Moving beyond model.fit()—building Gradient Descent from scratch. 🤖 I’ve been spending time lately digging into the mathematical foundations of Machine Learning. While libraries like Scikit-Learn make it easy to implement linear regression in two lines of code, I wanted to see if I could replicate those results by building a Gradient Descent algorithm from the ground up in Python. In this video, I’m: Defining the cost function (Mean Squared Error). Calculating partial derivatives to update weights ($m$) and bias ($b$). Fine-tuning the learning rate and iterations to reach global minima. Comparing my manual results against the LinearRegression class from Sklearn. The result? A near-perfect match! Understanding the "why" behind the "how" is making me a much better developer as I work on more complex computer vision projects. #MachineLearning #Python #DataScience #GradientDescent #AI #CodingLife

We all use optimisers in Machine Learning — but how often do we actually see them working? I built Gradient Descent from scratch in Python, implementing: • Vanilla Gradient Descent • Momentum • Learning Rate Decay • RMSprop No ML libraries. Just NumPy, math, and curiosity. I visualised the entire training process — loss curves, weight & bias updates, parameter movement, and even full training animations. Watching the line slowly move toward the true parameters makes the theory feel real. Big takeaway? Optimisers aren’t magic. They’re disciplined update rules applied repeatedly. I did take GPT’s help in structuring parts of the code — AI speeds things up, but real understanding comes from building and experimenting yourself. Code here: https://lnkd.in/d4maNaR4 #MachineLearning #GradientDescent #Python #AI #LearningInPublic #DeepLearning #NeuralNetworks #ArtificialIntelligence #MLResearch #LearningDynamics #Optimization #GradientDescent

🚀 Starting my journey in AI & Machine Learning Currently learning the fundamentals and practicing basic data operations using Pandas in Python. In this video, I worked with a sample hotel dataset to explore: • Reading CSV files • Understanding datasets using head(), tail(), info(), and describe() • Filtering data • Dropping rows and columns • Using loc[] and iloc[] for indexing Building strong foundations before moving into Machine Learning projects. #Python #Pandas #MachineLearning #ArtificialIntelligence #PythonDeveloper #FullStackDeveloper

What if you could estimate your CGPA before results? 👀 I built a Machine Learning model to simulate and predict CGPA using a synthetic dataset (500+ records). 📈 R² Score: 0.904 📊 Mean Absolute Error (MAE): 0.104 🧠 Linear Regression based approach This project helped me understand data preprocessing, model training, and evaluation metrics in a real ML workflow. Sharing a quick demo below — feedback welcome! 🚀 #MachineLearning #Python #DataScience #AI #StudentProject

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

🎬 Movie Recommendation System using Machine Learning In this project, I built a content-based recommendation system that suggests movies based on similarity metrics. 🔍 Key Highlights: • Data preprocessing and feature engineering • Text vectorization for movie features • Cosine similarity for recommendation logic • Interactive interface for user input 🛠 Tech Stack: Python | Pandas | NumPy | Scikit-learn | Streamlit This project helped me understand how platforms like Netflix recommend personalized content using similarity-based approaches. I’m continuously strengthening my skills in AI & Machine Learning by building real-world projects. Feedback and suggestions are welcome! #MachineLearning #AI #Python #DataScience #RecommendationSystem

🌸 What better way to start learning Machine Learning than with the classic Iris dataset? For my first ML project, I built an Iris Flower Classifier using Support Vector Machine (SVM) in Python. Here’s what I worked on: 🔹 Loaded and explored the Iris dataset (150 samples, 4 features) 🔹 Performed statistical analysis using df.describe() 🔹 Visualized feature relationships using Seaborn pairplots 🔹 Split the dataset into features (X) and labels (y) 🔹 Trained a classification model using Scikit-learn’s SVC The model learns to classify three species Setosa, Versicolor, and Virginica using just four measurements. 📊 Result: The model achieved 96% accuracy on the test dataset. 🎥 Here’s a short video showing the project and how it works. Excited to continue learning and building more ML projects. 🚀 #MachineLearning #Python #DataScience #SVM #AI #LearningJourney #100DaysOfCode

Deep dive into probability and performance optimization today 🔍📊 Explored Rayleigh, Pareto, and Zipf distributions — understanding where they apply in real-world data patterns. Also practiced NumPy ufuncs to perform fast and efficient element-wise operations. Small daily improvements. Stronger fundamentals. Bigger impact ahead 💡 #AI #MachineLearning #Python #NumPy #StudentDeveloper

🚀 Understanding Naive Bayes in Action Ever wondered how probabilistic models work? Naive Bayes is a classic generative model that shows the power of reasoning under uncertainty. 🔹 It uses Bayes’ theorem 🔹 Assumes feature independence 🔹 Works surprisingly well even with small datasets 💡Fun fact: It’s often taught using spam classification as an example — not because NB is the cutting-edge choice today, but because it’s perfect for learning core concepts. In my latest Jupyter notebook, I walk through: - Full mathematical derivation - Manual probability calculations with a tiny table - Log probabilities to avoid underflow - Gaussian, Multinomial, and Bernoulli NB variants - Decision boundary visualization - Comparison with Logistic Regression Whether you’re brushing up on ML fundamentals or teaching someone new, NB is a great way to visualize how probability can drive predictions. Check out the full notebook here: [https://lnkd.in/djzpdSCr] #MachineLearning #DataScience #Python #NaiveBayes #LogisticRegression #LinearRegression #HandsOnLearning

Understanding ColumnTransformer in Machine Learning When working with real-world datasets, we often have numerical + categorical features together. Applying the same preprocessing to all columns is not correct. That’s where ColumnTransformer from scikit-learn comes in! 🔹 It allows you to apply different transformations to different columns in a single pipeline. 🔹 It keeps preprocessing clean, organized, and production-ready. 🔹 It avoids data leakage when used with Pipeline. Example: Apply Standardization to numerical features Apply OneHotEncoding to categorical features Combine everything into one transformed dataset This makes your ML workflow: ✔️ Cleaner ✔️ More efficient ✔️ Scalable 💬 Question: Have you used ColumnTransformer in your ML projects? What challenges did you face? Github : https://lnkd.in/dee_ZATE #MachineLearning #DataScience #Python #ScikitLearn #FeatureEngineering