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Top 8 Machine Learning Algorithms

Data Science Dojo

By understanding machine learning algorithms, you can appreciate the power of this technology and how it’s changing the world around you! Let’s unravel the technicalities behind this technique: The Core Function: Regression algorithms learn from labeled data , similar to classification.

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Can CatBoost with Cross-Validation Handle Student Engagement Data with Ease?

Towards AI

This story explores CatBoost, a powerful machine-learning algorithm that handles both categorical and numerical data easily. CatBoost is a powerful, gradient-boosting algorithm designed to handle categorical data effectively. CatBoost is part of the gradient boosting family, alongside well-known algorithms like XGBoost and LightGBM.

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Top 17 trending interview questions for AI Scientists

Data Science Dojo

Business Benefits: Organizations are recognizing the value of AI and data science in improving decision-making, enhancing customer experiences, and gaining a competitive edge An AI research scientist acts as a visionary, bridging the gap between human intelligence and machine capabilities. Privacy: Protecting user privacy and data security.

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Text Classification in NLP using Cross Validation and BERT

Mlearning.ai

Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 11 Model Architecture The algorithms and models used for the first three classifiers are essentially the same. In addition, each tree in the forest is made up of a random selection of the best attributes.

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Unlocking Predictive Power: How Bayes’ Theorem Fuels Naive Bayes Algorithm to Solve Real-World…

Mlearning.ai

Unlocking Predictive Power: How Bayes’ Theorem Fuels Naive Bayes Algorithm to Solve Real-World Problems [link] Introduction In the constantly shifting realm of machine learning, we can see that many intricate algorithms are rooted in the fundamental principles of statistics and probability. Take the Naive Bayes algorithm, for example.

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Meet the finalists of the Pushback to the Future Challenge

DrivenData Labs

Several additional approaches were attempted but deprioritized or entirely eliminated from the final workflow due to lack of positive impact on the validation MAE. We chose to compete in this challenge primarily to gain experience in the implementation of machine learning algorithms for data science.

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Tree-Based Models in Machine Learning

Mlearning.ai

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. So buckle up!