#Day2 #100daysofMachineLearning Today completed 4 videos of 100 days of ML Learning. Which I already done in the past. So revised it for the upcoming videos and clear concept. With Ongoing pressure still need to do it,as we are Software Engineer #Buildinpublic #CampusX

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

About my #GandFadCoding i didn't updated the process here is what i did last 9 days Day 8 - 15 of #30dayscoding named #GandFadCoding - Completed the back-end series of @Hiteshdotcom - Started with Full stack generative and Agentic AI with python by @Hiteshdotcom and @piyushgarg_dev - also learning #100DaysofMachineLearning by CampusX This was Day 8 - 15 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 thinktube.in

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 👇 medium.com/next-gen-cloud-jo… #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: medium.com/next-gen-cloud-jo… #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: medium.com/@ShubhamVerma28/d… #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): medium.com/@ShubhamVerma28/d… #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: 👉 medium.com/@ShubhamVerma28/d… #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: