📄 "Pandas Introduction to Advanced" booklet

👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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Find these FREE AI Courses here 👇

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🔗 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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📚 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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📂 8 Steps to Mastering MLOps
✅ For data scientists


⏯️ Introduction to MLOps

📎 MLOps Zoomcamp

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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

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