📚 Understanding Linear Regression Through a Student’s Journey

Let’s take a trip back to your student days to understand linear regression, one of the most fundamental concepts in machine learning.

Alex, a dedicated student, is trying to predict their final exam score based on the number of hours they study each week. They gather data over the semester and notice a pattern—more hours studied generally leads to higher scores. To quantify this relationship, Alex uses linear regression.

What is Linear Regression?
Linear regression is like drawing a straight line through a scatterplot of data points that best predicts the dependent variable (exam scores) from the independent variable (study hours). The equation of the line looks like this:

Score= Intercept + Slope * Study Hours

Here, the intercept is the score Alex might expect with zero study hours (hopefully not too low!), and the slope shows how much the score increases with each additional hour of study.

Linear regression works under several assumptions:

1. Linearity: The relationship between study hours and exam scores should be linear. If Alex studies twice as much, their score should increase proportionally. But what if the benefit of extra hours diminishes over time? That’s where the linearity assumption can break down.

2. Independence: Each data point (study hours vs. exam score) should be independent of others. If Alex’s friends start influencing their study habits, this assumption might be violated.

3. Homoscedasticity: The variance of errors (differences between predicted and actual scores) should be consistent across all levels of study hours. If Alex’s predictions are more accurate for students who study a little but less accurate for those who study a lot, this assumption doesn’t hold.

4. Normality of Errors: The errors should follow a normal distribution. If the errors are skewed, it might suggest that factors beyond study hours are influencing scores.


Despite its simplicity, linear regression isn’t perfect. Here are a few limitations of linear regression.

- Non-Linearity:If the relationship between study hours and exam scores isn’t linear (e.g., diminishing returns after a certain point), linear regression might not capture the true pattern.

- Outliers: A few students who study a lot but still score poorly can heavily influence the regression line, leading to misleading predictions.

- Overfitting: If Alex adds too many variables (like study environment, type of study material, etc.), the model might become too complex, fitting the noise rather than the true signal.

In Alex’s case, while linear regression provides a simple and interpretable model, it’s important to remember these assumptions and limitations. By understanding them, Alex can better assess when to rely on linear regression and when it might be necessary to explore more advanced methods.

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Making all my interview experiences public so that I am forced to learn new things :)

Machine Learning
1. Explain 'irreducible error' with the help of a real life example
2. What two models are compared while calculating R2 in a regression setup?
3. How do you evaluate clustering algorithms?
4. What is Gini and Cross-entropy? What are the minimum and maximum value for both?
5. What does MA component mean in ARIMA models?
6. You are a senior data scientist and one of your team members suggests you to use KNN with 70:30 train test split , what must you immediately correct in his approach?

AWS & DevOps
1. Run time limit for Lambda functions.
2. What do you mean by a serverless architecture?
3. Tell me any four Docker commands.
4. What is Git Checkout?
5. How does ECS help container orchestration and how could you make it serverless?
6. Can you run a docker image locally?

Generative AI
1. Most important reason why one may just still use RAG when you have LLMs offering context window in million tokens
2. How do you handle a situation when tokens in your retrieved context exceed tokens that your LLM supports?
3. What is context precision and context recall in the context of RAG?
4. What is hybrid search and what are the advantages / limitations?
5. What inputs are shared when you do recursive chunking?

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🌟

SQL window functions are key to cracking technical interviews and optimizing your SQL queries. They’re often a focal point in data-focused roles, where showing your knowledge of these functions can set you apart. By mastering these functions, you can solve complex problems efficiently and design more effective databases, making you a valuable asset in any data-driven organization.

To make it easier to understand, I have divided SQL window functions into three main categories: Aggregate, Ranking, and Value functions.

1. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀

Aggregate functions like AVG(), SUM(), COUNT(), MIN(), and MAX() compute values over a specified window, such as running totals or averages. These functions help optimize queries that require complex calculations while retaining row-level details.

2. 𝗥𝗮𝗻𝗸𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀

Ranking functions such as ROW_NUMBER(), RANK(), and DENSE_RANK() assign ranks, dense ranks, or row numbers based on a specified order within a partition. These are crucial for solving common interview problems and creating optimized queries for ordered datasets.

