Source codes for data science projects from my Instagram post:
https://www.instagram.com/p/CJwDIpCA0nc/

1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro

2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python

3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/

4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/

5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/

6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/

7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/

#data_science #projects
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CS109 Data Science
By Harvard University

⌛️ 12 weeks
✅ Video lectures
✅ Slides
✅ Lab exercises

🔗 http://cs109.github.io/2015/pages/videos.html

Note: i have issues with first video link but others are fine.

#datascience #python #harvard
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Visualize data on Google Maps Platform

Learn to translate external data sources to graphics on maps.

✅ Free Online Course
🧱 4 modules
🎬 Video Lectures
🏃‍♂️ Self paced
📊 Lab: 1
🧮 Quiz
Source: Google

🔗 https://developers.google.com/learn/pathways/maps-visualize-data?hl=en

#Data_Science #Google_Map #Data_Visualization
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Python for Data Science: A Beginner’s Guide

Python is a programmer darling for plenty of reasons: the language is easy to read and work with, relatively simple to learn, and popular enough that there’s a great community and plenty of resources available.
And if you needed one more reason to consider starting Python for beginners, it plays an important role in lucrative data careers as well! Learning Python for data science or data analysis will give you a variety of useful skills.

✅ Free Online Tutorial
🧱 8 modules
🏃‍♂️ Self paced
Source: learntocodewithme

🔗 Course Link


#Data_Science #python #Python_For_Data_Science
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Data Science With Python Workflow Cheat Sheet

Creator: business Science
Stars ⭐️: 75
Forked By: 38
https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf


#Data #Science #cheatSheet
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How do Transformers work?

All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!

This type of model develops a statistical understanding of the language it has been trained on, but it’s not very useful for specific practical tasks. Because of this, the general pretrained model then goes through a process called transfer learning. During this process, the model is fine-tuned in a supervised way — that is, using human-annotated labels — on a given task

🔗 Read More

Datasets for Data Science and Machine Learning

Ten years ago, it use be years ago quite difficult to find good datasets for data science and machine learning projects. Today, we have the opposite problem.
We’ve been flooded with lists and lists of datasets. The problem nowadays is not finding datasets, but rather sifting through them to keep the relevant ones.
Well, we’ve done that for you right here.
Below, you’ll find a curated list of free datasets for data science and machine learning, organized by their use case. You’ll find both hand-picked datasets and our favorite aggregators.

✅ Exploratory Analysis
✅ General Machine Learning
✅ Deep Learning
✅ Natural Language Processing
✅ Cloud-Based Machine Learning
✅ Time Series Analysis
✅ Recommender Systems
✅ Specific Industries
✅ Streaming Data
✅ Web Scraping
✅ Current Events

🔗 Source Link


#Data_Science #python #datasets
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1000 Data Science Projects
you can run on the browser with IPython.

Explore from 1000+ ready code templates to kickstart your AI projects
⭐️Classification
⭐️Regression
⭐️Clustering

🔗 Source link

#ai #ml #data_science #deep_learning
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Google just dropped Generative AI learning path with 9 courses:
🤖: Intro to Generative AI
🤖: Large Language Models
🤖: Responsible AI
🤖: Image Generation
🤖: Encoder-Decoder
🤖: Attention Mechanism
🤖: Transformers and BERT Models
🤖: Create Image Captioning Models
🤖: Intro to Gen AI Studio
https://www.cloudskillsboost.google/paths/118

Data Science Engineering, your way

An introduction to different Data Science engineering concepts and Applications using Python and R
These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python.

We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.

To get a feeling of what is going on regarding this hot topic, we refer the reader to DataCamp's Data Science War infographic. Their infographic explores what the strengths of R are over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.

Far from being a repetition from the previous, our series of tutorials will go hands-on into how to actually perform different data science taks such as working with data frames, doing aggregations, or creating different statistical models such in the areas of supervised and unsupervised learning.

We will use real-world datasets, and we will build some real data products. This will help us to quickly transfer what we learn here to actual data analysis situations.

Link

#ai #ml #data_science
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How To Label Data

At LightTag, we create tools to annotate data for natural language processing (NLP). At its core, the process of annotating at scale is a team effort. Managing the annotation process draws on the same principles as managing any other human endeavor. You need to clearly understand what needs to be done, articulate it repeatedly to your team, give them the tools and training to execute effectively, measure their performance against your goals, and help them improve over time. we will draw on our experience with various annotation projects to describe the seven distinct stages of an annotation life cycle that Jane will go through. We will explain the purpose of each stage, describe key considerations that should occur during each, and wrap each stage up with the assets you should expect to have at the end.

Link

#ml #data_science
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Data Science Helps Engineers Discover New Materials for Solar Cells and LEDs