#Functional Patterns In Machine Learning

Why do we even need design patterns? 👇 Code starts simple… but as features grow → it becomes messy 😬 👉 too many if-else 👉 duplicate logic 👉 hard to modify without breaking things 💡 Design patterns give a structured way to handle these problems 🟢 Creational When object creation becomes messy Example: payment methods (UPI, Card, Wallet) 🔵 Structural When you want to add behavior without changing existing code Example: adding logging & authentication to every API call 🟣 Behavioral When system behavior changes based on situation Example: payment flows, notification systems 🧠 Learn patterns → write clean, scalable code #systemdesign #lld #softwareengineering #designpatterns #backenddeveloper frontenddeveloper

📍6 Pillar Machine Learning Algorithms (Episode 88 of 100): DM to download the Free PDF👇 1. Support Vector Machine (SVM): SVM is a commonly applied supervised machine learning algorithm that searches hyperplane with maximal separation from each data class. 2. Naive Bayes (NB): Naive Bayes, another supervised ML algorithm, is a probabilistic method based on Bayes’ law. 3. Logistic regression: Logistic regression is a classification algorithm utilized for probability prediction of target class by logistic function. 4. K-Nearest Neighbors: The K-Nearest Neighbors is a distance-based algorithm as it first finds all the closest points around new unknown data point and calculates the distance between them to determine the class of new data points. 5. Decision Trees: Decision tree, a supervised machine learning algorithm, is a tree-structured classifier that continuously divides the data based on specific parameters. 6. Random Forest: The random forest comprises multiple decision trees and can provide more accurate predictions by combining all of them. ⏰ Like this Post? Go to our bio, click subscribe button and subscribe to our page. Join our exclusive subscribers channel ✨ Hashtags (ignore): #datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code

Comment "ML" to get the links! 🧠 You Will Never Struggle With Machine Learning Again 📌 Watch these beginner-friendly ML tutorials: 1️⃣ Learn Machine Learning Like a Genius – by InfiniteCodes 2️⃣ All ML Concepts Explained in 22 Minutes – by InfiniteCodes 3️⃣ ML for Everybody (Full Course) – by FreeCodeCap Stop getting lost in complex formulas and confusing jargon. These videos break down Machine Learning step by step — from basic intuition to real-world model building. Whether you’re learning for AI projects, data science, or just starting your tech career, this is the fastest way to finally understand ML for real. ✨ Save this, share it, and turn confusion into clarity with hands-on Machine Learning skills.

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here is how you actually build a machine learning model from scratch. before you touch any code you need to define your problem. are you predicting a number or classifying something into categories. that one question determines every decision you make after it. step one is your data. collect it, clean it, and understand it before you do anything else. bad data will break a good model every single time. step two is feature engineering. decide which variables actually matter and how to represent them. this step separates people who understand ML from people who just copy code. step three is choosing your algorithm. start simple. linear regression for numbers, logistic regression for categories. do not reach for a neural network when a simpler model will do the job. step four is training and evaluation. feed your data in, measure how wrong it is, and adjust. then look beyond accuracy; check your precision, recall, and confusion matrix to understand where your model is actually failing. the goal is not a perfect model on the first try. the goal is understanding every decision you made along the way. Comment “ML” for my personal guide on how to build these things. #machinelearning #datascience #ai #cs #python

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The exact framework I’d use to learn ML from scratch in 2026. Save this if you’re actually trying to build - not just collect tutorials. #machinelearning #artificalintelligence #datascience #learntocode #coding

📍Machine learning algorithms every data scientist must know👇 1. Linear Regression: Used for predicting a continuous value. It’s simple yet effective for various problems. 2. Logistic Regression: Despite its name, it’s used for classification tasks, particularly binary classification. And I also use class probabilities (class proba), which is the probability of the class label. 3. Decision Trees: Used for both classification and regression tasks. They split data into branches to form a tree structure. 4. Gradient Boosting Machines (GBM): An ensemble technique that builds predictive models in a stage-wise fashion, often yielding high-quality predictions. I use these frequently for high accuracy and performance. 5. Random Forests: An ensemble method that uses a collection of decision trees to improve prediction accuracy and avoid overfitting. 6. Support Vector Machines (SVM): Primarily used for classification tasks, SVMs are effective in high-dimensional spaces. 7. K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm used for classification and regression. 8. Naive Bayes: A group of simple, probabilistic classifiers based on applying Bayes’ theorem with strong independence assumptions. 9. Neural Networks: Versatile and powerful, used for a wide range of tasks including classification, regression, and unsupervised learning. Deep learning models, a subset of neural networks, are particularly notable for their performance in complex tasks like image and speech recognition. Hashtags (ignore): #datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code

These are some of the best beginner-friendly resources I’ve found to actually understand machine learning. Nothing overly complicated, just what you need to get the concepts and start building. Comment ML and I’ll send you all the resources.

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The Secret to Perfect Data Models #MachineLearning #PolynomialRegression #Statistics #Math #Manim Ever wondered why your machine learning model isn't performing as expected? In this video, we break down polynomial curve fitting, a fundamental concept in data science and statistics. We explore the visual differences between Degree 1 (Underfitting), Degree 3 (Good Fit), and Degree 11 (Overfitting). Learn how increasing the degree of a polynomial affects how it captures data trends and why the optimal model is crucial for accurate predictions.

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