USC Master of Science in Applied Economics and Econometrics (MS AEE)’s Post

For a while, I have been writing about theoretical information on artificial intelligence. Now, I have provided examples of how we can use machine learning models practically. As the most commonly known example, I wrote about the time series forecasting using Python.

A guide for using the Wavelet transforms. Wavelet transforms can identify patterns in financial time series data, providing valuable insights for algorithmic trading strategies. Here's how to get started: The article provides a comprehensive guide for using the wavelet transform in machine learning with examples and code snippets. https://lnkd.in/eipEYiYy Looking to start using Python for algorithmic trading with ML? Here's a free crash course with everything you need to get started. Join the 1,000s of people who finally started with Python after reading it: https://lnkd.in/e-7FkSPP

How to Detect Seasonality in the Time Series Data, And Remove Seasonality in Python https://is.gd/a9K3c6 #MachineLearning #DataScience #Latest

Hey! Day-14 of Exploring Machine Learning Techniques. Today, I have covered Lasso Regression. About Lasso Regression - Supervised Learning Algorith - Regression Technique - Least Absolute Shrinkage and Selection Operator(LASSO) - implements L1 Regularization to avoid overfitting What if there are more than 2 Variables? Multiple Linear Regression is used - Multiple linear regression is a model for predicting the value of one dependent variable based on two or more independent variables. Simple Linear Regression : y = wx +b Multiple Linear Regression : y = wx1 +wx2 +wx3 +b Regularization: Regularization is used to reduce the overfitting of the model by adding a penalty term to the model. Lasso Regression uses LI regularization technique. The "penalty" term reduces the value of the coefficients or eliminate few coefficients, so that the model has fewer coefficients. As a result, overfitting can be avoided. This Process is called as Shrinkage. Also, with the help of cost function and gradient function. I predicted salary as an individual work Experience using Lasso Regression with a great accuracy. Dataset Link : https://lnkd.in/dfCbwHrd Let's Journey together towards the AI Excellence.🤝 #aiandml #datascience #lassoregression #python #ml #machinelearning

https://lnkd.in/gSQBtzs5 #MachineLearning #DataScience #KMeans #Clustering #UnsupervisedLearning #Python #DataMining #Algorithms #AI K-Means Clustering: Simplifying Complex Data and Finding Patterns with Machine Learning Intro:K-means clustering helps you organize things into groups based on how similar they are, just like sorting LEGO pieces by color.

Discover the importance of sequential execution in data processing 📊 Learn why executing tasks in a specific order is essential for efficient data handling 🔄 Explore how sequential execution enhances processes and boosts productivity using Coretex ⚙️🚀 🔗 Watch the full video: https://lnkd.in/dTteftY5   #MachineLearning #DataScience #ArtificialIntelligence #DeepLearning #DataAnalytics #BigData #AI #ML #DataMining #PredictiveAnalytics #NeuralNetworks #DataVisualization #NaturalLanguageProcessing #ComputerVision #Algorithm #Python #RStats #Analytics #DataEngineering #DataDriven

Now I understand how kNN takes a localized approach instead of building a global model that aims to capture the underlying distribution of the entire dataset. It efficiently approximates the target function locally by concentrating on a query point's immediate neighborhood. I highly recommend checking out Sebastian Raschka's lecture notes from the University of Wisconsin-Madison. His in-depth analysis provides a comprehensive understanding of this versatile algorithm and its applications. #kNN #machinelearning #datascience #algorithms #datamining #regression #classification #python #R #datasciencelife

🗒 Just wrote about DBSCAN Clustering Algorithm on the Code Like A Girl blog! Check it out for a clear explanation and even a Python implementation you can build from scratch 🛠! Huge thanks to Code Like A Girl for reviewing and accepting my article! If you found this article informative and helpful, please don’t hesitate to 👏 and follow me on Medium. #mlfromscratch #codelikeagirl #python #machinelearning #ai Women Techmakers