Empowering you to use machine learning to get valuable insights from data.

🔥 Implement basic ML algorithms and deep neural networks with PyTorch.
🖥 Run everything on the browser without any set up using Google Colab.
📦 Learn object-oriented ML to code for products, not just tutorials.

Github Link - https://lnkd.in/f8nu8UR

#datascience #data #dataanalysis #ml #machinelearning #deeplearning #ai #artificialintelligence

✴️ @AI_Python_EN

💡💡 Commonly used Machine Learning Algorithms 💡💡

Here is the list of commonly used machine learning algorithms. The code is provided in both #R and #Python. These algorithms can be applied to almost any data problem:

✅Linear Regression
✅Logistic Regression
✅Decision Tree
✅SVM
✅Naive Bayes
✅kNN
✅K-Means
✅Random Forest
✅Dimensionality Reduction Algorithms
✅Gradient Boosting algorithms
✔️GBM
✔️XGBoost
✔️LightGBM
✔️CatBoost

Credit: Analytics Vidhya,Sunil Ray

Thanks for the share Steve Nouri.

#datascience #deeplearning #ai #artificialintelligence #machinelearning #data #r #python

✴️ @AI_Python_EN

If you've read job descriptions in data lately you are probably confused. Are you a data scientist, machine learning engineer, or research scientist? Instead of title matching, try asking yourself these questions:

1. Can you use statistics to answer questions about a situation that is new to you? Meaning, is your comfort with stats solid enough that you can bring it to bear appropriately depending on scenario?

2. Can you explain why a particular model performs well in a scenario, rather than just noting it does well? Meaning, do you understand the inner workings of models to tune and make sense of why they do what they do?

3. If someone mentions time and space complexity to you, does it make sense? In a big data world, thinking carefully about load of a particular algorithm is extremely important. This matters particularly for MLE and science positions.

4. Can you build something new? Maybe there isn't a perfect algorithm for what you want. Maybe the package in R doesn't exist. Can you make it happen if you need to?

5. Do you know what it means to put something into production? Do you have examples of how you've succeeded or failed with this?

These questions are not all encompassing, but they point to some of the key skillsets you'll need.

#datascience #analytics #data

✴️ @AI_Python_EN

📚📖 Python Machine Learning Tutorial 📖📚

➡️ Python Machine Learning – Tasks and Applications ( https://lnkd.in/fZcs-xE)
➡️ Python Machine Learning Environment Setup – Installation Process (https://lnkd.in/fJHwbjr)
➡️ Data Preprocessing, Analysis & Visualization (https://lnkd.in/fVz58kJ)
➡️ Train and Test Set (https://lnkd.in/fq_GXjn)
➡️ Machine Learning Techniques with Python (https://lnkd.in/fjdsQzd)
➡️ Top Applications of Machine Learning (https://lnkd.in/f-CNyK2)
➡️ Machine Learning Algorithms in Python – You Must Learn (https://lnkd.in/fTxCA23)

#python #machinelearning #datascience #data #dataanalysis #artificialintelligence #ai #visualization #algorithms

✴️ @AI_Python_EN

It is a good feeling when a popular Python package adds a new feature based on your article :-)

#Yellowbrick is a great little #ML #visualization library in the Python universe, which extends the Scikit-Learn API to allow human steering of the model selection process, and adds statistical plotting capability for common diagnostics tests on ML.

Based on my article "How do you check the quality of your regression model in Python? they are adding a new feature to the library - Cook's distance stemplot (outlier detection) for regression models.

#python #datascience #machinelearning #data #model
https://www.scikit-yb.org/en/latest/

✴️ @AI_Python_EN

Welcome to @ai_machinelearning_big_data the world of :
* #Artificial #Intelligence,
* #Deep #Learning,
* #Machine #Learning,
* #Data #Science
* #Python Programming language
* and more advanced research
links🔗 and more you wanted.
Join us and learn hot topics of Computer Science together.👇👇👇

@ai_machinelearning_big_data