#R Language Machine Learning

A tip if you’re trying to learn R ⬇️ SWIRL is a package within R Studio that has tutorials so you can “learn R within R.” I did the R Programming course as an assignment a year or so ago and now use it to refresh my memory about basic terms and codes within R. It also looks like there are quite a few “courses” within SWIRL that are not just for beginners if you already know some R and want to advance - although I haven’t tried them yet 😄 Share this with your friends who might find this useful since R is surprisingly necessary for a lot of majors and academic fields 👩🏼‍💻 #rprogramming #collegetips #gradschool #womeninstem #r

My second most asked question is always how I learnt R     I taught myself mainly by just trial and error (I have to actually physically do something I can’t just watch videos as I don’t take it in) so I started with the very basics.   I think it’s so easy to overdo and feel like you need to know how to do everything or a lot of things at the start. Stick to simple things like understanding the R studio interface and loading packages and other basic commands     (after this most things I learnt were googling very specifically what I needed to do and adding the command to a ‘useful command’ list I have)    Next:  Following a vignette from start to finish (one that would be similar to what I would soon need) I then would go through and click on functions to look at the arguments (this tells you all the parameters for the function) and how I can change them if needed!    Finally try swirl it’s so easy to just load directly in the terminal and you learn as you go!        What’s your top tips?    I also have so many more so make sure you follow!      #phd #student #coding #rprogramming #university #tipsandtricks

Here’s your full roadmap on how to get into machine learning. Comment “Roadmap” to get the pdf. Save and follow for more. #ai #machinelearning #coding #programming #cs

These are some of the best beginner-friendly resources I’ve found to actually understand machine learning. Nothing overly complicated, just what you need to get the concepts and start building. Comment ML and I’ll send you all the resources.

Situation: A machine was given data… but the challenge was clear — should it predict a number or choose a category? 🤖📈 Task: To solve real-world problems like forecasting prices, detecting spam, or recognizing diseases with precision. Action: Regression learned to predict continuous values like house prices and sales trends, while classification mastered sorting data into categories like fraud vs safe or spam vs inbox. Result: Two core ML superpowers — one answers “How much?” and the other answers “Which one?” — powering smarter predictions every day. From business forecasts to life-saving diagnostics, machine learning turns raw data into decisions that shape the future. 🚀✨ #MachineLearning #Regression #Classification #AIForBeginners #DataScience MLFoundations ArtificialIntelligence STEMEducation TechLearning ChiragJain

📚 Starting a new learning journey! 🤓 For the next two months, Dive into the world of R programming language to enhance my data analysis skills. Here's what you should plan to cover: Month 1: 📌 Basics of R programming, data structures, and functions 📌 R packages and libraries for data manipulation and visualization 📚 Resources: ➡️ R Programming A-Z™ on Udemy, ➡️ R for Data Science book by Hadley Wickham and Garrett Grolemund, ➡️ DataCamp courses Month 2: 📌 Statistical analysis and modeling, machine learning algorithms 📌 Web scraping and text analysis 📌 Putting it all together with a real-world project 📚 Resources: ➡️ Introduction to Machine Learning with R book by Scott V. Burger ➡️ Web Scraping with R course on DataCamp, ➡️ Data Science Projects in R book by Prabhanjan Narayanachar Tattar . Check link in bio for free Roadmap and Resources . Are you excited to learn new things and expand my knowledge! Follow along for updates 🚀 . Follow @citizendatascientist for more related content. . Get notified right away on our daily content, tap the triple dots on the top right “…” and tap ‘Turn Post Notifications On’ . . #data #datascience #dataanalyst #dataanalysis #dataanalytics #sql #dataengineer #dataengineering #coding #coder #programming #programmer #programmerlife #coder #dataengineer #developer #100daysofcode #python #codingisfun #codinglife #programmers #datastructures r #coderslife #Python #R #DataScience #ProgrammingLanguages 🐍📊

Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai

What is Machine leaning part 1. #machinelearning #ai

Master R — the language trusted by statisticians, analysts, and researchers across the globe. From wrangling raw data to building interactive dashboards and machine learning models, R has everything you need to go pro. This guide covers: ✅ What is R? ✅ Topics to master ✅ Must-know libraries & functions ✅ Real projects to build ✅ Where to learn it (Free book and platform) 👉 Save this post, tag a learner, and start your R journey today! Follow @datateach.ai 📍 Visit Us: 3rd Floor, Manyavar Building, KPHB, Hyderabad 📞 +91 98859 46789 ✉️ [email protected] 🌐 www.datateach.ai #datascience #machinelearning #python #artificialintelligence #ai #data #dataanalytics #bigdata #programming #coding #datascientist #technology #deeplearning #computerscience #datavisualization #analytics #pythonprogramming #dataanalysis #programmer #business #ml #database #statistics #innovation

📍Day 10: Top 10 Machine Learning Algorithms for ML Engineers ⬇️ Save it for Later👇 1. Machine learning engineers need to use a diverse array of algorithms to solve problems and extract insights from data. 2. Each algorithm has its strengths and is suited to specific types of tasks. Knowing which algorithms to choose and how to apply them to real data is a crucial skill. 3. Most commonly, you will use these algorithms: - Linear regression - Logistic regression - Decision trees - Random forest - Support vector machines (SVM) - K-nearest neighbors - K-means clustering - Gradient boosting machines (GBM) - Neural networks/deep learning - Principal component analysis (PCA) ✅ Type ‘MLAlgos’ in the comment section and we will DM the PDF version for FREE ✨ ⏰ Like this post? Go to our bio click subscribe button and subscribe to our page. Join our exclusive subscribers channel ✨ Hashtags (ignore): #datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code

Large Language Models (LLMs) such as ChatGPT are based on neural networks called transformers, an architecture built using multiple attention mechanisms and multilayer perceptrons (MLPs). These models process input text by learning context through self-attention mechanisms, which weighs the importance of each pair of words. This way, long sequences are no longer an issue. This contextual understanding is passed through MLPs, which learn the representations and patterns of the sequence. To generate text, the model generates a probability distribution of the next word; we choose the highest-probability word and keep predicting the next word, iterating to create a sentence or paragraph. C: 3blue1brown Join our AI community for more posts like this @aibutsimple 🤖 #neuralnetwork #llm #gpt #artificialintelligence #machinelearning #3blue1brown #deeplearning #neuralnetworks #datascience #python #ml #pythonprogramming #datascientist

I wish I knew about this website when I started learning Machine Learning Algorithm. This website explains each ML algorithms with visual representation which makes it easy to understand the complex ML algorithms. Share this with someone who is learning about these concepts. Follow @thedataevangelist for more such content