Where to get data for your next machine learning project?

An overview of 5 amazing resources to accelerate your next project with data!

📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.

📌 Kaggle Dataset
Explore, analyze, and share quality data.

📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources

📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.

📌 Azure public data sets
Public data sets for testing and prototyping.

Can you write a program to print "Hello World" in python?

Many of you already guessed it correctly. Brilliant people ❤️

Here is the correct solution

import sys
sys.stdout.write("Hello World\n")

What is your preferred method for handling missing data in datasets?

1. Imputation techniques (mean, median, mode)
2. Deleting rows/columns with missing data
3. Using predictive models for imputation
4. Handling missing data as a separate category
5. Other (please specify in comments) 👇👇

Young people,

Go to the gym,
Even if you’re tired.

Start that business,
Even if you’re poor.

Invest in education,
Even if you’re broke.

Approach that boy or girl,
Even if you’re shy.

Do that work,
Even if you’re unmotivated.

You are a not weak.

Find a way to get things done.

TrueMinds

How to validate your models?

One of the most common approaches is splitting data into train, validation and test parts.

Models are trained on train data, hyperparameters (for example early stopping) are selected based on the validation data, the final measurement is done on test dataset.

Another approach is cross-validation: split dataset into K folds and each time train models on training folds and measure the performance on the validation folds.

Also you could combine these approaches: make a test/holdout dataset and do cross-validation on the rest of the data. The final quality is measured on test dataset.

How do you typically validate a machine learning model?

1. Train-test split
2. Cross-validation
3. Holdout validation
4. Bootstrap methods
5. Other (please specify in comments) 👇👇

Is accuracy always a good metric?

Accuracy is not a good performance metric when there is imbalance in the dataset. For example, in binary classification with 95% of A class and 5% of B class, a constant prediction of A class would have an accuracy of 95%. In case of imbalance dataset, we need to choose Precision, recall, or F1 Score depending on the problem we are trying to solve.

What are precision, recall, and F1-score?

Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).

Where TP is true positives, FP is false positives and FN is false negatives

In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.

F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.

What is your go-to tool or library for data visualization?

1. Matplotlib
2. Seaborn
3. Plotly
4. ggplot (in R)
5. Tableau

If you prefer a different tool, share it in the comments below! 👇👇

Which of the following is NOT a supervised learning algorithm?

A. Decision Trees
B. K-Means Clustering
C. Support Vector Machines
D. Linear Regression


Comment your answer 👇👇

The correct answer is:

B. K-Means Clustering

K-Means Clustering is an unsupervised learning algorithm, whereas Decision Trees, Support Vector Machines, and Linear Regression are all supervised learning algorithms.

How do you typically evaluate the performance of your machine learning models?

1. Accuracy
2. Precision and recall
3. F1-score
4. ROC-AUC curve
5. Mean Squared Error (MSE)

Share your preferred metrics or methods in the comments below! 👇👇

What is your favorite machine learning algorithm and why?

Share your thoughts below! 👇

Which evaluation metric is most appropriate for imbalanced classification tasks where detecting positive cases is crucial?

A. Accuracy
B. Precision
C. F1-score
D. ROC-AUC score

Choose the correct answer!

Last question was little tricky!

The correct answer is B. Precision. Congrats to all those who answered correctly

In imbalanced classification tasks, where one class (usually the minority class) is significantly less frequent than the other, accuracy can be misleading because it tends to favor the majority class. Precision, on the other hand, measures the proportion of true positive predictions among all positive predictions made by the model. It is particularly important in scenarios where correctly identifying positive cases (such as detecting fraud or diseases) is crucial, and false positives need to be minimized.

It focuses on the accuracy of positive predictions, making it a more suitable metric than accuracy for imbalanced datasets where the positive class is of interest.

What is the most exciting application of artificial intelligence in your opinion?

Share your thoughts below! 👇

Choosing the right chart type can make or break your data story. Today’s tip: Use bar charts for comparisons. Use Line Chart For WoW, MoM, YoY Analysis. What’s your go-to chart?

9 Distance Metrics used in Data Science & Machine Learning.

In data science, distance measures are crucial for various tasks such as clustering, classification, and regression. Below are nine commonly used distance methods:

1. Euclidean Distance:
This measures the straight-line distance between two points in space, similar to measuring with a ruler.

2. Manhattan Distance (L1 Norm):
This distance is calculated by summing the absolute differences between the coordinates of the points, similar to navigating a grid-like city layout.

3. Minkowski Distance:
A general form of distance measurement that includes both Euclidean and Manhattan distances as special cases, depending on a parameter.

4. Chebyshev Distance:
This measures the maximum absolute difference between coordinates of the points, akin to the greatest difference along any dimension.

5. Cosine Similarity:
This assesses how similar two vectors are based on the angle between them, used to measure similarity rather than distance. For distance, it's often inverted.

6. Hamming Distance:
This counts the number of positions at which corresponding symbols differ, commonly used for comparing strings or binary data.

7. Jaccard Distance:
This measures the dissimilarity between two sets by comparing the size of their intersection relative to their union.

8. Mahalanobis Distance:
This measures the distance between a point and a distribution, accounting for correlations among variables, making it useful for multivariate data.

9. Bray-Curtis Distance:
This measures dissimilarity between two samples based on the differences in counts or proportions, often used in ecological and environmental studies.

These distance measures are essential tools in data science for tasks such as clustering, classification, and pattern recognition.

What is your preferred method for handling imbalanced datasets in machine learning?

1. Resampling techniques (oversampling/undersampling)
2. Synthetic data generation (SMOTE, ADASYN)
3. Algorithm-specific techniques (class weights, cost-sensitive learning)
4. Ensemble methods (bagging, boosting)
5. Other (share your approach in the comments below!) 👇👇

In today’s world,

it’s crucial to focus on leading technologies like full-stack development or AI/ML.

However, many students are just copying projects instead of learning. To succeed,

it’s important to work on real, hands-on projects and truly understand the concepts.