📚 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.

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⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

📂 8 Steps to Mastering MLOps
✅ For data scientists


⏯️ Introduction to MLOps

📎 MLOps Zoomcamp

📎 Neptune Blog

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2️⃣ Model Management

📎 ML Model Registry

📎 ML Experiment Tracking

📎 Experiment Tracking

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3️⃣ Building a pipeline of models

📎 Building End-to-End ML Pipelines

📎 Orchestration Tools

📎 Orchestration & ML Pipelines

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4️⃣ Monitoring models

📎 Evidently AI Blog

📎 NannyML Blog

📎 Model Monitoring

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5️⃣ Introduction to Docker

📎 Docker Tutorial

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6️⃣ Designing ML systems

📎 Designing ML Systems

📎 ML System Design Patterns

📎 ML System Design Interview

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7️⃣ Sample projects

📎 Evidently AI Database

📎 LLMOps Case Studies

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8️⃣ Comprehensive roadmap

📎 MLOps Roadmap 2024

#MLOps #MachineLearning #DataScience #AI #ModelMonitoring #MLPipelines #Docker #MLSystemDesign #ExperimentTracking #LLMOps #NeuralNetworks #DeepLearning #AITools #MLProjects #MLOpsRoadmap

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