#Supervised Learning Algorithm

K-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to each other. They can be used for classification and regression. Imagine you have a scatter plot with red and blue points, where red points represent one class and blue points represent another class. Now, let’s say you get a new data point you haven’t seen before, and want to know if it should be red or blue. KNN looks at the “K” closest points (a hyperparameter that you set) to this new one — say, the 3 nearest points. If 2 out of those 3 are red and 1 is blue, the new point is classified as red. It’s like asking your closest neighbors what they are and choosing the majority answer. Although simple, KNN performs surprisingly well based on the principle of proximity. Want to get better at machine learning? Accelerate your ML learning with our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: visually explained Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn

📍Day 4: Difference between Supervised vs Unsupervised Learning cheatsheet. ⬇️ Save it for Later👇 1. Supervised and unsupervised learning are two key approaches in machine learning. 2. In supervised learning, the model is trained with labeled data where each input is paired with a corresponding output. 3. On the other hand, unsupervised learning involves training the model with unlabeled data where the task is to uncover patterns, structures or relationships within the data without predefined outputs. ✅ Type ‘supervised’ in the comment section and we will DM the PDF version for FREE ✨ ⏰ 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

Linear Regression explained in the simplest way. Linear Regression is a supervised machine learning algorithm used to predict a continuous value based on one or more input variables. At its core, it tries to find the best fitting straight line that represents the relationship between variables. The idea is simple: You have input (X). You have output (Y). You want to understand how Y changes when X changes. The model assumes a linear relationship: y = mx + c m → slope (how much Y changes with X) c → intercept (value of Y when X is 0) It works by minimizing the error between predicted values and actual values, usually using Mean Squared Error. Where is it used? • House price prediction • Sales forecasting • Demand estimation • Trend analysis Linear regression is not just about drawing a line. It is about modeling relationships, understanding patterns, and making data-driven predictions. [machine learning, linear regression, supervised learning, data science, python, ai basics, statistics, ml algorithms]

Complicated ass topics

When to use which approach in subarray problems: Sliding Window • Elements are non-negative • You need subarray sum / max / min • Window can expand and shrink predictably Kadane’s Algorithm • Elements can be positive or negative • You need the maximum subarray sum • Subarray must be contiguous HashMap (Prefix Sum) • Elements can be positive, negative, or zero • You need an exact target sum • Counting subarrays or checking existence is required Different problems need different tools. Once you identify the pattern, the solution becomes straightforward. Hope you remember these points in the interview Follow @rbanjali.codes if these videos are worth watching 🫶🏻 #coding #software #jobs #dsa

Supervised Learning → Regression & Classification Unsupervised Learning → Clustering & Dimensionality Reduction Deep Learning → Neural Networks, CNN, RNN Model Evaluation → Accuracy, Precision, Recall, F1 Model Improvement → Overfitting, CV, Hyperparameter Tuning Master this and you’re officially learning AI/ML the right way. 🔥 Save this roadmap before you forget. 📌

The decision tree algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features. As the name suggests, the decision tree algorithm is a tree-structured classifier preferably used to solve classification problems. When a decision tree categorizes data into different classes it is called a classification tree. When it predicts numeric values, it is known as a regression tree. Interestingly, decision tree algorithms are used for regression models as well. The same library that you would use to build a classification model, can also be used to build a regression model after changing some of the parameters. We use Attribute Selection Method techniques to simplify the decision-making process and calculate values for every attribute. Watch the video to learn in detail about Decision Trees.

🤖K-nearest neighbors (KNN) is an algorithm used in supervised machine learning for classification and regression tasks. It operates on the principle of proximity, where an unclassified data point is assigned a class label or numerical value based on the majority class or average value of its K nearest neighbors in the feature space. KNN is non-parametric, meaning it does not assume any underlying probability distributions in the data. 💡KNNs are versatile, easy to implement, and suitable for both small and large datasets, although its performance may degrade with high-dimensional data due to the curse of dimensionality. 🔥Lazy Programmer is the BEST place for you to learn Machine Learning. Follow us for more tutorials like this 🤓 #datascience #data #datanalytics #mathematics #deeplearning #machinelearning #ai #artificialintelligence #statistics

The perfect study system includes: - 2 Active Learning Methods - Spaced Repetition - Reward Based Learning The best Active Learning methods: Feynman Technique: after understanding a concept pretend to explain it to a 5 yr old. Record yourself explaining, make adjustments based on what you missed, then repeat. Blurting Method Steps: 1. Read information 2. Recall information (write, or speak) 3. Fill in gaps 4. Repeat until complete How to use Spaced Repetition: Use this spaced repetition time interval when learning information: - First repetition: 1 day - Second repetition: 3 days - Third repetition: 7 days - Fourth repetition: 16 days - Fifth repetition: 35 days If you have less time, the same concept can be applied over hours in a day, or over a week. Ideally each interval is spaced far enough to where you forget some of the information, so that it’s difficult to retrieve. With each repetition you’re memory of the information gets stronger. The harder it is to remember the stronger you’ll cement it in your memory through deeper connections. Reward Based Learning: The cherry on top. With this your study system goes from good to great. After every 30 minute pomodoro session, have a reward(chocolate). I also write positive notes to myself on post-its. Then, I crumple up the post-its into balls so I have a visual progress bar like a video game would. Examples of positive reinforcement: - Your favorite snacks/meals - Time with friends - Your favorite show - A massage - Buy something you really wanted - Visualization of how doing the task made you feel. Let the feeling of fulfillment fill you up You can also reward stack by: Going for a walk after studying as a rewards for studying. After, have chocolate as a reward for going on a walk. I’ve pasted this guide in the comments if you’d like to copy and paste it to your notes. If this helped share with a friend you think it would help. Lmk if you have any questions :) #studygram #studysmart #learnfast #collegestudent #collegestudents #studymotivation #activelearning #feynman #blurting #rewardbasedtraining #academicweapon #academicweapons

okay ragebait but this is the GOAT of all study techniques👇🔥 It’s the “Shadow Study Technique”💌 It’s a powerful yet simple study method where you learn by closely following a teacher instead of studying alone. In this technique, you study along with a teacher, topper, or video and try to copy their pace, explanation style, and thinking process. You don’t stop the video repeatedly or over-analyse the topic at first. You just move forward with the content, exactly like a shadow follows a person. This method is highly effective for students preparing for competitive exams, board exams, UPSC, MBA, and professional courses because it removes confusion and overthinking. It also works because it improves focus and consistency at the same time. It also keeps your mind engaged, reduce distractions, and helps learn faster. That’s all but if wanna know more such unique study schedules, you can follow @rish.kansal now✨ (study tips, studygram, studypost, studygrammer, helping students, study tools and techniques, trending, growth, viral)

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such a goated method if ur a chronic doomscroller #university #study #student #fyp #studywithme