📄 "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 👇

https://www.mltut.com/best-resources-to-learn-artificial-intelligence/

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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

📎 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

➖➖➖➖➖➖

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

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Mastering CNNs: From Kernels to Model Evaluation

If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Let’s break it down from basic to advanced.

1. What is Conv2D?

Conv2D is a 2D convolutional layer used in image processing. It takes an image as input and applies filters (also called kernels) to extract features.

2. What is a Kernel (or Filter)?

A kernel is a small matrix (like 3x3 or 5x5) that slides over the image and performs element-wise multiplication and summing.

A 3x3 kernel means the filter looks at 3x3 chunks of the image.

The kernel detects patterns like edges, textures, etc.


Example:
A vertical edge detection kernel might look like:

[-1, 0, 1]
[-1, 0, 1]
[-1, 0, 1]

3. What Are Filters in Conv2D?

In CNNs, we don’t use just one filter—we use multiple filters in a single Conv2D layer.

Each filter learns to detect a different feature (e.g., horizontal lines, curves, textures).

So if you have 32 filters in the Conv2D layer, you’ll get 32 feature maps.

More Filters = More Features = More Learning Power

4. Kernel Size and Its Impact

Smaller kernels (e.g., 3x3) are most common; they capture fine details.

Larger kernels (e.g., 5x5 or 7x7) capture broader patterns, but increase computational cost.

Many CNNs stack multiple small kernels (like 3x3) to simulate a large receptive field while keeping complexity low.

5. Life Cycle of a CNN Model (From Data to Evaluation)

Let’s visualize how a CNN model works from start to finish:

Step 1: Data Collection

Images are gathered and labeled (e.g., cat vs dog).

Step 2: Preprocessing

Resize images

Normalize pixel values

Data augmentation (flipping, rotation, etc.)

Step 3: Model Building (Conv2D layers)

Add Conv2D + Activation (ReLU)

Use Pooling layers (MaxPooling2D)

Add Dropout to prevent overfitting

Flatten and connect to Dense layers

Step 4: Training the Model

Feed data in batches

Use loss function (like cross-entropy)

Optimize using backpropagation + optimizer (like Adam)

Adjust weights over several epochs

Step 5: Evaluation

Test the model on unseen data

Use metrics like Accuracy, Precision, Recall, F1-Score

Visualize using confusion matrix

Step 6: Deployment

Convert model to suitable format (e.g., ONNX, TensorFlow Lite)

Deploy on web, mobile, or edge devices

Summary

Conv2D uses filters (kernels) to extract image features.

More filters = better feature detection.

The CNN pipeline takes raw image data, learns features, and gives powerful predictions.

If this helped you, let me know! Or feel free to share your experience learning CNNs!

#DeepLearning #ComputerVision #CNNs #Conv2D #MachineLearning #AI #NeuralNetworks #DataScience #ModelTraining #ImageProcessing

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🚀 Master the Transformer Architecture with PyTorch! 🧠

Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".

🔗 Check it out here:
https://www.k-a.in/pyt-transformer.html

This guide offers:

🌟 Detailed explanations of each component of the Transformer architecture.

🌟 Step-by-step code implementations in PyTorch.

🌟 Insights into the self-attention mechanism and positional encoding.

By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.

#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks



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