Snowflake’s Post

Snowflake making ML as simple as running select sysdate(). It's simple and no need of ML expertise to build ML solution. #snowflake #ML #SnowflakeCortex

I was SHOCKED the first time I realized that a lot of data teams use Airflow to deploy their machine learning workflows. Well at least in terms of training and retraining models. But it made me wonder, what really is the difference between machine learning and data engineering? To help answer this question I reached out to Sarah Floris, MS who put together an awesome piece on the difference between data engineering and machine learning pipelines. Check out the link below! https://lnkd.in/gDvAK7DP

I was SHOCKED the first time I realized that a lot of data teams use Airflow to deploy their machine learning workflows. Well at least in terms of training and retraining models. But it made me wonder, what really is the difference between machine learning and data engineering? To help answer this question I reached out to Sarah Floris, MS who put together an awesome piece on the difference between data engineering and machine learning pipelines. Check out the link below! https://lnkd.in/gDvAK7DP

Here is a simple example of building classification models using the ScikitLearn dataset. The process involves initializing the model, fitting it to the training data, and then making predictions on new unseen data.

This is a fantastic breakdown of the difference between machine learning and data engineering

Data scientists are always on the lookout for tools that can streamline their workflows and boost productivity. With the ever-growing landscape of data science tools, it can be challenging to keep up with the latest and most efficient options available. In this listicle, we've curated a list of six essential tools that every data scientist should consider adding to their toolkit. From collaborative platforms to powerful visualization software and machine learning frameworks, these tools are designed to help you work smarter, not harder. #datasciencetools #productivityboost #worksmarter #efficiency #toolsforsuccess

Excited to share my latest machine learning 1 st project on data scaling techniques with small data set! 🚀💻 #MachineLearning #DataScaling In this project, I explored various data scaling techniques to enhance the performance of my machine learning model. The goal was to improve accuracy and convergence speed. 📊🔍 Implemented StandardScaler , MinMaxScaler, LabelEncoder, OneHotEncoder, shifting Analysis from scikit-learn. Exploring the impact of different scaling methods on model accuracy and stability. 📈🔬 https://lnkd.in/dctjj_SB

Have you ever wondered why MNIST, an image data of hand-written digits, is mainly found in CSV formats on various platforms? An image comes under the unstructured data type, and storage becomes hectic. But smartly, we can convert it into the structured format by reshaping the image of size 28x28 into a vector of 1x784. This can now be seen as 784 features present in a dataset. Enjoy Learning! #machinelearning #artificialintelligenceai #dataanalysis

📚 Day 5 of learning Data Structures and Algorithms! Today, we learned about the Intersection of Two Linked Lists. #DataStructures #Algorithms #LearningJourney #LearnInPublic #SoftwareEngineer Check it out here: https://lnkd.in/gXN5RDTE