Feature engineering in Machine learning | A step by Step Guide | in Urdu/Hindi скачать в хорошем качестве

Feature engineering in Machine learning | A step by Step Guide | in Urdu/Hindi

#datascience --------------------------------------------------------------------------------------------------------------------------------------- Video Description: Unlock the secrets of successful Machine Learning with our comprehensive guide on Feature Engineering! This tutorial is a deep dive into the art and science of preparing your data to boost the performance of your models. Whether you're a beginner or an experienced data scientist, this video will equip you with the skills to transform raw data into a goldmine of insights. We'll explore techniques like normalization, binning, encoding, feature selection, and much more. Don't let unoptimized data hold back your machine learning projects. Get ready to elevate your data preprocessing game and make your machine learning models more accurate than ever! Remember to Like, Share, and Subscribe for more tutorials on Machine Learning and Data Science! #FeatureEngineering #MachineLearning #DataScience #DataPreprocessing #Python --------------------------------------------------------------------------------------------------------------------------------------- Feature engineering is the process of creating new input features for machine learning. Each input feature is a column of data that the machine learning model uses to make its predictions or classifications. Feature engineering makes a machine learning model more accurate by providing it with more or better input, making it one of the most important steps in building an effective model. The major steps involved in feature engineering are typically: Brainstorming or Testing Features: The first step is to consider what features could potentially improve the performance of your model. This might be based on your understanding of the problem, domain knowledge, or previous experiences. Creating Features: Once you have an idea of what features to create, the next step is to create them. This could involve anything from simple arithmetic operations to complex calculations. Checking how the Features work with your Model: After creating features, the next step is to include them in your model and see how they affect its performance. If a feature improves your model, you'll want to keep it. If not, you can discard it. Iterating on your Features: After testing your features, you might have new ideas for features to create. Alternatively, you might find that slight tweaks to your features improve their performance. Thus, this process is often iterative. ✅Subscribe to our Channel to learn more about the top Data Science, Machine Learning and Deep Learning :    / codanics   --------------------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------------------------- Explore other free courses here: 🔥 Python ka chilla v1.0 for Data Science (playlist):    • Python_ka_chilla (python in 40 days in urd...   🔥 Machine learning ka chilla (playlist):    • Видео   🔥 Streamlit course for dash boards and webapps for data science (playlist):    • Streamlit for webapps/Dashboards of Data S...   🔥 Cloud computing (playlist):    • Cloud Computing for Beginners in Urdu/Hind...   🔥 #RwithAammar Programming with R for Data Analysis and Data Science (Playlist):    • Complete Data Science Course in R   🔥 Make publication ready graphs in Rstudio:    • Publication Ready Graphs   🔥 Computer Vision openCV in Python (Playlist):    • Computer Visions (openCV) with Python in URDU   🔥MS Excel for Data Analysis:    • Microsoft Excel for Intermediate | Codanic...   🔥 SQL complete course in Hindi/Urdu:    • SQL | mySQL Complete Course Playlist in Ur...   --------------------------------------------------------------------------------------------------------------------------------------- 🔥-Follow me on following platforms: 1. Twitter:   / aammar_tufail   2. github: https://github.com/AammarTufail 3. Tiktok:   / draammar   --------------------------------------------------------------------------------------------------------------------------------------- #dataScience Join this telegram group for regular updates via zoom meeting 1-to-1 sessions: https://t.me/codanics --------------------------------------------------------------------------------------------------------------------------------------- for more details: www.codanics.com