Data science/ML/AI

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Understanding and Handling Overfitting When a machine learning method fits the Training Data really well but makes poor predictions, we say that it is Overfit to the Training Data. Let's try to undertstand how this occurs and how to handle it, 1) Overfitting in Machine Learning Link 2) Overfitting and Underfitting With Machine Learning Algorithms Link 3) Overfitting by IBM Link 4) The Complete Guide on Overfitting and Underfitting in Machine Learning Link

Huge collection of Data Science materials By Harvard You will find lecture notes/Notebooks for every data science/machine learning topic you heard about 🤯 https://harvard-iacs.github.io/2019-CS109A/pages/materials.html

I understand data science is not all about programming but, as far as I know, Python comes into play to some extent on this matter. How much should I know about programming to do data science? This question was asked earlier today by one our community member in our main channel @bigdataspecialist. I decided to share my answer here since it might be interesting to some of you and I am pretty sure great majority of you haven't even noticed his question/my answer. TL:DR Programming is important, but you don't have to be an expert. You just need some basic to intermediate skills to prepare your data (which you are going to use in data science tasks), possibly make some data visualizations to gain insights and at the end to create your machine learning models. These basic skills could probably be gained in a month, especially if you are not complete newbie who has never heard of programming 😅 You can get mentioned skills from this course: https://www.coursera.org/learn/python-data-analysis Teacher is Christopher Brooks and course is created by University of Michigan. Note: I know it says its paid one, buy you can apply for financial aid and get course for free. That's how I got this course when I just started learning data science. Keep in mind that Python is not only programming language which comes to mind when you think about doing data science. For example, for almost all data science and machine learning tasks, I use Java. It's very specific and usually data scientists don't do that, but platform developed by my company is receiving 4k requests per second, so we need something blazing fast, and Python is pretty slow. That's why we use Java. But if I am going to test something locally, or I need some easy data preparation or data visualizations, I use Python. Creating charts to gain some insights would be real nightmare with Java. But for you as a beginner Python is probably best choice Long story short: If you want it fast and easy, python is way to go IF you want it very fast (but probably pretty hard to make it work) - Java. If you want to perform advanced calculations and visualizations - R If you want to show your visualizations dynamically on some web page, then certain JavaScript libraries like D3.js or chart.js. Hope this helps. ➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

The StatQuest Illustrated Guide to Machine Learning Author: Josh Starmer Phd Pages: 305 Book Link: Read Me

Data Cleaning Checklist Data cleaning takes up 80% of the data science workflow. Use this checklist to identify and resolve any quality issues with your data. Link

GOOGLE CLOUD FREE MACHINE LEARNING AND AI COURSE Learn how to implement the latest machine learning and artificial intelligence technology by exploring training on BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more what you will learn: 👌Big Data & Machine Learning Fundamentals 👌Perform Foundational Data, ML, and AI Tasks in Google Cloud 👌Machine Learning on Google Cloud 👌Advanced Machine Learning with TensorFlow on Google Cloud Platform 👌MLOps (Machine Learning Operations) Fundamentals 👌ML Pipelines on Google Cloud 👌Build and Deploy Machine Learning Solutions on Vertex AI 👌Create Conversational AI Agents with Dialogflow CX https://cloud.google.com/training/machinelearning-ai

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Machine Learning YouTube Courses This repo contains some of the best and most recent machine learning courses available on YouTube. Creator: dair-ai Stars⭐️: 8.1k Fork: 977 Repo Link: Click Me

Understanding The Structure of Your Data 1) Univariate Visualization: This visualization is used to gain a summary statistics of each feature in your dataset. The goal of univariate visualization is to have a solid understanding of the data in order to start querying and visualizing our data in various ways. It uses tools such as barplots and histograms to reveal the structure of the data. 2) Bivariate Visualization: This visualization is used when you need to find relationships between two variables in your dataset where one of the variable could be the target variable. It uses correlations, scatter plots and line plots to reveal structure of the data. 3) Multivariate Visualization: This is employed to understand interactions between different fields in the dataset. It uses line plots, scatter plots, and matrices with multiple colors to understand the relationship between various features of a dataset.

Free Machine Learning Course Learn ML engineering in 4 months in a free online course by Al_Grigor from DataTalksClub What you will learn: - Linear and logistic regression - Tree-based models - Neural networks - Deployment with AWS, Serverless, Kubernetes Register here: Link

Multilingual NLI Dataset Moritz Laurer, a PhD researcher working with NLP at Vrije Univeristy of Amsterdam, announced on his Twitter account that new multilingual dataset is ready! New dataset contains 2 730 000 NLI text pairs in 26 languages. It was created from previous English dataset using the latest open-source machine translation model. The dataset can be loaded here.

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