#machine learning tools

[newaitechnology]

What Are the Best Free AI Tools for Developers in 2025?

In 2025, the rise of artificial intelligence continues to reshape how developers write, test, and deploy code. Whether you're building apps, creating AI models, or automating tasks, there’s a wide range of free AI tools for developers that can dramatically enhance your productivity. These tools are designed to reduce repetitive work, assist with code generation, and streamline your entire development workflow.

From intelligent code editors to open-source AI tools and cloud-based platforms, developers now have access to powerful solutions without needing to spend a dime. You’ll find tools that help with version control, documentation, real-time error detection, and even project planning—many of which are created and supported by thriving developer communities.

This guide covers a variety of free machine learning tools that allow you to train and deploy models easily, even if you’re just starting out. We’ll explore AI development platforms that simplify model building and deployment, along with tools that offer pre-built templates, automation workflows, and intelligent suggestions. Whether you're working in Python, JavaScript, or any other language, there’s something here for every developer.

You’ll also discover the best AI APIs free to integrate into your applications—from NLP and computer vision to sentiment analysis and chatbot development. Plus, we highlight some of the most popular free AI libraries for coders, which make it easy to add smart functionality to your apps without building everything from scratch.

No matter your experience level, these free tools can help you stay ahead in the fast-changing tech landscape. Dive into our expert-curated list of the best free AI tools for developers in 2025 and start building faster, smarter, and more efficiently with the power of AI.

[2ribu]

Wawasan Pelanggan Berbasis AI untuk Pemasaran yang Tepat Sasaran

Di era digital yang semakin kompleks, memahami pelanggan menjadi kunci keberhasilan pemasaran. Perusahaan yang dapat mengidentifikasi kebutuhan, preferensi, dan perilaku pelanggan memiliki keunggulan kompetitif yang signifikan. Namun, dengan jumlah data yang terus bertambah, menganalisis informasi secara manual menjadi tantangan besar. Di sinilah kecerdasan buatan (AI) memainkan peran penting. AI…

[greatonlinetrainingsposts]

Machine Learning in SAS: An Overview of Techniques and Real-World Applications

Machine learning is transforming industries around the world, and SAS programming stands out as a powerful tool for implementing machine learning techniques, particularly for enterprises focused on large-scale data and analytics-driven insights. SAS has been a leader in statistical analysis for decades, and its continued evolution makes it an ideal platform for businesses looking to leverage machine learning capabilities effectively.

In this article, we’ll explore some core machine learning techniques that SAS programming supports, the unique advantages SAS brings to machine learning, and several real-world applications that showcase its versatility across industries like finance, healthcare, and retail.

Why Use SAS Programming for Machine Learning?

SAS programming is renowned for its comprehensive suite of data analytics tools and extensive support for advanced statistical methods, making it particularly useful for machine learning. For businesses that prioritize data security, large-scale data processing, and consistent compliance, SAS offers a trusted platform with robust machine learning algorithms.

The advantage of using SAS programming for machine learning lies in its combination of analytical power, ease of integration with other data systems, and compatibility with both open-source and proprietary tools. SAS supports Python and R integration, allowing data scientists to leverage additional libraries while benefiting from SAS’s data management strengths.

Key Machine Learning Techniques in SAS

SAS programming provides an array of machine learning techniques that can support predictive modeling, clustering, natural language processing, and more. Here’s a look at some of the primary techniques you can use within SAS programming for machine learning:

1. Supervised Learning (Predictive Modeling)

- Overview: Supervised learning involves using labeled data to train models that can make predictions or classifications. In SAS programming, supervised learning algorithms are robustly supported, allowing users to build and deploy predictive models efficiently.

- Common Algorithms: Linear regression, decision trees, support vector machines (SVM), and neural networks are some popular options.

- Application: Predicting customer churn, credit scoring, and demand forecasting are common use cases that utilize supervised learning in SAS programming.

2. Unsupervised Learning (Clustering and Association Analysis)

- Overview: Unsupervised learning deals with data that lacks labeled responses, which makes it ideal for discovering hidden patterns. Clustering and association analysis are often used for market segmentation and recommendations.

