ML interview Question 📚

What is Quantization in machine learning?

Quantization the process of reducing the precision of the numbers used to represent a model's parameters, such as weights and activations. This is often done by converting 32-bit floating-point numbers (commonly used in training) to lower precision formats, like 16-bit or 8-bit integers.

Quantization is primarily used during model inference to:
1. Reduce model size: Lower precision numbers require less memory.
2. Improve computational efficiency: Operations on lower-precision data types are faster and require less power.
3. Speed up inference: Smaller models can be loaded faster, improving performance on edge devices like smartphones or IoT devices.

Quantization can lead to a small loss in model accuracy, as reducing precision can introduce rounding errors. But in many cases, the trade-off between accuracy and efficiency is worthwhile, especially for deployment on resource-constrained devices.

There are different types of quantization:
1. Post-training quantization: Applied after the model has been trained.
2.Quantization-aware training (QAT): Takes quantization into account during the training process to minimize the accuracy drop.

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🛠️ Must-Know SQL Commands & Functions ✅

1. SELECT – Retrieve data 
   › SELECT * FROM customers;

2. WHERE – Filter rows 
   › SELECT * FROM orders WHERE amount > 500;

3. ORDER BY – Sort results 
   › SELECT name FROM users ORDER BY age DESC;

4. GROUP BY – Aggregate data 
   › SELECT department, COUNT(*) FROM employees GROUP BY department;

5. JOIN – Combine tables 
   › SELECT a.name, b.salary FROM employees a JOIN salaries b ON a.id = b.emp_id;

6. INSERT INTO – Add new data 
   › INSERT INTO users (name, age) VALUES ('John', 30);

7. UPDATE – Modify existing data 
   › UPDATE products SET price = 100 WHERE id = 1;

8. DELETE – Remove data 
   › DELETE FROM logs WHERE date < '2023-01-01';

9. LIKE – Pattern matching 
   › SELECT * FROM customers WHERE name LIKE 'A%';

10. LIMIT – Restrict result rows 
    › SELECT * FROM sales LIMIT 10;

💡 Tip: Practice on real datasets. Learn JOIN and GROUP BY early—they’re game changers!

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✅ Statistics & Probability Cheatsheet 📚🧠

📌 Descriptive Statistics:
⦁  Mean = (Σx) / n
⦁  Median = Middle value
⦁  Mode = Most frequent value
⦁  Variance (σ²) = Σ(x - μ)² / n
⦁  Std Dev (σ) = √Variance
⦁  Range = Max - Min
⦁  IQR = Q3 - Q1

📌 Probability Basics:
⦁  P(A) = Outcomes A / Total Outcomes
⦁  P(A ∩ B) = P(A) × P(B) (if independent)
⦁  P(A ∪ B) = P(A) + P(B) - P(A ∩ B)
⦁  Conditional: P(A|B) = P(A ∩ B) / P(B)
⦁  Bayes’ Theorem: P(A|B) = [P(B|A) × P(A)] / P(B)

📌 Common Distributions:
⦁  Binomial (fixed trials)
⦁  Normal (bell curve)
⦁  Poisson (rare events over time)
⦁  Uniform (equal probability)

📌 Inferential Stats:
⦁  Z-score = (x - μ) / σ
⦁  Central Limit Theorem: sampling dist ≈ Normal
⦁  Confidence Interval: CI = x‌ ± z*(σ/√n)

📌 Hypothesis Testing:
⦁  H₀ = No effect; H₁ = Effect present
⦁  p-value < α → Reject H₀
⦁  Tests: t-test (small samples), z-test (known σ), chi-square (categorical data)

📌 Correlation:
⦁  Pearson: linear relation (–1 to 1)
⦁  Spearman: rank-based correlation

🧪 Tools to Practice: 
Python packages: scipy.stats, statsmodels, pandas 
Visualization: seaborn, matplotlib

💡 Quick tip: Use these formulas to crush interviews and build solid ML foundations!

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📊 Data Analytics – Key Concepts for Beginners 🔍

1️⃣ What is Data Analytics?
– The process of examining data sets to draw conclusions using tools, techniques, and statistical models.

