High-level coding is not required for starting in ML - DSBoost #11
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High-level coding is not required for starting in ML - DSBoost #11
Welcome to the 11th issue of DSBoost, the weekly newsletter where you can discover interesting people in the ML/AI world, get the main takeaways of a relevant podcast, and stay up to date with the latest news in the field!
💬 Interview of the week
This week we interviewed Santhosh Kumar, who is a Machine Learning Engineer. Enjoy:
What did you study/are you studying (if your background is different from DS, how did you end up in the field)?
For my Undergraduate studies, I opted for Mechanical Engineering, Yup I am a strange guy 😅.
What are your favorite resource sites and books (ML/AI)?
For resource sites, I would say Coursera and Documentation of the particular library/tool & in terms of Books I like “The Machine Learning Bookcamp”, it's so practical.
What got you into your current role (portfolio, certification, etc.)?
I am currently a data scientist trainee and contribute to open-source projects. I'll give networking the most of the credit for these because they wouldn't be possible otherwise.
What do you enjoy the most in your work?
I like how new & strange challenges are and often get fascinated by implementing and tuning new/unheard Algorithms.
What tools do you use the most / favorite tools?
A lot to say. Most Frequent- Pandas, numpy, Matplotlib, Sklearn
Favorite - Seaborn, Tensorflow, Darts
Do you use ChatGPT or other Al tools during your work? If so, how do they help you? Do they change your approach to problems?
Indeed, I primarily use ChatGpt for debugging straightforward coding issues, Also I use other tools like Codesquire & tools like Grammarly, and Quillbot for non-technical reasons.
What is your favorite topic within the field?
I would say Neural Networks as it gets me curious.
Which one of the recent AI/ML models will have the most significant impact on the industry in your opinion?
I believe LLMs will have a significant impact because they will significantly enhance services like IVR, Copywriting, etc. in view of recently revealed Models.
What are you currently learning or improving (topics you are interested in nowadays)?
Recently, when I was learning for an open-source project, I fell in love with the Time series Forecasting topic.
What is the biggest mistake you've made? (preferably DS related)
Initially held back at starting ML topics thinking high-level coding knowledge is required, Basic is enough and the rest can be picked up along the way in my POV.
What is your most significant achievement? (preferably DS related)
I would say I am relatively new to the DS field, I would consider working on Flood prediction for an Open source project in Serbia as my Biggest achievement so far, Hoping to Beef up the Portfolio!
Can you share a fun fact about yourself?
I eat Biryani when stressed to ease myself.
🎙️ Podcast of the week
Art and Data Science: Her Kaggle Grandmaster Story (Andrada Olteanu) - KNN Ep. 85
Key takeaways:
It is useful to have a presence on Kaggle. It is not just the best place to learn, but it can serve as a portfolio site as well.
No one is God on Kaggle. All users learn from each other, but it is important to reference the code you use. Referencing can be a great way to start networking.
Your main goal should be learning from others’ code.
It may be intimidating at the beginning to post on these sites, but the more you post it will disappear.
Tips on becoming a grandmaster:
Quality is a must!
Take advantage of communities. Share notebooks on Twitter or on Discord.
Comment and explain your code, which should be clean!
One of the most important skills in this domain is to write code that others can read!
🧵 Featured threads
https://twitter.com/TivadarDanka/status/1645373062089695232?s=20
https://twitter.com/NickSinghTech/status/1645310945617428481?s=20
🤖 What happened this week?
AI-generated newsreader: Fedha, a blonde-haired AI-generated woman appeared on Kuwait News' Twitter feed over the weekend.
Meta plans to release its generative AI later this year
Elon Musk and Bill Gates have differing opinions on the future of AI, with Musk urging caution and Gates advocating for more investment in the technology.