22 Machine Learning Projects That Will Make You A God At Data Science

22 Machine Learning Projects That Will Make You A GOD At Data Science

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I wish I knew about these 22 machine learning projects when I started! Moving beyond basic Titanic datasets, I’ll show you how to build everything from beginner-friendly Iris classifiers to advanced distributed ML systems. Each project is rated for difficulty, resume value, learning potential, and real-world impact – everything you need to build an impressive ML portfolio. Keywords: machine learning projects, portfolio projects, data science, ML engineering, AutoML, computer vision, NLP, MLOps.

Also Watch:
Learn Machine Learning Like a GENIUS and Not Waste Time https://youtu.be/qNxrPri1V0I
All Machine Learning Beginner Mistakes explained in 17 Min https://youtu.be/oMc9StPVzOU
All Machine Learning Concepts Explained in 22 Minutes https://youtu.be/Fa_V9fP2tpU
All Machine Learning algorithms explained in 17 min https://youtu.be/E0Hmnixke2g
The Math that make Machine Learning easy (and how you can learn it) https://youtu.be/wOTFGRSUQ6Q
15 Machine Learning Lessons I Wish I Knew Earlier https://youtu.be/espQDESe07w

Machine Learning Playlist: https://www.youtube.com/playlist?list=PLbdTl8vSSyUDAvDPc1r3j9itciu_kb5vG

Machine Learning & AI in 100 seconds: https://www.youtube.com/playlist?list=PLbdTl8vSSyUDWtx6ZRnfzU3jo0Kpd9CxX

Git/Github Playlist:

Python Tutorials for Beginner Data Scientists:https://www.youtube.com/playlist?list=PLbdTl8vSSyUAJid3yaBjqcMrvLwhcM6vf

AWESOME github repos that help you get started:
https://github.com/data-flair/machine-learning-projects
https://github.com/shsarv/Machine-Learning-Projects
https://www.projectpro.io/article/machine-learning-projects-on-github/465
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
https://github.com/gimseng/99-ML-Learning-Projects
https://github.com/aswintechguy/Machine-Learning-Projects
https://github.com/practical-tutorials/project-based-learning
https://github.com/NirantK/awesome-project-ideas
https://github.com/Nyandwi/machine_learning_complete
https://github.com/recodehive/machine-learning-repos
https://github.com/danistefanovic/build-your-own-x

================== Timestamps ================
00:00 – Intro
00:54 – Exploratory Data Analysis (EDA) Portfolio (Beginner)
01:40 – Iris Flower Classification (Beginner)
02:49 – Build Your Own Linear Regression (Intermediate)
03:26 – Titanic Survival Prediction (Beginner)
04:07 – Housing Price Predictor (Beginner)
04:47 – Image Classification System (Intermediate)
05:29 – Sentiment Analysis System (Intermediate)
06:07 – Customer Churn Predictor (Beginner)
06:43 – Stock Price Predictor (Intermediate)
07:14 – Build Your Own Neural Network (Advanced)
07:49 – Real-time Face Recognition System (Advanced)
08:29 – Recommendation System (Intermediate)
08:54 – Automated ML Pipeline (Advanced)
09:21 – Language Model From Scratch (Advanced)
09:50 – A/B Testing Framework (Advanced)
10:21 – Image Generation System (Advanced)
10:49 – Multi-language NLP Pipeline (Advanced)
11:14 – Reinforcement Learning Game AI (Advanced)
11:38 – Real-time Fraud Detection System (Advanced)
12:10 – Build Your Own AutoML (Advanced)
12:38 – MLOps Pipeline (Advanced)
13:06 – Distributed ML System (Advanced)
you know what’s both awesome and terrifying about machine learning you can basically teach computers to do anything but here’s the problem most people get stuck building the same boring projects over and over you know that Titanic data set that everyone and their grandma has used you get that late night motivation where you’re ready to build the next chat GPT killer but then you end up staring at your screen wondering where to start don’t worry though I’ve got you covered with 22 machine learning projects that go from Total beginner to AI wizard and no they’re not all just sentiment analysis projects they will be more or less in order but not fully to keep you on your toes and not at all for my Channel’s watch time before we start I’ll rate each project on four things how difficult it is how impressive it looks on your resume how much you’ll actually learn in real world impact I will also leave some GitHub links in the description none of this is super original I just wanted to make my own list exploratory data analysis portfolio yeah we’re starting with the basics but stay with me think of as your machine Learning Foundation it’s like going to the gym everyone wants to lift the heavy weights right away but you got to start with the fundamentals I’ll rate this a two out of 10 for difficulty because it’s basically just making pretty charts but make them really pretty please a three out of 10 for resume worthy because while everyone has one a good Eda portfolio still stands out especially if you apply to more business leaning positions like data analytics learning value a solid five out of 10 because you’ll understand what data scientists actually do 80% of the time cleaning messy data and making charts that business people can understand impact four out of 10 because these skills apply to every project you’ll ever do and you can show it to your mom Iris flower classification the hello world of machine learning now I know what you’re thinking why are we getting excited about flowers but trust me this project is a ride of passage in the ml world it’s like learning to ride a bike but instead of falling off you’re just going to mix up some virginas and versy colors you’ll learn how to use different algorithms like decision trees random forests and support Vector machines plus you’ll figure out which one works best spoiler they all work pretty well on this data set and that’s why we start with it the best part this data set is clean and simple no missing values no weird outliers I wish all data sets were this nice I’ll rate this a 2 out of 10 for difficulty because it’s about as easy as it gets an ml two out of 10 for resume value because everyone’s done it but hey you got to start somewhere 6 out of 10 for learning value because you’ll understand the fun of classification and three out of 10 for impact unless you’re really into botony Quick tip don’t skip the visualization part plot those features make some Scatter Plots it’s one of the few data sets

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