ot Unbalanced Dataset to analyse and confused how to use data strategically to get unbiased results
approach to handle unbalanced data
https://lnkd.in/dZHXigP

#datascience #unbalanced #data #analyse

✴️ @AI_Python_EN

ery interesting paper on machine learning algorithms. This paper compares polynomial regression vs neural networks applying on several well known datasets (including MNIST). The results are worth looking.

Other datasets tested: (1) census data of engineers salaries in Silicon Valley; (2) million song data; (3) concrete strength data; (4) letter recognition data; (5) New York city taxi data; (6) forest cover type data; (7) Harvard/MIT MOOC course completion data; (8) amateur athletic competitions; (9) NCI cancer genomics; (10) MNIST image classification; and (11) United States 2016 Presidential Election.

I haven't reproduced the paper myself but I am very tempted in doing it.

Link here: https://lnkd.in/fd-VNtk

#machinelearning #petroleumengineering #artificialintelligence #data #algorithms #neuralnetworks #predictiveanalytics

✴️ @AI_Python_EN

💡💡 K-means Clustering – In-depth Tutorial with Example 💡💡

Credits - Data Flair

Link - https://lnkd.in/eCQUriR

#machineleaning #supervisedlearning #unsupervisedlearning #datascience #data #ai # #technology #deeplearning #artificalintelligence

✴️ @AI_Python_EN

Sampling is a deceptively complex subject, and some academic statisticians have devoted the bulk of their careers to it.

It's not a subject that thrills everyone but is a very important one, and one which seems underappreciated in marketing research and #data science.

Here are some books on or related to sampling I've found helpful:

- Survey Sampling (Kish)
- Sampling Techniques (Cochran)
- Model Assisted Survey Sampling (Särndal et al.)
- Sampling: Design and Analysis (Lohr)
- Practical Tools for Designing and Weighting Survey Samples (Valliant et al.)
- Survey Weights: A Step-by-step Guide to Calculation (Valliant and Dever)
- Complex Surveys (Lumley)
- Hard-to-Survey Populations (Tourangeau et al.)
- Small Area Estimation (Rao and Molina)

The first three are regarded as classics (though still relevant.) Sharon Lohr's book is the friendliest introduction I know of on this subject. Standard marketing research textbooks also give simple overviews of sampling but do not get into depth.

There are also academic journals that feature articles on sampling, such as the Public Opinion Quarterly (AAPOR) and the Journal of Survey #Statistics and Methodology (AAPOR and ASA).

✴️ @AI_Python_EN

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,
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* #Machine #Learning,
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Join us and learn hot topics of Computer Science together.👇👇👇

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