Between Train And Test #8

Weekend Salutations To The MachineHackers,

Welcome to the MachineHack Weekly Newsletter, your one-stop shop for everything MachineHack-related. This week's edition is jam-packed with exciting data science news, insights, and updates. We cover everything from cutting-edge research to novel applications. So buckle up, grab a cup of coffee, and prepare to dive deep into the world of machine learning. Let's get this party started!

We start today by bringing together the best of articles written on MachineHack blogs:

1. Top Machine Learning Models every Data Scientist should know. [Link here]
2. Ensemble Learning Techniques. [Link here]
3. How to deal with Overfitting - Underfitting [Link here]
4. A simple introduction to Gradient Descent [Link here]
5. UniVet: A framework for Text-To-Speech Generation Based on a Given Voice Sample [Link here]

You may already be aware, as a machine learning data scientist, that data is at the heart of every AI application. Data-centric AI is a branch of artificial intelligence that emphasizes the value of data quality, data management, and data-driven decision making. Here are a few reasons why you should research data-centric AI:

• Better quality data leads to better models: In machine learning, the quality of the data used to train models is crucial. By studying data-centric AI, you can learn how to collect, clean, and preprocess data effectively, which can improve the accuracy and performance of your models.
• Learn how to manage large datasets: As the amount of data generated continues to grow exponentially, managing and analyzing large datasets has become a critical skill for machine learning data scientists. Studying data-centric AI can help you learn how to effectively manage large datasets, use distributed computing platforms, and leverage cloud computing technologies.

With this said we are almost coming to the end of the Text Data phase of Data Centric AI Competition 2023. To participate in this and learn Data Centric AI,?do click here and participate.?This competition is also featured at MIT’s IAP this January, where the first-ever course on Data-Centric AI was taught. The emerging science of DCAI focuses on improving ML models by improving the data, which is often the best way to improve the performance of practical ML applications. Check out the?course here.

Future Tease Updates

• We are working on a whole new updated experience for the Assessments, Hackathons, Practice and Blogs section of the platform.
• We are working on some Generative AI projects internally which will improve your experience on MachineHack.

Singularity Updates

Here is the weekly reminder that we are nearing singularity:

1. We Are All Going To Die [Link here]
2. A whole new storytelling UX powered by Generative AI [Link here]
3. Greater the AI, greater will be our dismissal [Link here]