Identifying and correcting Label Bias in Machine Learning

As machine learning (ML) becomes more effective and widespread it is becoming more prevalent in systems with real-life impact, from loan recommendations to job application decisions. With the growing usage comes the risk of bias – biased training data could lead to biased ML algorithms, which in turn could perpetuate discrimination and bias in society.

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Data Structures and Algorithms: 20 Problem-Solving Techniques

This is the article I wish I had read when I started coding. I will dive deep into 20 problem-solving techniques that you must know to excel at your next interview.
They have helped me at work too and even given me ideas for a side project I am working on. Also, the last section includes a step-by-step guide explaining how to learn data structures and algorithms, with examples. Furthermore, I recommend you read this post, where I outlined a high-level strategy to prepare for your next coding interview as well as the top mistakes to avoid.

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20 AutoML libraries for the Data Scientists

AutoML refers to automated machine learning. It explains how the end to end process of machine learning can be automated at the organizational and educational level. Initially all these steps were done manually. The demand for machine learning is increasing day by day. Let’s see some of the most common AutoML libraries which are present in different programming languages.

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AI 1.02 — Alan Turing’s The Imitation Game, A Summary:

Briefing you on the first ever paper on AI. Turing in 1950 published the first ever article on Artificial Intelligence which he then called ‘Computing Machinery and Intelligence’. This summary/ article will give you an idea of what he wrote in world’s first ever interpretation on Artificial Intelligence in this highly philosophical paper, his views and the predictions he made on AI which stand still even today.

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Understanding the Data Analytics Life Cycle (DALC)

Industries are changed a lot since the data became the most popular ingredient to generate better insights. Be it eCommerce, Health Care, Transport-Logistic, or any. Companies are more inclined towards the data and identifying the consumer patterns to increase their revenue by investing money in different sources.

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