- Common Techniques: k-means clustering, hierarchical clustering, and association rule mining are commonly applied within SAS programming’s unsupervised learning capabilities.

- Application: Retailers frequently use clustering to segment customers based on purchasing behavior, while financial firms use association analysis to identify patterns in transactions.

3. Natural Language Processing (NLP)

- Overview: NLP is essential for analyzing unstructured text data, and SAS programming provides a set of tools for handling tasks like sentiment analysis, topic modeling, and text summarization.

- Common Techniques: Sentiment analysis, text parsing, and latent Dirichlet allocation (LDA) are NLP techniques available in SAS programming.

- Application: SAS programming can analyze customer feedback, social media content, and surveys to help businesses understand sentiment and emerging trends.

4. Time Series Forecasting

- Overview: Time series forecasting is used to predict future values based on historical data patterns, making it invaluable for applications where timing and trend analysis are crucial.

- Common Techniques: ARIMA (AutoRegressive Integrated Moving Average), exponential smoothing, and seasonal decomposition are available in SAS programming for time series analysis.

- Application: Time series forecasting is highly beneficial in inventory management, economic forecasting, and sales predictions.

5. Deep Learning

- Overview: Deep learning algorithms like neural networks and convolutional neural networks (CNNs) allow for complex pattern recognition and are well-suited for tasks involving image and audio data.

- Common Techniques: Multilayer perceptrons, CNNs, and recurrent neural networks (RNNs) are supported in SAS programming for deep learning applications.

- Application: Deep learning models can be applied in fraud detection, image recognition in medical diagnostics, and product recommendation systems.

Real-World Applications of Machine Learning in SAS Programming

SAS programming is applied across various industries for machine learning-driven solutions, helping companies make data-informed decisions and automate critical business processes.

1. Finance: Credit Scoring and Risk Management

- Financial institutions rely on machine learning for predictive analytics, particularly in credit scoring and fraud detection. SAS programming enables these organizations to implement complex models that assess credit risk based on multiple factors like transaction history and financial behavior.

Example: By using logistic regression and decision tree models, a bank can predict the likelihood of loan default, allowing for better risk management.

2. Healthcare: Predictive Diagnostics and Patient Management

- In healthcare, SAS programming helps providers utilize patient data for predictive diagnostics, treatment personalization, and operational efficiency. With supervised learning, healthcare professionals can assess the probability of disease occurrence and predict patient outcomes.

Example: SAS programming can be used to develop predictive models for patient readmission rates, aiding hospitals in proactive patient care and resource planning.

3. Retail: Customer Segmentation and Personalized Marketing

- Machine learning in SAS programming supports customer segmentation, which helps retailers understand consumer behavior and tailor marketing strategies. SAS’s clustering and association analysis capabilities allow for precise segmentation based on purchasing patterns and preferences.

Example: Retailers can target segmented customer groups with personalized product recommendations, improving engagement and sales.

4. Manufacturing: Predictive Maintenance and Quality Control

- SAS programming’s time series forecasting and anomaly detection capabilities are highly valuable in manufacturing, where predictive maintenance can prevent equipment failures and minimize downtime.

Example: Manufacturing companies use SAS programming to predict machine failure by analyzing historical operational data, allowing for timely maintenance and reduced disruptions.

5. Telecommunications: Customer Churn Prediction

- Customer retention is a key focus for telecom companies. SAS programming’s predictive modeling capabilities allow telecom providers to identify customers at risk of churning and take preemptive measures.

Example: By using logistic regression models, telecom companies can predict churn likelihood and create retention campaigns for high-risk customers.

SAS Online Training for Machine Learning

For those looking to deepen their understanding of SAS programming and its machine learning capabilities, SAS online training offers comprehensive resources for learners at all levels. Whether you're starting from scratch or looking to enhance your skills, SAS online training programs provide access to expert-led courses and hands-on exercises. By enrolling in SAS programming tutorial sessions, you can gain in-depth knowledge about various machine learning techniques, algorithms, and real-world applications that are essential in the modern data landscape.

