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.

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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.

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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.

Welcome to Rose!

Rose is primarily a group management bot, and has limited functionality in channels.

Channel features include:
- Log channels
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- Joining federations

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
How do you handle SVM's bias-variance tradeoff?

Tuning the SVM’s 𝗖 and 𝗴𝗮𝗺𝗺𝗮 parameters plays a crucial role in managing the model's bias-variance tradeoff, directly influencing the model's complexity, generalizability, and how well it can handle unseen data.

𝗧𝗵𝗲 𝗖 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿
Effect on Margins: C controls the penalty for misclassified points. A high C forces the model to classify training points more accurately, potentially reducing the margin and creating a more complex decision boundary that fits the training data closely. This reduces bias but increases variance, risking overfitting.

High C: Low bias (since the model tries to perfectly classify the training data) but high variance (overfitting).
Low C: High bias (since the model allows more misclassifications, resulting in a larger margin) but low variance (underfitting).

𝗧𝗵𝗲 𝗴𝗮𝗺𝗺𝗮 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 (𝗳𝗼𝗿 𝗡𝗼𝗻-𝗹𝗶𝗻𝗲𝗮𝗿 𝗞𝗲𝗿𝗻𝗲𝗹𝘀)
Effect on Feature Space: gamma determines the influence of each training point in the decision boundary by controlling the scale of the kernel function. A high gamma restricts influence to points very close to the decision boundary, creating more complex, localized boundaries. This can lead to high variance and overfitting.

High gamma: Low bias, high variance (overfitting) as the model can create extremely localized, intricate boundaries.
Low gamma: High bias, low variance (underfitting) as the model forms smoother, simpler decision boundaries.

Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables

2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries

3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting

2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)

3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)

2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX

3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.

Show some ❤️ if you're ready to elevate your data science game! 📊

ENJOY LEARNING 👍👍

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
How would you extend SVM for multi-class classification?

Two common ways are -

𝗢𝗻𝗲-𝘃𝘀-𝗥𝗲𝘀𝘁 (𝗢𝘃𝗥) (𝗼𝗿 𝗢𝗻𝗲-𝘃𝘀-𝗔𝗹𝗹)
Each classifier is trained to separate one class from all others. For K classes, OvR builds K SVM models, where each model is trained with the class of interest labeled as positive and all other classes labeled as negative. For a new instance, each classifier outputs a score, and the class with the highest score is chosen as the predicted class.

Pros of OvR -
🧤 Computationally efficient, especially when there are many classes, as it requires fewer classifiers.
🧤 Works well when the dataset is large, and class overlap isn’t significant.

Cons of OvR -
🔻 The negative class for each classifier can be a mix of very different classes, which can make the boundary between classes less distinct.
🔻 May struggle with overlapping classes, as it requires each classifier to make broad distinctions between one class and all others.

𝗢𝗻𝗲-𝘃𝘀-𝗢𝗻𝗲 (𝗢𝘃𝗢)
This method involves building a separate binary classifier for each pair of classes, resulting in (K(K−1))/2 classifiers for K classes. Each classifier learns to distinguish between just two classes. For classification, each binary classifier votes for a class, and the class with the most votes is selected.

Pros of OvO -
🧤 Creates simpler decision boundaries, as each classifier only has to separate two classes.
🧤 Often yields higher accuracy for complex, overlapping classes since it doesn't force each classifier to distinguish between all classes.

Cons of OvO -
🔻 Computationally intensive for large numbers of classes, due to the higher number of classifiers.
🔻 Prediction time can be slower as it requires voting among all classifiers, which can be significant if there are many classes.

𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗢𝘃𝗥 𝗮𝗻𝗱 𝗢𝘃𝗢
The choice between OvR and OvO depends largely on the specific dataset characteristics and computational constraints:
👉 If computational resources are limited and the number of classes is high, OvR may be preferred, as it requires fewer classifiers and is faster to train and predict with.
👉 If accuracy is critical and the classes overlap significantly, OvO often performs better since it learns more specialized decision boundaries for each pair of classes.