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CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
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#Difference Between Algorithm And Machine Learning
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FR📍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.
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@freakz.ai
1.3M
SAI’ve been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start.
Comment down below “TRAIN” and I’ll send you a more in-depth checklist along with the best GitHub links to help you start learning each concept.
If you don’t receive the link you either need to follow first then comment, or your instagram is outdated. Either way, no worries. send me a dm and I’ll get it to you ASAP.
#cs #ai #dev #university #softwareengineer #viral #advice #machinelearning
@sambhav_athreya
23.8K
CO🚀 Machine Learning Roadmap (2025 Edition)
Unlock your journey into AI, Machine Learning & Deep Learning with this step-by-step guide designed for beginners to advanced learners.
📌 What You’ll Learn in This Video:
⚙️ Phase 1 – Core Foundation
📐 Math Basics | 🐍 Python Programming
🧹 Phase 2 – Data Preparation
🧽 Data Cleaning | 🎛 Feature Engineering | 📊 Visualization
🤖 Phase 3 – Machine Learning Concepts
🎯 Supervised & Unsupervised Learning | 🔍 Key Algorithms
🧪 Phase 4 – Model Optimization
📈 Cross-Validation | 🛠 Hyperparameter Tuning | 📍 Metrics
🧠 Phase 5 – Advanced ML
🌀 Neural Networks | 👁 Computer Vision | 💬 NLP
🚀 Phase 6 – Deployment & Real-World Use
🗃 Model Serialization | 🌐 APIs | ☁ Cloud | 🧩 MLOps
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💡 Whether you're a beginner, student, or career switcher, this roadmap will help you become job-ready in AI and ML.
📚 Save this video and start learning step by step.
👇 Comment "ROADMAP" if you want a downloadable PDF version.
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🔍 Keywords:
Machine Learning Roadmap 2025, AI learning path, Deep Learning, Data Science Roadmap, Python for ML, Best way to learn AI, MLOps, Cloud AI skills.
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#MachineLearning #AI #ArtificialIntelligence #DeepLearning #DataScience #Python #MLRoadmap #LearnML #TechCareers #Programming #NLP #ComputerVision #MLOps #DataEngineer #FutureSkills #Roadmap2025 #AIEducation #AIRevolution #CodingJourney
@codewithprashantt
842.4K
SEComment “Stat” and I’ll send you the link.
A visual, beginner-friendly site that explains machine learning statistics with clear examples and live probability experiments.
@sebintel
134.3K
SUIf you were starting Machine Learning in 2026, what would your roadmap look like?
ㅤ
#MachineLearning
#MLJourney
#LearnML
#AI2026
#DataScienceJourney
@sundaskhalidd
158.2K
EQThe Secret Behind Machine Learning Predictions! Ever wondered how machines make binary decisions? This video breaks down Logistic Regression using the Sigmoid Function. We visualize how the weight (w) controls the steepness of the curve and how the bias (b) shifts it along the x-axis. See how Cross-Entropy (CE) Loss is minimized to find the optimal fit for your data points. Finally, we explore the decision boundary at P=0.5, which separates predictions into Class 0 and Class 1. Perfect for data science students and machine learning enthusiasts looking for a quick, intuitive visualization of classification algorithms and mathematical optimization. #LogisticRegression #MachineLearning #SigmoidFunction #Math #Manim
@equationsinmotion
447.7K
CHHere’s your full roadmap on how to get into machine learning. Comment “Roadmap” to get the pdf.
Save and follow for more.
#ai #machinelearning #coding #programming #cs
@chrispathway
58.9K
PLDemystifying Linear Regression: The Foundation of Machine Learning
Have you ever wondered how data scientists predict future trends based on past information? Linear regression is the perfect starting point. It is a fundamental machine learning algorithm that finds the best straight line through a scatterplot of data points. By drawing this line, we can understand the relationship between variables and make accurate predictions for the future. Whether you are forecasting sales or estimating housing prices, linear regression turns raw data into actionable insights. It is simple, powerful, and essential for anyone stepping into the world of predictive modeling.
linear regression, machine learning basics, predictive modeling, data science algorithms, artificial intelligence education, statistics for data science, regression analysis, tech fundamentals, statistical learning, line of best fit, forecasting models, data analytics, predictive analytics, coding algorithms, beginner machine learning, ai fundamentals, data trends, regression model, mathematical modeling, tech concepts
#LinearRegression #MachineLearning #DataScience #PredictiveModeling #AI
@plotlab01
81.9K
ARHow Machines Learn - Part 1
This is gradient descent. The algorithm behind every AI you’ve ever used. In this series, we’ll go over the basics of machine learning and AI. Slowly building our intuition and foundation, understanding the math, and finally taking on tougher projects.
This is all in effort of my mission; providing the best education I can give for free. Thanks for watching!
#ai #machinelearning #software #manim #engineering
@armas.4am
52.0K
AIGradient descent is an optimization algorithm widely used in machine learning to minimize a loss function, which is a measure of how well a model’s predictions match the actual outcomes.
In the gradient descent process, the model iteratively adjusts its parameters (its weights and biases) to reduce the loss.
The parameters are adjusted based on the gradient, or partial derivatives, of the loss function with respect to each parameter. The gradient points in the direction of the steepest increase in the loss function, so to minimize the loss, we move the parameters in the opposite direction (why negative gradients are used).
By repeatedly subtracting the gradient step-by-step, gradient descent guides the parameters toward values that ideally correspond to the lowest possible loss, improving the model’s performance over time.
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@aibutsimple
402.2K
LA🤖Bayesian Machine Learning uses probabilities to update predictions with new data. It’s great for uncertain environments.
💡A/B testing is a real-world example; it helps decide which version of a product or service is better by considering past knowledge and current results. It’s also used in medical diagnosis and financial predictions for its ability to handle uncertainty effectively.
🚀In short, Bayesian Machine Learning boosts prediction accuracy by accounting for uncertainty, making it valuable across different fields like A/B testing, medicine, and finance.
🔥The Lazy Programmer is the NO.1 place for you to learn everything about Bayesian Machine Learning. From Bayesian Linear Regression to Classification and Clustering, we’ve got you covered! Head to our link in bio to start learning today!
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