2️⃣ Types of Data Analytics:
- Descriptive: What happened?
- Diagnostic: Why did it happen?
- Predictive: What could happen?
- Prescriptive: What should we do?

3️⃣ Common Tools:
- Excel
- SQL
- Python (Pandas, NumPy)
- R
- Tableau / Power BI
- Google Data Studio

4️⃣ Basic Skills Required:
- Data cleaning & preprocessing
- Data visualization
- Statistical analysis
- Querying databases
- Business understanding

5️⃣ Key Concepts:
- Data types (numerical, categorical)
- Mean, median, mode
- Correlation vs causation
- Outliers & missing values
- Data normalization

6️⃣ Important Libraries (Python):
- Pandas (data manipulation)
- Matplotlib / Seaborn (visualization)
- Scikit-learn (machine learning)
- Statsmodels (statistical modeling)

7️⃣ Typical Workflow:
Data Collection → Cleaning → Analysis → Visualization → Reporting

💡 Tip: Always ask the right business question before jumping into analysis.

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🔹 Top 10 SQL Functions/Commands Commonly Used in Data Analysis 📊

1️⃣ SELECT
– Used to retrieve specific columns from a table.
SELECT name, age FROM users;

2️⃣ WHERE
– Filters rows based on a condition.
SELECT × FROM sales WHERE region = 'North';

3️⃣ GROUP BY
– Groups rows that have the same values into summary rows.
SELECT region, SUM(sales) FROM sales GROUP BY region;

4️⃣ ORDER BY
– Sorts the result by one or more columns.
SELECT * FROM customers ORDER BY created_at DESC;

5️⃣ JOIN
– Combines rows from two or more tables based on a related column.
SELECT a.name, b.salary
FROM employees a
JOIN salaries b ON a.id = b.emp_id;

6️⃣ COUNT() / SUM() / AVG() / MIN() / MAX()
– Common aggregate functions for metrics and summaries.
SELECT COUNT(×) FROM orders WHERE status = 'completed';

7️⃣ HAVING
– Filters after a GROUP BY (unlike WHERE, which filters before).
SELECT department, COUNT() FROM employees GROUP BY department HAVING COUNT() > 10;

8️⃣ LIMIT
– Restricts number of rows returned.
SELECT * FROM products LIMIT 5;

9️⃣ CASE
– Implements conditional logic in queries.
SELECT name,
CASE
WHEN score >= 90 THEN 'A'
WHEN score >= 75 THEN 'B'
ELSE 'C'
END AS grade
FROM students;

🔟 DATE functions (NOW(), DATE_PART(), DATEDIFF(), etc.)
– Handle and extract info from dates.
SELECT DATE_PART('year', order_date) FROM orders;

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1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/

2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/

3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT

4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv

5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf

6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG

7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7

8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8

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✅ Master Exploratory Data Analysis (EDA) 🔍💡

1️⃣ Understand Your Dataset 
› Check shape, column types, missing values 
› Use: df.info(), df.describe(), df.isnull().sum()

2️⃣ Handle Missing & Duplicate Data 
› Remove or fill missing values 
› Use: dropna(), fillna(), drop_duplicates()

3️⃣ Univariate Analysis 
› Analyze one feature at a time 
› Tools: histograms, box plots, value_counts()

4️⃣ Bivariate & Multivariate Analysis 
› Explore relations between features 
› Tools: scatter plots, heatmaps, pair plots (Seaborn)

5️⃣ Outlier Detection 
› Use box plots, Z-score, IQR method 
› Crucial for clean modeling

6️⃣ Correlation Check 
› Find highly correlated features 
› Use: df.corr() + Seaborn heatmap

7️⃣ Feature Engineering Ideas 
› Create or remove features based on insights

🛠 Tools: Python (Pandas, Matplotlib, Seaborn)

🎯 Mini Project: Try EDA on Titanic or Iris dataset!

Data Science। Roadmap:💯🎯

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DSA INTERVIEW QUESTIONS AND ANSWERS

1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.

2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.

3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.

4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.

5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).

6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.

7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.

8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.

Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).

Time complexity: best case O(n2); worst O(n2)

Space complexity: worst O(1)

9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them

10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.

11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect

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