3. 𝗩𝗮𝗹𝘂𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀

Value functions like LAG(), LEAD(), FIRST_VALUE(), and LAST_VALUE() allow you to access specific rows within your window. These functions are essential for trend analysis, comparisons, and detecting changes over time.

I’ve broken down each category with examples, sample code, expected output, interview questions, and even ChatGPT prompts to help you dive deeper into SQL window functions. Whether you're preparing for an interview or looking to optimize your SQL queries, understanding these functions is a game-changer.

ARIMA is easier than you think.

Explained in 3 minutes.

ARIMA stands for AutoRegressive Integrated Moving Average. It’s a popular method used for forecasting time series data.

In simple terms, ARIMA helps us predict future values based on past data. It combines three main components: autoregression, differencing, and moving averages.

Let's breakdown those three parts:

1️⃣ Autoregression means we use past values to predict future ones.

2️⃣ Differencing helps to make the data stationary, which means it has a consistent mean over time.

3️⃣ Moving averages smooth out short-term fluctuations.

Using ARIMA can help you make better decisions, manage inventory, and boost profits. It’s a powerful tool for anyone looking to understand trends in their data!

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Recently, I completed two rounds of technical interviews for an ML Engineer role focused on LLMs, which pushed me to dive deep into concepts like attention mechanisms, tokenization, RAG, and GPU parallelism. I ended up creating a 30-page document of notes to organize my learnings.

To further solidify these concepts, I built three projects:
1️⃣ Two follow-along RAG-based "ChatPDF" projects with slight variations—one using Google Gen AI + FAISS, and another using HuggingFace + Pinecone.
2️⃣ A custom web scraper project that creates a vector store from website data and leverages advanced RAG techniques (like top-k retrieval and reranking) to provide LLM-driven answers for queries about the website.

Although the company ultimately chose another candidate who better matched their specific requirements, I received positive feedback on both rounds, and I’m excited to continue building on what I’ve learned. Onward and upward!

Notes: https://lnkd.in/dAvJjawc
Google Gen AI + FAISS+ Streamlit: https://lnkd.in/d7hPEz8c
Huggingface + Pinecone:https://lnkd.in/dgbJTSpq
Web scraper + Advanced RAG: https://lnkd.in/ddJfbBcF

P.S. you would need your own API keys for Google Gen AI, Pinecone and Cohere. All these are free to use for the purposes of small projects and for learning.

https://youtu.be/ZOJvKbbc6cw


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In my previous team at IBM, we hired over 450 AI Engineers worldwide. They are working on Generative AI pilots for our IBM customers across various industries.

Thousands applied, and we developed a clear rubric to identify the best candidates.

Here are 8 concise tips to help you ace a technical AI engineering interview:

𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.

𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.

𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.

𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.

𝟱. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.

𝟲. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.

𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.

𝟴. 𝗔𝘀𝗸 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.

https://youtu.be/w2anY0hYsL0

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Resume key words for data scientist role explained in points:

1. Data Analysis:
- Proficient in extracting, cleaning, and analyzing data to derive insights.
- Skilled in using statistical methods and machine learning algorithms for data analysis.
- Experience with tools such as Python, R, or SQL for data manipulation and analysis.

2. Machine Learning:
- Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.
- Experience in model development, evaluation, and deployment.
- Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.

3. Data Visualization:
- Ability to present complex data in a clear and understandable manner through visualizations.
- Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.
- Understanding of best practices in data visualization for effective communication of findings.

4. Big Data:
- Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.
- Knowledge of distributed computing principles and tools for processing and analyzing big data.
- Ability to optimize algorithms and processes for scalability and performance.

5. Problem-Solving:
- Strong analytical and problem-solving skills to tackle complex data-related challenges.
- Ability to formulate hypotheses, design experiments, and iterate on solutions.
- Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.


Resume key words for a data analyst role

1. SQL (Structured Query Language):
- SQL is a programming language used for managing and querying relational databases.
- Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.

2. Python/R:
- Python and R are popular programming languages used for data analysis and statistical computing.
- Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.

3. Data Visualization:
- Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.
- Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.

4. Statistical Analysis:
- Statistical analysis involves applying statistical methods to analyze and interpret data.
- Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.

