Despite the structural issues, the content is fantastic. A cleaned-up PDF with proper chapter order, numbering, and missing sections added would make the reading experience as smooth and intuitive as the concepts you explain. Thank you for writing this book. @subhashchy

4. Several chapters have no Key Takeaways (13, 14, 16, 17).

2. After the Docker chapter, the entire build-up suggests Kubernetes should come next but instead, 2 unrelated chapters appear before Kubernetes. 3. Chapters appear out of order: chapter 13 chapter 16 - 238 chapter 15 - 245 all are skipped chapter 19 - 269 chapter 16 -280

1. Duplicate chapter numbers Two chapters are labelled “Chapter 10” Chapter 10: Docker (The Shipping Container Revolution) Chapter 10: The Smart Clerk - Search)

I’m on pg 252 of 'The Accidental CTO' - the content is brilliant with great analogy🩷 but the structure in the second half becomes confusing and messy, makes it difficult to read and breaking my flow. Here are all the issues I found in the current PDF 👇 @subhashchy

Day 17/100 – #100DaysOfML 🚀 - Bias : model too simple, misses patterns (underfitting) - Variance : model reacts too much to small data changes (overfitting) - BUVO: Bias Underfitting, Variance Overfitting ✅Done L1 & L2 reg @codebasicshub exercise. #MachineLearning #AI

Underfitting causes: 1. model too simple 2. bad feature engineering 3. not trained enough(less epochs) 4. excessive regularization Fix: better features, more training, more complex model

Overfitting causes: 1. too many features 2. poor model choice 3. little data 4. no validation 5. no regularization Fix: better features, more data, k-fold, apply regularization

Day 16/100 - #100DaysOfML 🚀 - Learnt how L1 (Lasso) & L2 (Ridge) regularization help reduce overfitting by penalizing large coefficients. - Also revised causes & fixes for overfitting/underfitting. Practiced Labs for it. #MachineLearning #AI

Day 15/100 – #100DaysOfML 🚀 (for my reference😅)

Missed posting for a few days, but I’m back on track! This week I learnt: - Linear Regression - Multiple Linear Regression - Polynomial Regression Today I completed the exercise & lab for Poly Reg. thanks to #campusx Linear Regression playlist. #MachineLearning #AI #codebasics

Day 14/100 – #100DaysOfML 🚀 Today I learnt: Practiced 1-0 (One-Hot) Encoding for nominal data. Reduced multicollinearity by removing one dummy column. Trained & evaluated the model after encoding. @codebasicshub #MachineLearning #AI

Day 13/100 – #100DaysOfML 🚀 Today I learnt: Applied MSE, MAE, and R2 Score to evaluate model performance. Multicollinearity : when features are highly correlated. Dummy Variable Trap in 1-0 Encoding can cause it, fix: remove 1 column. @codebasicshub #MachineLearning #AI

Day 12/100 – #100DaysOfML 🚀 Learnt about R2 score and done lil lab Ps: gonna resume my 100 day journey from today. Also i passed Oracle Foundation Ai cert exam😅 #MachineLearning #AI #oraclecertificationprogram

I’m taking a break from ML today to recover from a cold and come back refreshed tomorrow. #100DaysOfMl #ai

Day 11/100 – #100DaysOfML 🚀 Learnt why MSE > MAE for GD MSE (x²) : - Best when few outliers. - smooth, differentiable (f’(x)=2x). - GD finds minima easily. MAE (|x|) : - better with many outliers. - not smooth, undefined at 0. - harder to optimize. #MachineLearning #AI

Day 10/100 – #100DaysOfML 🚀 Learnt: Gradient Descent : finds the best-fit line by adjusting slope & intercept to reach the global minim Manually found the best-fit line using MSE and partial derivatives. min max scaling: bring features into 0–1 range. #MachineLearning #AI

My system got corrupted so evrything is delayed(currently fixing) also exam going on. Hopefully will post my progress on ml from tommorow or soon after exam.

Day 9/100 – #100DaysOfML 🚀 Yesterday i missed, because of laptop issue. Today I learnt : Confidence Intervals! - CI gives a range where the true population parameter likely lies 𝑥̄ ± Z * (σ/√n) - Wider CI -> more uncertainty, narrower CI -> more precise #MachineLearning #AI

Day 8/100 – #100DaysOfML 🚀 Today I practiced solving problems using the Z-Score Table to find probabilities under the Normal Distribution. 📊 Following @codebasicshub 📚 #MachineLearning #AI #DataScience