Building a Neural Network from Scratch with Python and NumPy

Today’s ML Learning Milestone Implemented Linear Regression from scratch using: • Gradient Descent • Ordinary Least Squares (Normal Equation) • NumPy only No libraries. Just math + implementation. Understanding the fundamentals deeply before moving forward into more advanced ML models. Consistency > Motivation. Code available on GitHub 👇 https://shorturl.at/utDPZ #MachineLearning #AI #Python #LearningJourney #NumPy #MLEngineer

Strong ML systems start with strong fundamentals. Today I implemented: • KNN (distance-based classification) • Logistic Regression (sigmoid + gradient descent) From scratch. No high-level libraries. Understanding the math > importing the model. Step by step. Code available on GitHub 👇 https://shorturl.at/2ZT2X #MachineLearning #AI #Python #DeepLearningJourney #EngineeringMindset

Mastered NumPy for numerical computing. Comparing Python lists vs. NumPy arrays was eye-opening—vectorization isn't just a feature, it's a necessity for high-performance AI. ⚡ Special thanks to Elevate Labs for the structured challenges. It’s one thing to read about these concepts, but another to build them from scratch! #Elevate Labs,#Python #AIML #DataScience #SQLite #NumPy #Pandas #EngineeringStudent #BackendDevelopment #TechLearning

I scaled my features before splitting the data. Validation accuracy hit 𝟗𝟒%. I thought I nailed it. I didn't. I was leaking test information into training. When you 𝐟𝐢𝐭 𝐚 𝐬𝐜𝐚𝐥𝐞𝐫 𝐨𝐧 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭, it learns statistics from data your model will be evaluated on. The 𝐭𝐞𝐬𝐭 𝐬𝐞𝐭 𝐢𝐬𝐧'𝐭 𝐭𝐫𝐮𝐥𝐲 𝐮𝐧𝐬𝐞𝐞𝐧 anymore. 𝐓𝐡𝐞 𝐟𝐢𝐱: fit preprocessing only on training data, then transform test data using those learned parameters. This won't ruin a simple project. But in real Machine Learning work, it's the difference between honest evaluation and quietly inflated metrics. Subtle leakage compounds. #DataScience #Python #MachineLearning #AIEngineer #DataAnalysis #ScikitLearn #MLBasics

Machine Learning Project | House Price Prediction I built an end-to-end Machine Learning project to predict house prices using regression techniques. What I did: • Explored and cleaned the dataset • Engineered new features to capture non-linear effects • Encoded categorical variables • Trained and evaluated a regression model using RMSE and R² • Interpreted model coefficients for insights Result: The model achieved a strong R² score, showing good predictive performance. Tools: Python | Pandas | Scikit-learn | Google Colab GitHub Repository: [https://lnkd.in/d-3yTf5P] #MachineLearning #DataScience #Python #Scikit-learn #NumPy #Pandas

Features vs Labels In Machine Learning, everything starts with data. But data has two important parts: 1) Features 2) Labels What are Features? Features = Input variables These are the characteristics or properties the model uses to learn. Imagine we are training a model to recognize a cat. The model might look at: - Ears - Eyes - Nose - Whiskers - Fur pattern All of these are features. Features are the inputs the model observes. What is a Label? Label = Output or Target This is what we want the model to predict. In this case: “Cat” is the label. The label is the correct answer we want the model to learn. #Python #MachineLearning #DataScience #ArtificialIntelligence #MLBasics #DeepLearning #LearningJourney #DataEngineering

Data is the fuel of AI, and Data Types are the engine. In Day 5 of our Python for AI Engineering series, we explore how Python identifies and stores information. Much like organizing a professional workspace, knowing which "container" to use for your data is crucial for efficiency. Without a solid grasp of these data types, building scalable AI models or complex automations is impossible. Inside today's session: Comprehensive overview of the 7 primary Python Data Types. Practical Type Conversion (Type Casting) methods. Understanding List, Tuple, and Dictionary structures for AI data. #Python #AIEngineering #DataTypes #ProgrammingBasics #MachineLearning #Day5 #PythonForAI #TechEducation #LearnToCode #DataScience #SoftwareEngineering #CodingLogic #AIFoundations

Day 3/100 – AI Engineer Challenge Focused on building a strong foundation in NumPy, the backbone of numerical computation in machine learning. Today’s work: • Understanding NumPy arrays vs Python lists • Practicing vectorized operations • Working with 1D and 2D arrays • Applying row-wise and column-wise computations using axis Gaining clarity on how structured numerical data is handled before moving deeper into ML concepts. Github: https://lnkd.in/g94n-B5h #AIEngineer #NumPy #100DaysOfAI #MachineLearning #Python

Day 3– AI Engineer Challenge Focused on building a strong foundation in NumPy, the backbone of numerical computation in machine learning. Today’s work: • Understanding NumPy arrays vs Python lists • Practicing vectorized operations • Working with 1D and 2D arrays • Applying row-wise and column-wise computations using axis Gaining clarity on how structured numerical data is handled before moving deeper into ML concepts. Github: https://lnkd.in/dsY837Hv hashtag #AIEngineer hashtag #NumPy hashtag #DaysOfAI hashtag #MachineLearning hashtag #Python

Small Actions, Big Momentum Some nights I feel like I haven’t learned enough. Then I remind myself: Small, consistent actions beat random intensity. Today, I: • Practiced a tiny Python script • Fixed one small bug • Noted one lesson learned It doesn’t look like much. But done every day, this adds up — fast. If you’re learning anything in tech, remember: You don’t need to do everything. Just do something daily. Follow me if you want to see a daily journey in Python, AI & Data Science — building in public, one step at a time. #PythonLearning #AIJourney #DataScienceStudent #LearningInPublic #Consistency #BuildInPublic #TechGrowth