Best tweets about Machine Learning

Practical Machine learning is more than just building models. 3 ways to put your machine learning model in production 👇

When a data scientist tells you: "I spend 80% of my time on machine learning." ...they actually mean: "I spend 80% of my time on data processing and the rest on ML"

Taking this moment to thank everyone who follows me. Are you into Python, Machine Learning and Data Science? I am sharing daily content around that, so make sure to follow if you haven't already. Introduce yourself below to the amazing community, let's keep building this up👇

Are you serving machine learning models in production? How are you deploying your models? What technology do you use?

Are you looking to get job as a Data Scientist or ML Engineer? The general way is to apply for an opening but the best way is to find open source companies and then contributing to their projects Here's a list of 10 Open Source Machine Learning Startups you can contribute to👇🏽

I have created a document that contains 15 resources for: - Machine Learning 📠 - Python 🐍 - Data Science 🔢 - Artificial Intelligence 🤖 - NLP 🗣️ - Open Source 💜 Access it now 👇

Machine Learning models do not predict the future. They just find patterns in the past.

Some people will tell you that you must know math to get into machine learning Don't listen to them They are just afraid of competition

Working on problems is the best way to learn machine learning. But most people don't know where to start. Here are 10 projects to start your journey: ↓

Note to self: Statistics = Math Statistics + Python = Data Analytics Statistics + Python + Model = Machine Learning Statistics + Python + Model + Domain Knowledge = Data Science

thinking of becoming a luddite except for smashing the machine learning code mills

it would be a lot more accurate to call it computational statistics than machine learning or artificial intelligence

I am much less worried about sentient AIs than the fact that we are surrounded and influenced by machine learning systems and are not taught how to reason through how they work.

Crush Machine Learning in the second half of 2022 🎯 🚀July - Python/SQL 🚀August - Data Viz in Pandas/Matplotlib 🚀September - Math/Stats 🚀October - Machine Learning 🚀November - Deep Learning 🚀December - Projects + Interview Prep 🤖New Year - Apply for ML Engineer roles

Thank you for 10K followers🥳 We plan to release even more content around 🐍Python 🤖Machine Learning 🧠Deep Learning 🚀Building a business with ML in the upcoming weeks, so stay tuned!

What are some beginner-level courses you can recommend me to learn Machine Learning? This is one of the questions I get most. I don't recommend any paid courses to anyone. You can consider checking these 2 free courses

We're hiring! Come build chinchilla optimal code gen models with us. We're looking for skilled data engineers and machine learning engineers.

Here are 10 video tutorials for 10 popular Machine Learning algorithms implemented from scratch in Python👇

Machine learning is hard, there's no doubt about that But Machine learning in production is much harder than ML in notebooks. Developing the ML pipeline to deployment and then maintaining consistent quality prediction building a retraining pipeline becomes more complicated...

Hey everyone! ✨ I am actively looking for new opportunities in: 🔸Data Science 🔸 Machine Learning 🔸 Python Development I am open to developer 👨🏻‍💻or dev advocate 🥑 positions. I have 2.4 years of experience in ML/DL/Python, building end-to-end software. DMs are open! 📩

Most important things in applied machine learning: 🔸 Cross-validation 🔸 Exploratory data analysis 🔸 Data cleaning and preparation 🔸 Feature engineering 🔸 Scikit-Learn's fit() and predict() Focus on them first

Kick everyone's butt in your next machine learning interview: • Pick a problem • Build a model • Wrap it in a simple API • Host it • Build a small app that uses it • Set up a retraining pipeline • Set up monitoring • Write about all of this Nobody will be close.

I've helped hundreds of people start with machine learning. Everyone asks me the same, fundamental question. But they all hate my answer. Engineers even more. Let me try again, but this time I'll show you a few lines of code that will 10x your process: 1 of 16

The most important skills for Machine Learning: • Python • Linux (env setup, ssh, moving files, editor) • git • pdb • creating good train - val - test splits • ability to scan papers for relevant information • a learner mindset • clear writing Anything I missed?

It's really weird how much machine learning is done in Python, because numerical/array computing should in theory be like 50x easier in a compiled language where you can build abstractions and leave the optimization to the compiler, no?

If you know python and SQL It’s super easy to make the leap to doing data science and machine learning A combination of watching YouTube videos and attending a handful of ML workshops will do trick

📊The only Statistics and Probability Playlist you'd need to start your Data Science/Machine Learning journey: (I was super clueless about stats and prob despite watching 100s of videos but this playlist totally changed everything for me)

particle physics? more like machine learning

Get into Machine Learning and you'll become a forever student.

The best place to learn about challenges and best practices for production machine learning has got to be the personal blogs of ML engineers.

6 questions I answer before starting a new machine learning project: 1. What problem am I solving? 2. What constraints do I have? 3. Who are the main stakeholders? 4. What are the risks? 5. How do I measure progress? 6. What does success look like?

7 baby steps to start with machine learning: 1. Start with Python 2. Learn to use Google Colab 3. Take a Pandas tutorial 4. Then a Seaborn tutorial 5. Decision Trees is a good first algorithm 6. Finish Kaggle's "Intro to Machine Learning" 7. Solve one simple problem

To start with machine learning from the very beginning: • Introduction to Python Programming (Udacity) • Machine Learning Crash Course (Google) • Machine Learning (Coursera) In that order. They are all free. Take your time. This is a marathon, not a sprint.

I did not spend four years in art school in Europe learning how to draw giant anime titty girls in the style of 19th century oil paintings just to be replaced by “machine learning”

The biggest lie in modern AI is that you don't need to understand the math behind it to be able to create successful AI systems. To solve a problem using machine learning, you need to formulate an optimization problem. If you don't understand math, good luck in guessing it!

Software engineering sprints are highly predictable. Machine learning sprints are highly unpredictable. You should expect to try 10+ things that don't work for every 1 that does.

"FAANG" companies using Machine Learning. Facebook → Targeted Advertising Amazon → Product recommendations Apple → Voice Assistants, Language Translations, Health Monitoring Netflix → Movie Recommendations Google → Ads, Search Engine, Recommendation Systems

9 Python Libraries You Should Know 🔽 🐼Pandas → Data Analysis 🕷️BeautifulSoup/Scrapy → Web scraping 📊Matplotlib/Seaborn → Visualization 📸SimpleCV → Computer vision ⚙️Scikitlearn → Machine Learning 🌐Tensorflow/Pytorch → Deep Learning

Our 100,000-core machine learning cluster has spent 2.5 million CPU years looking for innovative solutions to highway congestion AI: trains NO NOT LIKE THAT

Machine Learning Project Ideas for Beginners : 🧑‍🏭Waiter tip prediction 🧑‍🏭Language Detection 🧑‍🏭News classification 🧑‍🏭Stress detection 🧑‍🏭Sales prediction