Machine Learning with Python

The Large Language Model Course How to become an LLM Scientist or Engineer from scratch Read FreeL

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Python, Bash and SQL Essentials for Data Engineering Specialization What you'll learn Develop #dataengineering solutions with a minimal and essential subset of the Python language and the Linux environment Design scripts to connect and query a #SQL #database using #Python Use a #scraping library in Python to read, identify and extract data from websites Enroll Free: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke https://t.me/DataScience4

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Running a Neural Network Model in OpenCV Many machine learning models have been developed, each with strengths and weaknesses. This catalog is not complete without neural network models. In OpenCV, you can use a neural network model developed using another framework. In this post, you will learn about the workflow of applying a neural network in OpenCV. Specifically, you will learn: 🏐 What OpenCV can use in its neural network model 🏐 How to prepare a neural network model for OpenCV Read: https://machinelearningmastery.com/running-a-neural-network-model-in-opencv/

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"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA) 📘 Freely available at: https://web.stanford.edu/~boyd/vmls/ 📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9

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Practical Deep Learning A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems. New! Enroll Free: https://course.fast.ai/

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how transformers remember facts

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MIT's "Machine Learning" lecture notes PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

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🚀 EXCITING UPDATE ALERT 🚀 A brand-new, FREE course on LLM evaluations has just been launched in collaboration with Elvis Saravia! Dive into the world of AI model assessments and elevate your skills with cutting-edge insights. 📚 What’s Included? - ⚖️ In-depth coverage of LLM-as-a-judge metrics, LLM unit testing, monitoring, and beyond - 🤖 Hands-on experience with real-world projects, such as building a YouTube search agent - 🎉 Access to open-source models via LiteLLM Whether you're an AI enthusiast, developer, or researcher, this course is designed to empower you with practical knowledge and tools. Don’t miss out—enroll now to secure your spot! 👇 https://www.comet.com/site/llm-course/

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Some people asked me about a resource for learning about Transformers. Here's a good one I am sharing again -- it covers just about everything you need to know. brandonrohrer.com/transformers Amazing stuff. It's totally worth your weekend.

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🔰 How to become a data scientist in 2025? 👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field. 🔢 Step 1: Strengthen your math and statistics! ✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master: ✅ Linear algebra: matrices, vectors, eigenvalues. 🔗 Course: MIT 18.06 Linear Algebra ✅ Calculus: derivative, integral, optimization. 🔗 Course: MIT Single Variable Calculus ✅ Statistics and probability: Bayes' theorem, hypothesis testing. 🔗 Course: Statistics 110 ➖➖➖➖➖ 🔢 Step 2: Learn to code. ✏️ Learn Python and become proficient in coding. The most important topics you need to master are: ✅ Python: Pandas, NumPy, Matplotlib libraries 🔗 Course: FreeCodeCamp Python Course ✅ SQL language: Join commands, Window functions, query optimization. 🔗 Course: Stanford SQL Course ✅ Data structures and algorithms: arrays, linked lists, trees. 🔗 Course: MIT Introduction to Algorithms ➖➖➖➖➖ 🔢 Step 3: Clean and visualize data ✏️ Learn how to process and clean data and then create an engaging story from it! ✅ Data cleaning: Working with missing values ​​and detecting outliers. 🔗 Course: Data Cleaning ✅ Data visualization: Matplotlib, Seaborn, Tableau 🔗 Course: Data Visualization Tutorial ➖➖➖➖➖ 🔢 Step 4: Learn Machine Learning ✏️ It's time to enter the exciting world of machine learning! You should know these topics: ✅ Supervised learning: regression, classification. ✅ Unsupervised learning: clustering, PCA, anomaly detection. ✅ Deep learning: neural networks, CNN, RNN 🔗 Course: CS229: Machine Learning ➖➖➖➖➖ 🔢 Step 5: Working with Big Data and Cloud Technologies ✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing. ✅ Big Data Tools: Hadoop, Spark, Dask ✅ Cloud platforms: AWS, GCP, Azure 🔗 Course: Data Engineering ➖➖➖➖➖ 🔢 Step 6: Do real projects! ✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio. ✅ Kaggle competitions: solving real-world challenges. ✅ End-to-End projects: data collection, modeling, implementation. ✅ GitHub: Publish your projects on GitHub. 🔗 Platform: Kaggle🔗 Platform: ods.ai ➖➖➖➖➖ 🔢 Step 7: Learn MLOps and deploy models ✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model. ✅ MLOps training: model versioning, monitoring, model retraining. ✅ Deployment models: Flask, FastAPI, Docker 🔗 Course: Stanford MLOps Course ➖➖➖➖➖ 🔢 Step 8: Stay up to date and network ✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field. ✅ Read scientific articles: arXiv, Google Scholar ✅ Connect with the data community: 🔗 Site: Papers with code 🔗 Site: AI Research at Google

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