5. Data-driven Decision Making:
- Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.
- Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.

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Tokenization in NLP is the first essential step in breaking down text into smaller pieces, often referred to as "tokens." This looks simple but is the foundation of everything that follows in NLP tasks from text classification to machine translation.


For example, in a sentence like "I love learning NLP", tokenization splits it into four tokens: ["I", "love", "learning", "NLP"].

But it can get more complicated with contractions, punctuations and languages without clear word boundaries like Chinese.

That’s where techniques like Byte-Pair Encoding (BPE) and WordPiece help to handle these complexities.

Mastering tokenization helps NLP models capture the right meaning from the data.

SQL Interview Questions (0-5 Year Experience)!

Are you preparing for a SQL interview? Here are some essential SQL concepts to review:

𝐁𝐚𝐬𝐢𝐜 𝐒𝐐𝐋 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬:

1. What is SQL, and why is it important in data analytics?
2. Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
3. What is the difference between WHERE and HAVING clauses?
4. How do you use GROUP BY and HAVING in a query?
5. Write a query to find duplicate records in a table.
6. How do you retrieve unique values from a table using SQL?
7. Explain the use of aggregate functions like COUNT(), SUM(), AVG(), MIN(), and MAX().
8. What is the purpose of a DISTINCT keyword in SQL?

𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐒𝐐𝐋:

1. Write a query to find the second-highest salary from an employee table.
2. What are subqueries and how do you use them?
3. What is a Common Table Expression (CTE)? Give an example of when to use it.
4. Explain window functions like ROW_NUMBER(), RANK(), and DENSE_RANK().
5. How do you combine results of two queries using UNION and UNION ALL?
6. What are indexes in SQL, and how do they improve query performance?
7. Write a query to calculate the total sales for each month using GROUP BY.

𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋:

1. How do you optimize a slow-running SQL query?
2. What are views in SQL, and when would you use them?
3. What is the difference between a stored procedure and a function in SQL?
4. Explain the difference between TRUNCATE, DELETE, and DROP commands.
5. What are windowing functions, and how are they used in analytics?
6. How do you use PARTITION BY and ORDER BY in window functions?
7. How do you handle NULL values in SQL, and what functions help with that (e.g., COALESCE, ISNULL)?

Most Important Mathematical Equations in Data Science!

1️⃣ Gradient Descent: Optimization algorithm minimizing the cost function.
2️⃣ Normal Distribution: Distribution characterized by mean μ\muμ and variance σ2\sigma^2σ2.
3️⃣ Sigmoid Function: Activation function mapping real values to 0-1 range.
4️⃣ Linear Regression: Predictive model of linear input-output relationships.
5️⃣ Cosine Similarity: Metric for vector similarity based on angle cosine.
6️⃣ Naive Bayes: Classifier using Bayes’ Theorem and feature independence.
7️⃣ K-Means: Clustering minimizing distances to cluster centroids.
8️⃣ Log Loss: Performance measure for probability output models.
9️⃣ Mean Squared Error (MSE): Average of squared prediction errors.
🔟 MSE (Bias-Variance Decomposition): Explains MSE through bias and variance.
1️⃣1️⃣ MSE + L2 Regularization: Adds penalty to prevent overfitting.
1️⃣2️⃣ Entropy: Uncertainty measure used in decision trees.
1️⃣3️⃣ Softmax: Converts logits to probabilities for classification.
1️⃣4️⃣ Ordinary Least Squares (OLS): Estimates regression parameters by minimizing residuals.
1️⃣5️⃣ Correlation: Measures linear relationships between variables.
1️⃣6️⃣ Z-score: Standardizes value based on standard deviations from mean.
1️⃣7️⃣ Maximum Likelihood Estimation (MLE): Estimates parameters maximizing data likelihood.
1️⃣8️⃣ Eigenvectors and Eigenvalues: Characterize linear transformations in matrices.
1️⃣9️⃣ R-squared (R²): Proportion of variance explained by regression.
2️⃣0️⃣ F1 Score: Harmonic mean of precision and recall.
2️⃣1️⃣ Expected Value: Weighted average of all possible values.

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