Additionally, for individuals seeking an extensive and structured learning experience, a SAS programming full course can guide you through everything from the basics of data analysis to advanced machine learning applications, preparing you for real-world challenges in data science and machine learning.

The Future of Machine Learning in SAS Programming

As SAS programming continues to evolve, its integration with open-source languages like Python and R enhances flexibility, making it an attractive platform for businesses that want to blend SAS’s capabilities with the vast libraries available in open-source environments. Moreover, SAS Viya, the cloud-enabled, open analytics platform, allows organizations to deploy models faster, scale machine learning applications, and enable cross-functional collaboration.

In addition to ongoing advancements, SAS has also been expanding its support for deep learning and neural networks, making it a powerful tool for tackling increasingly complex machine learning problems. With its robust data processing abilities and strong focus on enterprise security, SAS programming is well-positioned to support industries aiming to harness the full potential of machine learning.

Conclusion

Machine learning in SAS programming offers powerful techniques and a reliable platform for implementing predictive models, uncovering insights, and optimizing business processes across a variety of industries. From customer segmentation and churn prediction to predictive maintenance and patient management, SAS programming’s machine learning tools help organizations make data-driven decisions and gain a competitive edge. As technology and data demands continue to grow, SAS remains a trusted partner for machine learning applications, offering both stability and innovation for data-driven enterprises.

[polyxersystems]

Top 5 Machine Learning Tools for Software Development in 2024

Introduction

Machine learning has been widely used by various industries in 2023. The software development industry can take great advantage of machine learning in 2024 as well.

It has great potential to revolutionize various aspects of software development including task automation, boosting user experience, and easy software development and deployment.

Machine learning could be leveraged throughout the software development process to improve productivity in 2024.

Hence, this blog explores the best machine learning tools that software development industries can adopt for daily development tasks and significantly boost productivity.

But first, let’s discuss the pivotal role of machine learning in software development.

What Is Machine Learning?

Machine learning tools in software development help developers analyze large volumes of data and identify patterns to create more efficient, reliable, and user-friendly software.

In software development, machine learning tools are useful in streamlining workflows, automating manual processes, and generating valuable insights for informed decision-making.

The uses of machine learning tools in software development are wide and growing. Let’s explore some of its real-life examples to understand more.

Top 5 Real-World Machine Learning Examples

1. Recommendation systems

This is one of the most famous applications of machine learning. Product recommendations are commonly used and featured by businesses.

Using machine learning, developers can build software that can track user behavior to recognize patterns through their browsing history, previous purchases, and other shopping activities. This collection of data helps in predicting user preferences.

Various companies like Spotify and Netflix use machine learning algorithms to recommend music and shows to their customers based on their previous listening and viewing history.

2. Social media connections

Another most popular machine learning algorithm is the “people you may know” feature on social media platforms like Instagram, Facebook, LinkedIn, and X.

According to user contacts, comments, likes, or existing connections, this machine-learning algorithm suggests familiar accounts that users might want to follow or connect with.

Read More: Best Machine Learning Tools

[maodun]

shout out to machine learning tech (and all the human-input adjustment contributors) that's brought about the present developmental stage of machine translation, making the current global village 地球村 moment on rednote小红书 accessible in a way that would not have been possible years ago.

[whump-in-the-closet]

characters who’s identity revolves around their purpose, defined by something or someone else. By the prophecies, by their service; the lapdog, the weapon, the chosen one. And then there’s a moment of softness, a complete breach and utterly human— they cradle their head in their hands, they bend to pick up a cat and hold it tight, they slump against someone’s shoulder, completely trusting for the first time

thank you that’s it. exits stage and screams.

