#Data Sets

🔍 Ever wondered what types of data sets you need to train a neural network effectively? Let’s dive into the essentials in this reel! Join our upcoming AI & DataScience cohort at @aifolksorg 🔥 #NeuralNetwork #MachineLearning #AI #DataScience #TrainingData #SupervisedLearning #UnsupervisedLearning #DeepLearning #DataSets #TechEducation [types of datasets, training data, neural network, supervised learning, unsupervised learning, machine learning, AI, data science, image data, text data, time-series data, aifolks, OpenBootcamp ]

Comment “project” for my full video that breaks each of these projects down in detail with examples from my own work. If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude! Here are the 3 types of projects you need: ↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables ↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one. ↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it Save this video for later + send to a data friend!

Types of Data Structure . Video by @codingwithjd . . . #coding #cppproject #cplusplusprogramming #codinglife #codingbootcamp #codingisfun #codingninjas #coder #coderlife #coderslife #codersofinstagram #programming #programmingproblems #programmers #codingdays #codingchallenge #assembly #instagramgrowth #asciiart #cmd #cmdprompt #batchprocessing #aiartcommunity #artificialintelligence #deepseek #openai #meta #metaverse

1. Look at the big picture. 2. Get the data. 3. Explore and visualize the data to gain insights. 4. Prepare the data for machine learning algorithms. 5,. Select a model and train it. 6. Fine-tune your model. 7. Present your solution. 8. Launch, monitor, and maintain your systen. The Unbelievable Perks Exclusive Instagram Subscribers🔻 ➡️ Resume review & ATS Editable Resume Template ➡️ Priority replys ➡️ Exclusive QA ➡️ Job Postings ➡️ MIT + Stanford notes ➡️ Data Science Masterclass PDF Notes ⭐️ And many more just for Rs.45/month #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp

Luke Belmar On Elon Musk | Luke Belmar #LukeBelmar #ElonMusk #Datasets #Tesla #Cobalt

AI in with ai out #garbage #datasets

I won’t be mad if you copy this entire roadmap… #dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python

Top 3 data visualization packages 🤔 1️⃣ https://matplotlib.org 2️⃣ https://seaborn.pydata.org 3️⃣ https://ggplot2.tidyverse.org Being able to visualize and tell the story is a key component of being a data scientist. With these 3 packages you can pretty much create any plot you can think of! Not only that, but these packages aren’t actually that bad to learn! There are many other packages out there for data visualization as well, but these are my 3 favorites! Drop a follow for more coding tips 🎯 #code #coding #datascience #tech #python

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

Learn Tableau: ✨Templates✨ #tableau #tutorial #data #analytics #tech #datavisualization #mavenanalytics

Day 3: Importing Data into Power BI (+ importing data from the web!) #dataanalyst #dataanalysis #dataanalytics #powerbi #powerquery

Comment “LINK” to get links! 🚀 Want to learn database design in a way that actually sticks? This mini roadmap takes you from beginner fundamentals to designing production ready schemas you can confidently use in real apps. 🎓 Idea to Prod DB Perfect starting point if you are new to database design. You will understand how to go from a product idea to a clean data model, how to identify entities and relationships, and how to avoid common beginner mistakes. Great for learning the basics of schema thinking, constraints and tradeoffs. 📘 DBs in Depth Now deepen your understanding. This resource helps you build a strong mental model for how databases actually work under the hood. You will learn core concepts like indexing, query planning, transactions, isolation levels and normalization vs denormalization so you stop guessing and start designing with confidence. 💻 DB Design Course Time to go end to end. You will apply what you learned by designing schemas for real world features like users, payments, orders and analytics. You will learn how to model one to many and many to many relationships, choose data types, set keys and constraints, and prepare your database for real production workflows. 💡 With these database resources you will: Design clean schemas that scale with your product Understand normalization, indexes and transaction safety Build portfolio ready backend projects with production style database design If you are serious about backend engineering, system design interviews or building real products, database design is a must have skill. 📌 Save this post so you do not lose the roadmap. 💬 Comment “LINK” and I will send you all the links. 👉 Follow for more content on databases, backend engineering and system design.