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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Placement Resources:
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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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Join for more: https://t.me/TechAndEvents

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

Placement Material 💯🎯: https://topmate.io/sumit_kumar80/1151675

✅ 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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