[cyle]

still confused how to make any of these LLMs useful to me.

while my daughter was napping, i downloaded lm studio and got a dozen of the most popular open source LLMs running on my PC, and they work great with very low latency, but i can't come up with anything to do with them but make boring toy scripts to do stupid shit.

as a test, i fed deepseek r1, llama 3.2, and mistral-small a big spreadsheet of data we've been collecting about my newborn daughter (all of this locally, not transmitting anything off my computer, because i don't want anybody with that data except, y'know, doctors) to see how it compared with several real doctors' advice and prognoses. all of the LLMs suggestions were between generically correct and hilariously wrong. alarmingly wrong in some cases, but usually ending with the suggestion to "consult a medical professional" -- yeah, duh. pretty much no better than old school unreliable WebMD.

then i tried doing some prompt engineering to punch up some of my writing, and everything ended up sounding like it was written by an LLM. i don't get why anybody wants this. i can tell that LLM feel, and i think a lot of people can now, given the horrible sales emails i get every day that sound like they were "punched up" by an LLM. it's got a stink to it. maybe we'll all get used to it; i bet most non-tech people have no clue.

i may write a small script to try to tag some of my blogs' posts for me, because i'm really bad at doing so, but i have very little faith in the open source vision LLMs' ability to classify images. it'll probably not work how i hope. that still feels like something you gotta pay for to get good results.

all of this keeps making me think of ffmpeg. a super cool, tiny, useful program that is very extensible and great at performing a certain task: transcoding media. it used to be horribly annoying to transcode media, and then ffmpeg came along and made it all stupidly simple overnight, but nobody noticed. there was no industry bubble around it.

LLMs feel like they're competing for a space that ubiquitous and useful that we'll take for granted today like ffmpeg. they just haven't fully grasped and appreciated that smallness yet. there isn't money to be made here.

[jaguarsoup]

Work in progress

||DO NOT FEED TO AI DO NOT USE FOR MACHINE LEARNING||

[tj-crochets]

Hey y'all! Do you have any recommendations for other plushie makers or designers with shops where I can buy their plushies?

[sometimesanequine]

:+: Good Morning :+:

I had no idea you read Sherlock Holmes! I haven’t gotten to the Hounds of Baskervilles yet, but I am anticipating it greatly. I did read A Study in Scarlet, the Sign of The Four and lots of other little stories.

I must say that I love Sherlock Holmes a ton. They have been the best mystery/detective stories I have ever read.

Anyway, hope you’re well and have a good day!

-Lu

im going out of order with my reading but it makes it more fun for me. i loved the hound of the baskervilles! i read a lot of classical literature when im out of spoons for much else its rather comforting. im reading a study in scarlet now. or will be when i go to read it before bed anyways.

hope you have a good day as well today and as always. enjoy your bay tobiano

[queen-mabs-revenge]

communist generative ai boosters on this website truly like

[daemonya]

Useful AI Websites

Remember when we thought robots would take over the world? Well, they kinda did, but instead of laser eyes and metal claws, they're armed with… tools? Yep, these days, AI is less "Terminator" and more "personal assistant on steroids" 🤖

Bot Making Assistant:

Ever wanted a personal minion but can't afford the banana budget?

Fantasy Name Generators

Rabid's Generators and RPG Resources

Random Original Character Generator

Perchance ― AI Character Description Generator

Perchance ― AI Chat & Roleplay and AI Chat w/image

Perchance ― AI Story Generator

Perchance ― AI Text Adventure and AI Adventure w/image

Perchance ― AI Hierarchical World Generator

AI Writing Assistant:

Don't blame me when your AI-assisted love letters start sounding suspiciously like robot poetry.

Cohesive

Dreamily

Fiction Fusion

Grammarly

Hemingway Editor

NovelAI

Perplexity

Phind

Quicktools

RambleFix: AI Note-taking & Writing Tool

RedQuill

TinyWow

ToolBaz

Tune Chat

WriterHand

You

AI Voice Generator:

Want to sound like Morgan Freeman without the years of smoking?

Murf AI

Dupdub AI

Vocal Removal

Adobe: Enhance Speech

Kits.AI (vocal removal, voice cloning)

AI Music Generator:

Who knows, you might accidentally create the next viral TikTok earworm and retire to a private island.

AI VOCALOID

Suno

Udio

AI Image Generator:

Whatever you need, these tools are your ticket to visual madness.

Bing Image Creator (SFW only) 👉🏻 how to prompt

Microsoft Designer (SFW only)

Maze Guru

Tensor.Art

CivitAI

PixAI

Runware

Text to Image

NeuralBlender

Leonardo.AI (and videos too)

Perchance AI Image Generator

Perchance AI Photo Generator (realistic)

AI Video Generator:

Video killed the radio star, and now AI is coming for Hollywood.

