Jakub Polec’s Post

Best machine learning algorithm to use based on the type of problem (classification, regression, clustering, or dimensionality reduction), the nature of your data (labeled, quantity prediction, structure prediction), and sample size.

Best machine learning algorithm to use based on the type of problem (classification, regression, clustering, or dimensionality reduction), the nature of your data (labeled, quantity prediction, structure prediction), and sample size.

Step 1: You understand Linear regression, you will realize curve fitting Step 2: With logistic regression, you will know about nonlinear activation functions Step 3: Then you will understand cross-entropy loss, the holy grail of machine learning losses Step 4: As you progress toward maximum likelihood estimation you will get the essence of probability and optimization Then the actual fun of function approximation begins. Most people never reach step 4.

Amazing steps - log likelihood is the key to understanding ML. Learn log likelihood today - https://lnkd.in/g4ETc6P9

https://lnkd.in/gXKVzPYb Machine learning for volatility trading leverages advanced computational techniques to forecast market volatility and improve trading strategies. Here is a detailed Thread on Machine Learning for Volatility Trading🧵

Great exposure on ML algorithms.

🚀 Day 32/100: Today's learning journey was dedicated to revising fundamental concepts in machine learning and regression analysis. 1. K-Nearest Neighbors (KNN): - Explored the KNN algorithm, a simple yet powerful method for classification and regression tasks. - Revised the concept of KNN, where predictions are made based on the majority vote (for classification) or average (for regression) of the k nearest data points. 2. Simple and Multiple Linear Regression: - Reinforced understanding of linear regression, a foundational technique for modeling the relationship between independent and dependent variables. - Reviewed simple linear regression, which involves predicting a continuous target variable based on a single predictor variable. - Delved into multiple linear regression, where multiple predictor variables are used to predict the target variable, allowing for more complex modeling of relationships. By revisiting these concepts, I solidified my understanding of regression analysis and its application in predictive modeling. Excited to apply these techniques in real-world projects! #MachineLearning #RegressionAnalysis #KNN #LinearRegression #100DaysChallenge 📈🔍

It is based on Bayes' theorem with an assumption of independence between features. Click for more https://bsapp.ai/-DuuoosY4 #MachineLearning

Good explanation with neat visualization is a key to understand the topic, check quickly your understanding of linear regression with this post! 🤟🏼 Sharing the knowledge 🫶🏽 #linearregression #machinearning #learning #visualization

Diversifying your trading strategy? Consider Probabilistic Models, Linear Regression. Ideal for handling a wealth of data, trumping Time Series models built for fewer, noisy data sets. Need simple benchmarks? SVM is your go-to. For NN testing, don't forget LSTMs 📈💡 #AlgoTrading