Staying consistent isn't as easy as it looks... 🙏
#BuildInPublic #100DaysOfML
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Day 11/100 of #100DaysOfML ✅
Learned Feature Scaling today.
Covered:
• Z-score scaling
• StandardScaler
• Impact on Logistic Regression
• When scaling is needed (KNN, K-Means, Neural Nets, PCA)
• When it isn't (Tree-based models)
🔗Notebook:
github.com/sadaaizaz/Machine…
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Jun 13
Day 10/100 of #100DaysOfML ✅
Submitted my college hackathon project today
Also continued my ML journey by learning Feature Scaling and why it's important for improving model performance and training efficiency.
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Jun 13
I was very inconsistent with my ML journey. Starting today, consistency becomes non-negotiable. Small steps, every single day. #MachineLearning #100DaysOfML #AI
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100 Days of ML | Day 35 🚀
Learned about Handling Missing Data — Complete Case Analysis today. 📊
Complete Case Analysis simply removes rows that contain missing values.
✅ Easy to implement
✅ Works well when missing data is minimal
#100DaysOfML
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Jun 12
Day 9/100 of #100DaysOfML ✅
Continued working on the college hackathon project today and finally completed the core codebase.
From building features to debugging and integration, it's satisfying to see everything come together. Next up: testing, polishing, and deployment.
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Jun 11
🚀 Day 47
Built an Employee Attrition Prediction model using Logistic Regression with L1 (Lasso) and L2 (Ridge) regularization to reduce overfitting.
#MachineLearning #DataScience #LogisticRegression #Lasso #Ridge #Python #AI #100DaysOfML #Day47
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Jun 11
#100DaysOfCode/#100DaysOfML day 94
ML - gradient descent for logistic regression and its formula derivation and some eda project work.
DSA - solved 'validate bst'(brute force)
#LearnInPublic #DSA
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Jun 11
Day 8/100 of #100DaysOfML
Working on a college hackathon.
It’s a great opportunity to apply everything I’ve been learning so far - data handling, feature engineering, and model thinking - on a real problem statement.
Excited to build and learn:)
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Jun 11
🚀 Solved the Merge Strings Alternately problem on LeetCode today and got familiar with the Machine Learning Development Life Cycle. Learning and improving step by step! 📚💻
#LeetCode #MachineLearning #100DaysOfML #LearningInPublic
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Jun 11
I wanted to learn applied ai rather than core ML, my friend @riyasayshie is following 100daysofML from campusX for in detail concepts of ML u can ask her if u have qs
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Jun 10
🚀 Day 46 of My ML Journey
Learned Normalization, Confusion Matrix, Precision, Recall, F1-Score, and Variance—key concepts for evaluating models and understanding performance.
#MachineLearning #DataScience #AI #Normalization #ConfusionMatrix #LearningInPublic #100DaysOfML
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Jun 10
#100DaysOfCode/#100DaysOfML day 93
ML - loss and cost function for logistic regression and its simplified version.
DSA - inorder successor/predecessor in bst, deleting a node in bst.
Gym - back, biceps
#LearnInPublic #DSA
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Jun 10
📚 Revised the fundamentals of Machine Learning today. Strengthening the basics one step at a time. 🚀
#MachineLearning #100DaysOfML #LearningInPublic
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Day 3/100 of Learning Machine Learning in Depth 🚀
Focused today on the fundamentals:
• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Engineering
Taking time to revise and truly understand these core concepts before moving ahead.
#MachineLearning #100DaysOfML
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Jun 10
Day 7/100 of #100DaysOfML ✅
Learned Pandas Profiling for fast EDA and explored Feature Engineering:
• Missing Value Imputation
• Encoding
• Outlier Handling
• Feature Scaling
• Feature Construction
• Feature Selection
• PCA
🔗Notebook:
github.com/sadaaizaz/Machine…
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Jun 9
#100DaysOfCode/#100DaysOfML day 92
ML - decision boundary for logistric regression
DSA -tried "delete a node in bst"
(again spent a full day(10am-10:30pm) at my fathers shop, and honestly can't blame anyone for still not being able to get out of this place)
#LearnInPublic #DSA
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Jun 9
Day 6/100 of #100DaysOfML ✅
Learned how to understand and explore data before modeling.
📊 Covered:
• Understanding different types of data
• Univariate EDA
• Bivariate & Multivariate EDA
• Finding patterns and relationships in data
🔗Notebooks:
github.com/sadaaizaz/Machine…
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100 Days of ML | Day 34 🚀
Learned about Handling Date & Time Variables in Machine Learning. 📅
Dates contain more information than they seem:
• Day, Month, Year
• Weekday vs Weekend
• Quarter, Season
• Time differences & durations
#100DaysOfML
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Day 2/100 of Learning Machine Learning in Depth 🚀
📚 Topics covered today:
• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Engineering
Tomorrow, I'll start diving into Machine Learning algorithms.
#MachineLearning #100DaysOfML #LearnInPublic #DataScience
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