Top Tweets for #100daysofMachineLearning
Day 16 of #30dayscoding named #GandFadCoding
- complete 5 topics of python @Hiteshdotcom and @piyushgarg_dev
- also learning #100DaysofMachineLearning by CampusX watched day 15 video
This was Day 16 of my #GandFadCoding
#100DaysOfMachineLearning Day 3 Got to know about logistic regression
#100DaysOfMachineLearning Day 2 Got to know about tensors.
#100DaysOfMachineLearning Machine Learning Day 1 on https://t.co/Zqotga7FH4
Learning ML alone is hard.
Learning ML together is fun 🚀
I’m looking for people to collaborate on ML projects as part of #100DaysOfMachineLearning.
If you’re building, learning, or just curious — let’s connect 🤝
Comment “ML” or DM me.
#MLTwitter #DataScienceCommunity #AI
🧠 Offline vs Online Machine Learning — in 1 minute
Offline ML = Train once on past data
Online ML = Learn continuously from live data
Same goal. Very different approach.
Part of my #100DaysOfMachineLearning 🚀
👇 Details in comments
#MachineLearning #AI #DataScience #LearnIn
Day 17 of #100DaysOfMachineLearning
Today I learned about optimizers.
Gradient descent tells a model where to go.
Optimizers decide how fast and how smoothly it gets there.
Momentum, RMSProp, Adam, AdamW
All built to make learning faster and more stable.
Free to read 👇
https://t.co/uSgepJy5x0
#MachineLearning #DataScience #AI #DeepLearning #MLForBeginners
Day 16 of #100DaysOfMachineLearning
Today’s topic: Gradient Descent
This is how machines actually learn:
make a prediction → measure the error → move parameters slightly → repeat.
The learning rate decides how big each step is.
Too small and training is slow.
Too big and training breaks.
📖 Free to read:
https://t.co/dPtUlmwMnb
#MachineLearning #AI #DataScience #DeepLearning #MLForBeginners #GradientDescent #TechLearning
Day 12 of #100DaysOfMachineLearning
Today I learned about Overfitting and Underfitting — two problems that can make or break a model.
Underfitting happens when the model learns too little.
Overfitting happens when it learns too much.
The real goal is balance: a model that generalizes well on new data.
📖 Free to read:
https://t.co/OE714MkFh3
#MachineLearning #DataScience #AI #DeepLearning #TechLearning #MLForBeginners
Day 11 of #100DaysOfMachineLearning
Today I covered one of the most important steps in ML:
Data Preprocessing and Feature Engineering.
Clean data matters more than complex models.
Handling missing values, encoding categories, scaling features, and engineering new ones can transform your results.
📖 Full article (free):
https://t.co/KBHEzubOgW
#MachineLearning #DataScience #AI #MLForBeginners #FeatureEngineering #DataPreprocessing #DeepLearning #AIExplained #TechLearning
Day 9 of #100DaysOfMachineLearning 🧠
Today’s topic: Classification — how AI learns to make decisions, not just predictions.
From spam filters to fraud detection to facial recognition — classification helps machines separate data into categories based on patterns.
📖 Free to read:
👉 https://t.co/XQ5oFcOiV2
#MachineLearning #AI #DataScience #DeepLearning #AIExplained #MLForBeginners #AITrends2025 #AICommunity #Classification #TechLearning
Day 74:
✅ Explored the foundation of gradient-free learning in Tangled Program Graphs
✅ 60-minute walk and dance💃🏻🔋
#100DaysofMachineLearning #AI
Day 30 of #100DaysOfMachineLearning
I completed Exploratory Data Analysis. It included
-> EDA in python
-> Advance EDA
->Time Series Data Visualization.
Off to Model Evaluation next..
Finished my MERN full-stack journey and ready for the next big leap 🚀 Starting #100DaysOfMachineLearning with @CampusX 🤖
From building websites to building smart systems… let’s see where this takes me! 🙌
Who’s learning ML too? Let’s connect!
#AI #ML #CampusX #LearningTogether
Week 3 of my #100DaysOfMachineLearning has been intense!
From Day 15 to Day 22, these are some topics that I did:
1. Explored Simple Linear Regression – understanding how one feature can predict an outcome.
2. Moved to Multiple Linear Regression – where things get more real.
Day 14 of #100DaysOfMachineLearning
Completed these within the last few days-
1. Complete case analysis
2. Arbitrary value imputation
3. Missing categorical value
4. Automatically select imputer parameters
5. KNN Imputer
6. Outlier removal using Z Score
Day 13 of #100DaysOfMachineLearning
Here’s what I coded in the past few days:
1. Handling missing categorical data
2. Doing a complete case analysis (basically dropping rows with missing values)
3.Trying out arbitrary value imputation
Day 12 of #100DaysOfMachineLearning
I did these topics within the last 3 days:
1. From Statistics, I did- probability distribution function (pdf, pmf, cdf) and Normal distribution.
2. I did 6 cases of handling missing data using:
Day 11 of #100DaysOfMachineLearning
I started diving into maths for machine learning. I've always heard that Statistics is a non-negotiable for machine learning.
Although was never particularly a very big fan of this subject, but I did these topics under Descriptive Statistics
Day 10 of #100DaysOfMachineLearning
So, I coded the stuff learnt the previous day( column Transformer, sklearn Pipelines, function transformer, power transformer).
I started the theory of machine learning from geeksforgeeks too. I really like this site for concept clarity.
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