Daily Dose of Data Science
“Daily Dose of Data Science” provides insights into the practical applications of Python in various fields like AI, machine learning, and web development. The content covers topics such as data augmentation for machine learning, Python programming for web3 and blockchain, and the importance of short-term memory in AI applications. It delves into the challenges of generative AI, the significance of understanding big data, and the use of tools like Spark, EMR, and Airflow in building secure and efficient systems. The document source offers a comprehensive guide to leveraging Python for data science and engineering innovative solutions.
Popular Interview Question: PCA vs. t-SNE
Comparing both algorithms on six parameters.
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Loss Function of 16 ML Algos
An Algorithm-wise summary of loss functions.
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Transform Decision Tree into Matrix Operations.
Make classical ML models deployment friendly.
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Why Prefer Mahalanobis Distance Over Euclidean distance?
Euclidean distance is not always an ideal choice.
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KMeans vs. Gaussian Mixture Models
Addressing the major limitation of KMeans.
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Correlation != Predictiveness
Here’s how to measure predictiveness.
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How You Can Simplify Cloud Development with Winglang?
An ecosystem dedicated to cloud.
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10 Ways to Declare Type Hints in Python
Must-know for Python programmers.
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Is Your Model Data Deficient?
More data may not always help.
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Automatically Profile Pandas DataFrame with AutoProfiler
...without writing any redundant code.
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When is Random Splitting Fatal for ML Models?
Here's when to avoid it.
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11 Powerful Techniques to Supercharge Your ML Models
Take your ML models to the next level.
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