Hedra (make your characters sing)

VIGGLE (make your characters dance)

Dreamina (text/image to video)

Luma (text/image to video)

Vidu (text/image to video)

Genmo (text/image to video)

Haiper (text/image to video)

KLING (text/image to video)

Pika (text/image to video)

PixVerse (text/image to video)

invideo (text to video)

Fliki (text to video)

AKOOL (deepfake, face swap, talking photo)

D-ID (make live, speaking portraits)

Runway (prompt to video)

Creatify (create AI video ads)

Adobe: Animate from Audio

AI Image and Video Editor:

These magical tools are here to save your digital bacon!

123apps (edit, convert, create video, audio, PDF)

3D Book Cover Creator (book cover mockups)

Color Picker (from image)

Capcut AI Tools (upscale video)

Upscale.media (upscale image)

removal AI (image background remover)

Photopea (advanced image editor)

[spearxwind]

i think also a huge part of why artists majorly refuse machine-learning (bc that’s what it is, i refuse to call it ai bc it’s inaccurate and gives tech bros too much credit) is that the people currently championing and developing those tools actively want it to replace artists. They loudly and proudly hate the arts and want every creative professional put out of work. They want every creative HOBBYIST to give up. I have seen machine-learning art generators call us artoids (like ‘femoids’ incels or unhealthily online misogynists use to refer to women. To give you the idea of the kind of hate-fueled superiority we’re dealing with) and circle-jerk to the idea of art no longer being a career and no one being able to ask for commissions anymore.

Machine-learning tools are currently a symbol of people who see creativity and art as an enemy, a boogeyman to be slain. They are designed accordingly - stealing human work to create the data, designing it so that people can generate ‘sketches’ or ‘doodles’ to deceive the layman that it was hand-drawn, using real-world likenesses without consent, etc. When tech bros get tired of weaponizing machine-learning because they think we need to get ‘real jobs’ or that furry porn artists charge too much for comms and need to be stopped, it will probably be a lot easier for artists to embrace it as it’ll be a lot easier to develop ethical tools. On top of making development easier, it could become a great tool to make the visual arts accessible for people that have disabilities affecting drawing ability. It could be a wonderful technology.

But as it stands we’re not there yet.

WHATTTT.... ARTOIDS 💀.............................. that is THE most cringe fucking word ever im gonna start calling them fucking inceloids or something

Arent these the people who also have hentai addictions and collect all sorts of images of anime women breasting boobily? Do they think before AI that those images just popped up from the aether? They should also get real jobs that arent living in their moms basements and being a hateful little bitch

It's kind of hilarious that they think machine learning models will be the the end of art though. As if art hasnt been a core human function from prehistoric age and as if it hasnt survived hundreds of purges, demonizations, and attempts to erase certain styles and movements and people. We're going to prevail no matter what and they can die mad about it

[bluejay-makes]

Not my usual posting but I did my very first fruit tats today :')

[tim-official]

thought experiment

imagine there was a generative text bot called AlphaGPT* whose abilities were on par with ChatGPT's. instead of being trained on an enormous dataset comprising all human writings, though, AlphaGPT was trained by giving it a dictionary, an encyclopedia, and hand-crafted rules of grammar, syntax, conversational dynamics, courtesy, ethics, etc.**, all carefully tailored by a team of linguists and philosophers and math people***. no risk of plagiarism, no web scraping. of course, it knows a lot less about the world.

(***no joke, at points in the 60s and 70s, before backpropagation was a thing, this is how most researchers assumed "artificial intelligence" would come about. i doubt this would actually work. but its a thought experiment.) (**what rules of ethics? what conversational dynamics does it prefer? much like with how the company openAI works, let's assume you don't get to know that and you just have to trust them) (*after the program AlphaGo, which learned to play Go at a higher level than any human player in much the same way). reblog for sample size i guess. or dont reblog if you want a lower sample size. thats your prerogative

[eighth-heroine]

if i could just content block everything AI from my life that would be great