Data science/ML/AI

Implementing DBSCAN in Python DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based unsupervised learning algorithm. It computes nearest neighbor graphs to find arbitrary-shaped clusters and outliers. Whereas the K-means clustering generates spherical-shaped clusters. Learn more about working with it in this article Link

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Efficient Python Tricks and Tools for Data Scientists - By Khuyen Tra GithubRepo : https://github.com/khuyentran1401/Efficient_Python_tricks_and_tools_for_data_scientists Stars ⭐️: 675 Forked By: 202

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Types of Regression Analysis in Machine Learning If you are looking to dive deeper into Regression Analysis for Machine Learning and understand how to choose the right type of regression analysis model for your project, here's an article that can help. Link: https://www.projectpro.io/article/types-of-regression-analysis-in-machine-learning/410

👉Here's an amazing self explanatory infographics that depicts the SQL Join clause with each category quite easily. 📍Types of joins used very often includes - ✔️LEFT JOIN - All data from the left table but common data from the right table ✔️RIGHT JOIN - All data from right table and common data from the left table ✔️INNER JOIN - Only common data from both the tables ✔️OUTER JOIN - All the data from both the tables keeping null values with no common keys ✔️UNION - Stack table data on top of one another ✔️CROSS JOIN - All possible combinations of data from both the tables

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A WELL CONCISED INTRODUCTION TO REINFORCEMENT LEARNING Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward. This article will guide you through understanding RL and it's applications. Link: Read Me👀 What you will learn: 👌How RL Works 👌Examples of RL 👌Benefits of RL 👌Challenges of RL 👌Future of RL

Harvard University Data Science Course 2021 Link: https://github.com/Harvard-IACS/2021-CS109A/tree/master/content

How to Write a Great Data Science Resume Writing a resume for data science job applications is rarely a fun task, but it is a necessary evil. The majority of companies require a resume in order to apply to any of their open jobs, and a resume is often the first layer of the process in getting past the “Gatekeeper” — the recruiter or hiring manager. Link: https://www.dataquest.io/blog/how-data-science-resume-cv/

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Importance of Theory in Data Science While there are many resources covering the theoretical foundations of data science concepts, few demonstrate why having these foundations is practically important. This article gives four examples illustrating why it’s crucial for a data scientist to know what they’re doing Link: https://towardsdatascience.com/the-importance-of-theory-in-data-science-3487b4e93953

How To Use Tableau and Python TabPy (the Tableau Python Server) is an Analytics Extension implementation that expands Tableau’s capabilities by allowing users to execute Python scripts and saved functions via Tableau’s table calculations. You can learn more about it in this article Link: https://medium.datadriveninvestor.com/introducing-tabpy-tableau-python-e812bf3f2632

Silhouette coefficient: A score from -1 to 1 describing the clusters found during modeling. A score near zero indicates overlapping clusters, and scores less than zero indicate data points assigned to incorrect clusters. A Stop words: A list of words removed by natural language processing tools when building your dataset. There is no single universal list of stop words used by all-natural language processing tools. In supervised learning, every training sample from the dataset has a corresponding label or output value associated with it. As a result, the algorithm learns to predict labels or output values. Test dataset: The data withheld from the model during training, which is used to test how well your model will generalize to new data. Training dataset: The data on which the model will be trained. Most of your data will be here. Transformer: A more modern replacement for RNN/LSTMs, the transformer architecture enables training over larger datasets involving sequences of data. In unlabeled data, you don't need to provide the model with any kind of label or solution while the model is being trained. In unsupervised learning, there are no labels for the training data. A machine learning algorithm tries to learn the underlying patterns or distributions that govern the data.