🔗 Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.

https://dafriedman97.github.io/mlbook/content/introduction.html

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https://t.iss.one/CodeProgrammer ✅

The Beginner’s Guide to Clustering with Python

Clustering is a widely applied method in many domains like customer and image segmentation, image recognition, bioinformatics, and anomaly detection, all to group data into clusters in terms of similarity. Clustering methods have a double-sided nature: as a machine learning technique aimed at discovering knowledge underneath unlabeled data (unsupervised learning), and as a descriptive data analysis tool for uncovering hidden patterns in data.

This article provides a practical hands-on introduction to common clustering methods that can be used in Python, namely k-means clustering and hierarchical clustering.

Read: https://machinelearningmastery.com/the-beginners-guide-to-clustering-with-python/

By: https://t.iss.one/DataScienceM 🌟

The Roadmap for Mastering MLOps in 2025

Organizations increasingly adopt machine learning solutions into their daily operations and long-term strategies, and, as a result, the need for effective standards for deploying and maintaining machine learning systems has become critical. MLOps (short for machine learning operations) arose to meet these needs. It encompasses a series of practices that blend machine learning modeling, software engineering, and data engineering across the entire machine learning system lifecycle.

If you are keen on venturing into the realm of MLOps in 2025 and unsure of where to start, this article highlights and puts together its building blocks and latest trends, both crucial to gain understanding of the current #MLOps landscape.

Read: https://machinelearningmastery.com/the-roadmap-for-mastering-mlops-in-2025/

By: https://t.iss.one/DataScienceM 💠

📚 Become a professional data scientist with these 17 resources!



1️⃣ Python libraries for machine learning

◀️ Introducing the best Python tools and packages for building ML models.

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2️⃣ Deep Learning Interactive Book

◀️ Learn deep learning concepts by combining text, math, code, and images.

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3️⃣ Anthology of Data Science Learning Resources

◀️ The best courses, books, and tools for learning data science.

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4️⃣ Implementing algorithms from scratch

◀️ Coding popular ML algorithms from scratch

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5️⃣ Machine Learning Interview Guide

◀️ Fully prepared for job interviews

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6️⃣ Real-world machine learning projects

◀️ Learning how to build and deploy models.

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7️⃣ Designing machine learning systems

◀️ How to design a scalable and stable ML system.

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8️⃣ Machine Learning Mathematics

◀️ Basic mathematical concepts necessary to understand machine learning.

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9️⃣ Introduction to Statistical Learning

◀️ Learn algorithms with practical examples.

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1️⃣ Machine learning with a probabilistic approach

◀️ Better understanding modeling and uncertainty with a statistical perspective.

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1️⃣ UBC Machine Learning

◀️ Deep understanding of machine learning concepts with conceptual teaching from one of the leading professors in the field of ML,

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1️⃣ Deep Learning with Andrew Ng

◀️ A strong start in the world of neural networks, CNNs and RNNs.

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1️⃣ Linear Algebra with 3Blue1Brown

◀️ Intuitive and visual teaching of linear algebra concepts.

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🔴 Machine Learning Course

◀️ A combination of theory and practical training to strengthen ML skills.

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1️⃣ Mathematical Optimization with Python

◀️ You will learn the basic concepts of optimization with Python code.

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1️⃣ Explainable models in machine learning

◀️ Making complex models understandable.

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⚫️ Data Analysis with Python

◀️ Data analysis skills using Pandas and NumPy libraries.

#DataScience #MachineLearning #DeepLearning #Python #AI #MLProjects #DataAnalysis #ExplainableAI #100DaysOfCode #TechEducation #MLInterviewPrep #NeuralNetworks #MathForML #Statistics #Coding #AIForEveryone #PythonForDataScience

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer

📖 100 Essential Data Science Interview Questions

👨🏻‍💻 Preparing for a data science interview?

Reviewing fundamental questions is one of the best strategies for success. During the interview, it's crucial to communicate clearly and simply—especially when explaining complex models and data.
These 100 carefully selected questions will not only help you impress your interviewer but also boost your confidence throughout the interview process.

#DataScienceInterview #TechCareers #InterviewPreparation

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

💧 18 of the best blogs to start and grow on your data science path!


👨🏻‍💻 If I were to start my data journey all over again, I would follow these 18 blogs without a break!

💬 The path to growth in data has nothing to do with your starting point, what matters is how consistently you learn.

✅ These blogs are like a mentor in your email; every week a new insight, a learning, a new idea. 👇


🥵 The Data Hustle Blog

⏪ Real experiences from the world of data in a friendly tone.

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🥵 Diving Into Data Blog

⏪ Summarizing data-heavy concepts in simple language.

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🥵 Tech Growth Series Blog

⏪ A guide to professional growth in the field of technology and data.

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🥵 Data Neighbor Blog

⏪ Discussion and experience of the real learning path in the data field.

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🥵 DataEngineer.io Blog

⏪ The world of data engineering with real solutions.

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🥵 The Data Analyst Blueprint Blog

⏪ A complete roadmap to becoming a data analyst step by step.

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🥵 Jam with AI Blog

⏪ Up-to-date content in the field of AI; suitable for starting and continuing learning.

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🥵 Zero2Dataengineer Blog

⏪ From zero to becoming an engineer with a specific path.

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🥵 Maistermind Blog

⏪ Deep insights and systems thinking in data.

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🥵 The Fit Data Scientist Blog

⏪ Data science + healthy lifestyle = true balance.

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🥵 To Be a Data Scientist Blog

⏪ Focus on career path, soft skills, and motivation to continue in the world of data.

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🥵 Smarter Techies Blog

⏪ Continuous learning and golden tips for data scientists.

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🥵 Data Marks Blog

⏪ An excellent compilation of useful resources and tools in the world of data.

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🥵 Tech Audience Accelerator Blog

⏪ Building a personal brand and growing in the technology space.

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🥵 ByteByteGo Blog

⏪ System design and technical concepts for every data engineer.

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🥵 The Neural Maze Blog

⏪ Exploring neural models and artificial intelligence with tangible examples.

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🥵 To Data & Beyond Blog

⏪ Data, personal growth, and new paths for the future of work.

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🥵 Non-Brand Data Blog

⏪ Combining data with marketing expertise and personalized strategy.