📘 Day 49 & 50: AI/ML Journey Spent time reviewing key concepts in Linear Regression: 🔹 Linear Regression basics 🔹 Linear Regression (Vector Form) 🔹 Training Linear Regression using the Normal Equation Steady progress! 🚀 #AI #MachineLearning #ZoomCamp #LearningInPublic

Day 47 & 48: AI/ML Journey 📘 I Spent the past 2 days completing Module 2 – Homework 2 of #MachineLearning Zoomcamp 🚗 Worked on Car Fuel Efficiency dataset: handled missing values, trained linear & regularized regression, tested seeds, and evaluated final model ✅ #AI

📘 Day 46: #AI/#ML Journey Started Module 2 – Data Prep 🚗💡 Cleaned Kaggle car price dataset w/ Pandas Standardized columns & strings Learned handling missing values, outliers & scaling Train/validation split ready ✅ Next: EDA + Linear Regression 👨‍💻 #MachineLearning

📘 Day 45: AI/ML Journey Started Module 2 – Data Preparation. Tried setting up Jupyter Notebook to practice, but it refused to work 😅. Will troubleshoot and continue tomorrow. #AI #MachineLearning #ZoomCamp #LearningInPublic

📘 Day 44: AI/ML Journey Today was fully spent on a client project, so I couldn’t study ML. Learning will continue tomorrow. It is consistency over perfection. #AI #MachineLearning

📘 Day 43: AI/ML Journey @DataTalksClub Intro to Pandas 🐼 •DataFrame & Series •Adding/deleting columns •Index & element access •Element-wise operations ✅ Building a solid foundation in data manipulation! #AI #MachineLearning #Pandas #ZoomCamp #LearningInPublic

📘 Day 42: AI/ML Journey Reviewed: Dot product → vector similarity Matrix–Vector → linear transformations Matrix–Matrix → core ML ops Implemented all in Python + NumPy ✅ Solid linear algebra foundations built! #AI #MachineLearning #ZoomCamp #LearningInPublic

📘 Day 41: AI/ML Journey Topic:Linear Algebra Refresher Today’s focus: **vector basics** ✨ 🔹 Scalar × Vector → scales each element 🔹 Vector + Vector → add components one by one These simple ops form the foundation of ML math \#AI #MachineLearning #ZoomCamp

📘 Day 40: NumPy Practice 🔹 Indexing & slicing → grab rows, cols, subarrays 🔹 Random arrays → rand, randn, randint (with seed for reproducibility) 🔹 Element-wise math → add, multiply, divide arrays 🔹 Boolean masking → filter values by condition #NumPy #Python

📘 Day 39: AI/ML Journey Started with NumPy today – the backbone of Python for ML & Data Science. 🔹 Learned: Create arrays → zeros, ones, full, array Generate sequences → arange, linspace Multi-dimensional arrays & slicing Random arrays → rand, randn, randint #AI

📘 Day 37-38: AI/ML Journey 🎢 -@DataTalksClub Set up Jupyter, Python, Pandas & NumPy for my first ML homework. Pandas gave me trouble on day 37😅 but I finally got it today-day 38. Learned: datasets & stats, matrix/array ops #AI #MachineLearning #ZoomCamp #LearningInPublic

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📘 Day 36: AI/ML Journey 🔹 Wrapped up Model Selection Process Train/validation/test split Avoid MCP (lucky models) with a final test set Steps → Split → Train → Evaluate → Select → Test 🔹 Next → Environment Setup ✅ Python 3.11, NumPy, Pandas, Sklearn, Matplotlib

Happy new week From earning ₦70k as a teacher ➝ to six figures in tech 🙏 My first design vs. my latest reminds me how far I’ve grown. Grateful for the journey Left: Where I Started Right: Where I Am Now

📘 Day 35: AI/ML Journey Today’s Progress: ML Concepts - Intro to Model Selection 🧠 Learned that models must generalize, not just memorize training data. 👉 Solution: split data to check performance on unseen examples. Didn’t dive deep today, but got the big picture ✅ #AI

📘 Day 34: AI/ML Journey @DataTalksClub 🔹 Learned about CRISP-DM (ML workflow with 6 steps: Business → Data → Prep → Model → Eval → Deploy → Iterate). 🔹 Explored Stochastic Gradient Descent (SGD) ML is more than just models, it’s a cycle 🔄 #AI

It’s the backbone of modern ML 🚀 #AI #MachineLearning #ZoomCamp #LearningInPublic

📘 📘 Day 33: Supervised ML-@DataTalksClub Supervised Learning = teaching models with labeled data (features + target). 🔹 Regression → predict numbers (price, age) 🔹 Classification → predict categories (spam/ham, cat/dog) 🔹 Ranking → score items (recommender, search)

ML scales better than rules 💡 #AI #MachineLearning #ZoomCamp #LearningInPublic

📘 Day 32: AI/ML Journey Math: Optimization (Convex functions, Lagrange multipliers) ML ZoomCamp @DataTalksClub 🚀: Rule-based systems vs ML (spam filter) 🔹 Rules = brittle, hard to maintain 🔹 ML = collect data → extract features → train model → predict spam/ham