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Hope this was helpful for you

Optimize your resume to get more interviews

Many job seekers don’t get enough interviews even after applying for dozens of jobs. Why? Companies use Applicant Tracking Systems (ATS) to search and filter resumes by keywords. The Jobscan resume scanner helps you optimize your resume keywords for each job listing so that your application gets found by recruiters.

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Machine Learning Interview Questions

Famous ML Project Ideas
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BCG Hiring ML Engineer
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Requirements:
Very high proficiency in Python programming language, knowledge of other languages.
such as R, Java would be a plus.
Knowledge of various AI/ML models including deep learning models.
Knowledge of Generative AI stack – Large Language Models / Foundation Models, vector databases, orchestration stack.
Hand on experience in building AI orchestration with frameworks like LangChain.
Knowledge of vector databases e.g., Pinecone, Chroma etc.
Deep understanding of data processing frameworks e.g., Data Bricks, Airflow etc.
Knowledge of API frameworks Django, Flask etc.
Understanding of cloud data & AI stack on AWS / Azure / GCP is preferred.

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Data Science Interview Questions
https://www.linkedin.com/posts/sql-analysts_data-science-interview-questions-activity-7148900952924737536-Vbwa?utm_source=share&utm_medium=member_android

What if we all are just a part of AI experiment by god- human’s life created as a unique dataset, contributing to the overall learning process. Creator contemplates the diversity of experiences encoded in the training data, like the complex interplay of joy, sorrow, love, hatred and conflict.

Read more.....

Modern Computer Vision with Pytorch
V. Kishore Ayyadevara, 2020

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Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

By following these tips, you can be well-prepared for your next data science interview. Good luck!

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SQL Project Handbook 👇👇
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Here are 5 fresh Project ideas for Data Analysts 👇
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Data Science Interview Preparation 👇👇
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SQL Cheatsheet 👇👇
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Prompt Engineering in itself does not warrant a separate job.

Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.

You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.

The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.

Popular Python packages for data science:

1. NumPy: For numerical operations and working with arrays.
2. Pandas: For data manipulation and analysis, especially with data frames.
3. Matplotlib and Seaborn: For data visualization.
4. Scikit-learn: For machine learning algorithms and tools.
5. TensorFlow and PyTorch: Deep learning frameworks.
6. SciPy: For scientific and technical computing.
7. Statsmodels: For statistical modeling and hypothesis testing.
8. NLTK and SpaCy: Natural Language Processing libraries.
9. Jupyter Notebooks: Interactive computing and data visualization.
10. Bokeh and Plotly: Additional libraries for interactive visualizations.