Top Tweets for #100DaysofML
#100DaysOfCode/#100DaysOfML day 88
ML - multiple linear regression, vectorization and gradient descent for multiple features, some eda work
DSA-
solved-> zigzag order traversal, constructing b.t from inorder and postorder traversal
Gym- back, biceps
#LearnInPublic #DSA
Day 2/100 of #100DaysOfML ✅
Today I explored:
• Applications of Machine Learning
• ML Development Life Cycle
• Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer
Building a stronger foundation before diving deeper into the technical side. 🚀
#MachineLearning #AI
#100DaysOfCode/#100DaysOfML day 87
ML - updating parameters using gradient descent, env setup and started my first eda project
DSA-
solved-> binary tree pruning, symmetric tree
tried->zigzag traversal
(which platform do you use for data analysis??)
#LearnInPublic #DSA
Day 1/100 of #100DaysOfML ✅
Covered the fundamentals today:
• What is ML?
• AI vs ML vs DL
• Types of ML
• Batch vs Online Learning
• Challenges & Applications
Onward. 🚀
#100DaysOfCode/#100DaysOfML day 86
ML -cost function, its visualization wrt the parameter values, started eda on netflix dataset.
DSA-
->Univalued binary tree
->max binary tree (O(n^2) solution but did it myself)
Gym- Legs & shoulders
#LearnInPublic #DSA
LIKE. SHARE. SUBSCRIBE
100 Days of ML | Day 31
Learned about Power Transformations for making data more Gaussian-like and improving model performance.
• Box-Cox Transform → works on positive values only
• Yeo-Johnson Transform → works with both positive and negative values
#100DaysOfML
A few months ago, I completed a 100 Days of Code challenge.
It taught me that progress doesn't come from motivation - it comes from showing up consistently.
Starting tomorrow, I'm taking on a new challenge:
100 Days of Machine Learning.
One day at a time.🚀
#100DaysOfML
The more I learn about Machine Learning, the more I realize that training the model is just one small part of the process.
There's so much happening behind the scenes with data, deployment, and making things work reliably.
Still learning, one step at a time.
#100DaysOfML
#100DaysOfCode/#100DaysOfML day 85
ML - dumped stanford CS229, started with https://t.co/vol5XJNp76 ML specialization, started linear regression
DSA - solved "Binary Tree Tilt" and "Invert Binary Tree"
Gym- Chest, triceps
#LearnInPublic #DSA
Built a Retail Demand Forecasting model using LightGBM, Trained on 5 years of data to predict daily sales across multiple stores & items
Actual vs Predicted tracking closely — model generalizes well across different store-item combos
🔗 https://t.co/LW3RiyfAd0
#100DaysOfML
day 14 was yesterday
One hot Encoder
#100DaysOfML
#100DaysOfCode/#100DaysOfML day 84
ML - started stanfords CS229 ML course
DSA - solved "same tree" problem
(spent the full day at my fathers shop, was completely drained, but my last 2 posts got 5+ likes, so I had to do it, couldn't betray my audience🧡😭)
#LearnInPublic #DSA
#100DaysOfCode/#100DaysOfML day 83
ML - completed all pending lectures, labs, assignments of the stats and prob. course and FINALLY COMPLETED THE MATHS FOR ML AND DS SPECIALIZATION COURSE SERIES
DSA - solved balanced binary tree problem
Gym - back & biceps
#LearnInPublic #DSA
#100DaysOfCode/#100DaysOfML day 82
ML - completed a programming assignment on A/B testing(things that took me weeks to study are just a one line code😭)
DSA-
solved -> count total nodes, max depth, and diameter of a binary tree
Gym- Legs & shoulders
#LearnInPublic #DSA #pandas
100 Days of ML | Day 29 🚀
Explored Machine Learning Pipelines end-to-end today. ⚙️
From preprocessing → feature transformation → model training → prediction, everything can be connected into one streamlined workflow.
#100DaysOfML
#100DaysOfCode/#100DaysOfML day 81
ML - completed a pandas eda lab on hypothesis testing and confidence intervals(chicago ridesharing dataset)
DSA - learned and solved "build a binary tree from preorder and postorder traversal"
Gym - Chest, triceps
#LearnInPublic #DSA
day 12 I think
Ordinal encoding
Use of .iloc ☝️
#100DaysOfML
#100DaysOfCode/#100DaysOfML day 80
ML - revised stats(sampling, regularization, mle, map, bayesics, etc.) and gave an assignment for these topics
DSA - tried "build a binary tree from preorder and postorder traversal", will learn the correct solution tomorrow
#LearnInPublic #DSA
#100DaysOfCode/#100DaysOfML day 79
ML - completed a lab on sampling from distributions, and one eda lab on Linear Regression using sk-learn(world happiness index dataset)
DSA -
solved-> inorder, preorder, postorder traversals in binary tree
Gym - Back, biceps
#LearnInPublic
#100DaysOfCode/#100DaysOfML day 78
ML - completed a very boring programming assignment on multiple loaded dice's probabilities and all(idts it was useful)
DSA - started binary trees.
solved-> binary tree level order traversal and reverse level order traversal
#BuildInPublic
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