps • Introduction to Databases in Data Science
Getting Started with SQL in 5 Steps • Introduction to Databases in Data Science • Time 100 AI: The Most Influential?
-
Closed Source VS Open Source Image Annotation
This blog strikes a comparison between open-source and closed-source image annotation tools and how it makes the life of AI model developers easy and convenient.
-
KDnuggets Survey: Benchmark With Your Peers On Data Science Spend & Trends 2023 H2
KDnuggets, along with The All Things Insights Survey Committee and its partners, have created a Spend & Trends survey to provide you and your colleagues in our community with much needed benchmarking information on mindset and focus trends as well as budget and technology spend.
-
Applying Descriptive and Inferential Statistics in Python
As you progress in your data science journey, here are the elementary statistics you should know.
-
Everything you Need to Become a SAS Certified Data Scientist
With a shortage of talent and an abundance of opportunity, there’s never been a better time to launch or advance your data science career with the SAS Academy for Data Science. Read on to find out everything you need to become a SAS Certified Data Scientist.
-
Understanding Machine Learning Algorithms: An In-Depth Overview
Understanding Machine Learning: Exposing the Tasks, Algorithms, and Selecting the Best Model.
-
A Comprehensive Guide to Pinecone Vector Databases
This blog discusses vector databases, specifically pinecone vector databases. A vector database is a type of database that stores data as mathematical vectors, which represent features or attributes. These vectors have multiple dimensions, capturing complex data relationships. This allows for efficient similarity and distance calculations, making it useful for tasks like machine learning, data analysis, and recommendation systems.
-
Statistics in Data Science: Theory and Overview
High-level exploration of the role of statistics in data science.
-
Profiling Python Code Using timeit and cProfile
An introductory guide to profiling Python code using the timeit and cProfile modules.
-
10 Math Concepts for Programmers
The not so secret behind becoming a proficient programmer - Math & it’s top 10 concepts.
-
From Zero to Hero: Create Your First ML Model with PyTorch
Learn the PyTorch basics by building a classification model from scratch.
-
Working with Big Data: Tools and Techniques
Where do you start in a field as vast as big data? Which tools and techniques to use? We explore this and talk about the most common tools in big data.
-
Data Management Principles for Data Science
Back to Basics: Understanding key data management principles that data scientists should know.
-
Getting Started with SQL in 5 Steps
This comprehensive SQL tutorial covers everything from setting up your SQL environment to mastering advanced concepts like joins, subqueries, and optimizing query performance. With step-by-step examples, this guide is perfect for beginners looking to enhance their data management skills.
-
Meet MetaGPT: The ChatGPT-Powered AI Assistant That Turns Text Into Web Apps
This revolutionary AI tool lets you create no-code web applications in just seconds!
-
Leveraging Geospatial Data in Python with GeoPandas
A comprehensive introduction to geospatial data analysis with GeoPandas.
-
Introduction to Databases in Data Science
Understand the relevance of databases in data science. Also learn the fundamentals of relational databases, NoSQL database categories, and more.
-
Time 100 AI: The Most Influential?
Time Magazine just released its Time 100 AI list, spotlighting 100 key figures in AI across categories such as leaders and innovators. The list aims to highlight the human effort behind AI advancements. The list serves as a snapshot of how mainstream media views the AI landscape, offering a mix of familiar and new names in the field.
-
Building Microservice for Multi-Chat Backends Using Llama and ChatGPT
As LLMs continue to evolve, integrating multiple models or switching between them has become increasingly challenging. This article suggests a Microservice approach to separate model integration from business applications and simplify the process.
-
-
Creating Visuals with Matplotlib and Seaborn
Learn the basic Python package visualization for your work.
-
KDnuggets News, September 6: Happy 30th Anniversary KDnuggets! • Getting Started with Python Data Structures in 5 Steps
Happy 30th Anniversary KDnuggets! • Getting Started with Python Data Structures in 5 Steps • KDnuggets 30th Anniversary Interview with Founder Gregory Piatetsky-Shapiro
-
Python Control Flow Cheat Sheet
The latest KDnuggets cheatsheet focuses on Python flow control, how we manage the execution order of statements in a program. Check it out for a quick start.
-
Elevate Math Efficiency: Navigating Numpy Array Operations
Uncover the art of optimizing math for data analysis and how NumPy transforms complex calculations into simple, efficient spells, making math a breeze.
-
Data Visualization: Theory and Techniques
Unlocking the secrets of how to observe our data-driven world.
-
5 Portfolio Projects for Final Year Data Science Students
From cleaning data to wowing recruiters - this blog shares 5 killer data science projects to launch your data science career and get hired!
-
Building a Formula 1 Streaming Data Pipeline With Kafka and Risingwave
Build a streaming data pipeline using Formula 1 data, Python, Kafka, RisingWave as the streaming database, and visualize all the real-time data in Grafana.
-
Data Cleaning with Pandas
This step-by-step tutorial is for beginners to guide them through the process of data cleaning and preprocessing using the powerful Pandas library.
-
Want to Become a Data Scientist? Part 1: 10 Hard Skills You Need
A quick 10-step hard skill guide on what you need to become a Data Scientist.
-
ReAct, Reasoning and Acting augments LLMs with Tools!
Blending reasoning with action, AI takes a bold new step toward replicating human intelligence.
-
WavJourney: A Journey into the World of Audio Storyline Generation
From Prompt to Power: Unleashing Stories and Audio with a Single Spark!
-
Introduction to Numpy and Pandas
A primer on using Numpy and Pandas for numerical computation and data manipulation in Python.
|
