Towards Data Science’s Post

Curious about how frequency encoding can enhance machine learning models? In my new article, "A Practical Guide to Frequency Encoding and Its Impact on Machine Learning," I dive into the benefits and challenges of this powerful technique. This article explores: 🔄 How frequency encoding works and its impact on model performance 📉 The trade-offs of using frequency encoding, including its limitations in scenarios like imbalanced datasets and when category identity is crucial 💡 Practical insights on when to apply frequency encoding and its role in handling categorical data in machine learning ✨ Check out the full article here! https://lnkd.in/gcS9KG2t I'm thrilled to share this project as part of my data science journey at Purwadhika Digital Technology School under the guidance of Median Hardiv Nugraha. My goal is to provide actionable insights for better data preprocessing and model development. 📖 Dive in and share your thoughts! Feedback is always welcome. #datascience #dataanalyst #machinelearning #preprocessing #featureencoder

10 Techniques to Solve Imbalanced Classes in Machine Learning (Updated 2024) https://lnkd.in/gNDMxncX

Explore quintessential stages within the Machine Learning life cycle, each with critical considerations for proper ML model development → https://lnkd.in/ejvyNTh6 #MLOps #MLengineer #MLmanagement #ML

Ever wondered how a tiny tweak in data can bring a robust machine learning pipeline to its knees? Discover why data validation is the unsung hero of successful AI deployments and learn how to safeguard your models from unexpected breakdowns. Dive into my article to explore the crucial steps to maintain your ML pipeline's health and efficiency! https://lnkd.in/gNRmX2fm #MachineLearning #AIPipeline #DataValidation #TFX

In the realm of machine learning, data is often considered the fuel that drives models to achieve remarkable feats of intelligence. Traditionally, labeled data, where each input is accompanied by a corresponding output, has been the primary focus of training machine learning algorithms. However, the world is replete with vast amounts of unlabeled data – information that lacks explicit #dataannotation or categorization. Let’s take a look at labeled and unlabeled data and how you can use the latter to power your machine-learning projects. #artificialintelligence #datalabeling #dataannotation #machinelearning #dataannotation #aiforgood https://lnkd.in/dKKjQnnm

🔍 Want to know why 80% of ML projects fail? Poor data preprocessing! Here's your 5-step guide to effective data preprocessing: 1. Clean Your Data ✨ Remove duplicates Handle missing values Fix inconsistencies 2. Transform & Scale 📊 Normalize numerical features Encode categorical variables Handle outliers 3. Feature Engineering 🛠️ Create relevant features Remove redundant ones Reduce dimensionality 4. Split Your Data 📈 Train/test/validation sets Maintain class balance Prevent data leakage 5. Validate & Document 📝 Cross-validation Record preprocessing steps Version control your data Pro tip: Spend more time here than model building. Clean data = Better results! 👉 What's your biggest data preprocessing challenge? Share below! #MachineLearning #DataScience #AI #DataPreprocessing #MLOps #DataEngineering https://lnkd.in/ekH2QZAp

The Role of Data Structures and Algorithms in Machine Learning In the fast-evolving world of machine learning, data structures and algorithms (DSA) are foundational elements that drive efficiency and effectiveness. They are more than just theoretical concepts; they directly influence the performance and capabilities of ML systems. As the field expands, understanding DSA becomes vital for anyone aspiring to excel in machine learning. The following article provides a direction on how DSA and problem solving enhance the approach to developing and increasing the efficiency of ML models. https://lnkd.in/dUsB5y3j. #learning #dsa #problemsolving #machinelearning

In machine learning, overfitting is a problem that results from attempting to capture every variance in a data set. An overfit model will lead to major errors when deployed to production, causing inaccurate predictions and unreliable results. In this article, join Altair's Chief Data Scientist Dr. Mamdouh Refaat as he explores what causes overfitting in the machine learning model development process and how to fix it to ensure your machine learning projects are reliable. #Altair #MachineLearning #DataScience #DataAnalytics

Machine Learning on Unstructured Data : An Opinion from ML Newcomer. I believe that machine learning in its simplest form is a linear regression model, where it can be divided into two main components : Data plots and the algorithm (OLS). I genuinely think the magic of machine learning do not reside on the complex algorithm being used to solve a problem, but rather how the problem is represented (data plots). In OLS we are dealing with numbers of interval/ratio data type which we dont have that luxury when we are dealing with unstructured data. There are many ways how the data can be seen, that is why we can have unigram, bigram or trigram when processing strings. Or we can see an image as a collection of individual pixel cell or a collection of 3x3 cell or even a new single cell value generated by surrounding cells. What differs one data scientist to another is the domain knowledge of the problem which differentiate how the problem is represented.

Ever wondered how to spot and tackle data anomalies in machine learning? Dive into our latest blog where we explore just that! Learn best practices for detecting and mitigating data anomalies to ensure your ML models are robust and reliable. Check it out here: Detecting and Mitigating Data Anomaly in ML Link: https://lnkd.in/gEgMcuX4 #DataScience #MachineLearning #DataAnomalyDetection