Yvens Alberus

06/19/2024

🌟 Working with NumPy (.npy) Files should be more popular 🌟

Here’s why:

🔹 Retaining Data Format:
- Ensures original dataset format
- No need to reorganize or specify values

🔹 Faster Performance:
- Speedup ~70 times compared to .csv files
- Immediate dataset loading

🔹 Memory Efficiency:
- Consumes less memory space
- Ideal for large datasets

🔹 **Native Text File**:
- Recognizes numbers automatically
- Seamlessly integrates with NumPy

🔹 Code Example:
- Simple conversion using `np.save()` to save and `np.load()` to load data

.npy files can significantly boost your productivity and efficiency when handling large datasets in Python.

A career in data/AI is: "you start to learn... And never stop" ✨

**Keep learning with me, like, share, and follow!**

06/16/2024

🚀“The First Rule of Machine Learning: Start without Machine Learning”🚀

This is a summary of Eugene's Yan article I shared two days ago,

Let's break it down: 📊🧠
🤔 Why should you solve problems without machine learning first?

- Understand the Problem and Data:
Gain deeper insights by attempting manual solutions first. It helps understand the problem and the data better

- Establish Non-ML Baselines:
Simple heuristics like if/else rules and moving averages can be surprisingly effective.

- Ensure Robust Data Pipelines:
Quality data and labels are the backbone of ML’s success.

- WHEN TO MOVE TO ML 🤔
Move to ML when maintaining non-ML solutions become too complex and hard to maintain.

A career in data/AI is: "You start to learn... And never stop."
🌟 Keep learning with me, like, share, and follow. 📚🤝

06/12/2024

🚀Embeddings in Machine Learning! 🧠✨

How ML models understand human stuff?

Vector Representation: Embeddings represent objects like text, images, and audio as points in a continuous vector space. examples include: One-Hot Encoding, PCA, SVD, Word2vec, Bert

What they bring to the table:

- Algorithm Learning:
They help to learn from data using various algorithms, especially neural networks.

- Versatile Applications:
Powering text and image search engines, recommendation systems, chatbots, and fraud detection.

- NLP Powerhouse:
Crucial for tasks like text classification, sentiment analysis, and machine translation.

- Semantic Insight:
Capture complex semantic relationships, boosting ML model performance.

A career in data/AI is: "you start to learn... And never stop" Keep learning with me, like, share, and follow.

06/10/2024

✨Essential Tips for SQL Performance Tuning:

🔹 **Reducing Table Size**:
- Filter data to include only necessary observations for quicker results.
- Limit data to a small time window, ideal for time series data.
- Use `LIMIT` sparingly; subqueries work better for aggregations.

🔹 **Simplifying Joins**:
- Optimize by reducing table sizes before joining.
- Pre-aggregate data to evaluate fewer rows in joins.

🔹 **Using the EXPLAIN Command**:
- Get a preview of query ex*****on time.
- Understand the order and cost of each query step.
- Pinpoint and optimize the most expensive parts of your query.

A career in data/AI is: "you start to learn... And never stop."

🌟 Keep learning with me, like, share and follow. 🌟

About me In the realm of Data Science and Machine Learning, my journey began with a deep dive into genomic data mining during my master's program. Since then, I've relentlessly pursued excellence in this field. Equipped with a Python and SQL proficiency, I vividly recall the exhilaration of executin...

06/09/2024

🌟 Dive into the world of ! Did you know that mastering Regularization can dramatically improve your models? Here's why it's essential:

🔹 **Reduces Overfitting:** By introducing a penalty term, Regularization corrects overfitting.

🔹 **Improves Generalizability:** Trades a minor decrease in training accuracy for better performance on new data.

🔹 **Bias-Variance Trade-off:** Decreases model variance, even if it slightly increases bias.

🔹 **Techniques to Explore:** Lasso regression, Ridge regression, Elastic net regularization, and more.

🔹 **Ensures Robust Models:** Helps your models make accurate predictions beyond just the training data.

🔹 **Versatile Methods:** Use data augmentation, early stopping, dropout, and weight decay.

A career in data/AI is: "you start to learn... and never stop."

👉 Keep learning with me, like, share, and follow.

06/09/2024

"Regularization in machine learning is essential for preventing overfitting and improving model generalizability. Here are some key techniques to keep in mind:
- Lasso Regression penalizes high-value coefficients
- Ridge Regression shrinks feature weights towards zero
- Elastic Net combines L1 and L2 regularization
- Data Augmentation creates artificial samples
- Early Stopping limits model training iterations
- Dropout trains multiple models with different architectures
- Weight Decay reduces network weights

A career in data/AI is: "you start to learn... and never stop"
Keep learning with me, like, share and follow. "

04/16/2024

Mi 11 Imbatible de jugadores que realmente ví jugar:
Neuer- Ramos- Lucio-Marcelo-Alves- Iniesta- KDB-Casemiro(un duro que corte jugadas)
- Neymar- Messi-Cr7
Cuál es el tuyo?

05/31/2023

Moments we live for!!💯.

05/28/2023

Happy mother’s
Beautiful mom🥰😍😍🥰