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If tomorrow I were hit by a bus, got a concussion, and lost all of my memory.
I would be SAD, but I'd still want to learn ML engineering from scratch. And here is how I would do it in 2024, mostly in order:

Learn math:
- linear algebra: Matrices, vectors, linear equations and transformations,
- probability and statistics: Distributions, hypothesis testing, Bias-Variance Tradeoff, and conditional probability.
- calculus: Derivatives, integrals, gradient descent.

Learn Python:
- data structures: Lists, arrays, and dictionaries.
- libraries: NumPy, Pandas for data manipulations, scikit-learn for ml and Matplotlib for data viz.
- code organization and control flow: Functions, loops, and conditionals.

Data Preprocessing:
- learn how to handle missing values, duplicates, and outliers
- read on feature engineering
- try out data normalization and scaling

Core ML Algorithms:
- Supervised Learning: Linear and Logistic Regression, and Classification algorithms(K-NN, Decision Trees, SVM)
- Unsupervised Learning: Clustering (K-means) and dimensionality reduction techniques (PCA)
- Reinforcement Learning: Basics of agents, environments, and reward systems

Model Evaluation and Validation:
- learn metrics like accuracy, precision, recall, and F1 score
- cross-validation techniques
- learn about overfitting and underfitting, and how to address them

Get Hands-On Experience with Deployment:
- use datasets from Kaggle and/or models from Huggingface.
- learn to deploy with Flask, FastAPI, and on Cloud (AWS or Azure or GCP)
- participate in ml competitions or workshops.

Digest everything before diving into advanced topics like:
- ml frameworks like PyTorch, and TensorFlow
- neural networks, convolutional networks, and recurrent networks
- understand (NLP) and computer vision (CV) applications and ensemble techniques

Some frequently Asked SQL Interview Questions with Answers in data analyst interviews:

1. Write a SQL query to find the average purchase amount for each customer. Assume you have two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount).

SELECT c.CustomerID, c. Name, AVG(o.Amount) AS AveragePurchase
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
GROUP BY c.CustomerID, c. Name;

2. Write a query to find the employee with the minimum salary in each department from a table Employees with columns EmployeeID, Name, DepartmentID, and Salary.

SELECT e1.DepartmentID, e1.EmployeeID, e1 .Name, e1.Salary
FROM Employees e1
WHERE Salary = (SELECT MIN(Salary) FROM Employees e2 WHERE e2.DepartmentID = e1.DepartmentID);

3. Write a SQL query to find all products that have never been sold. Assume you have a table Products (ProductID, ProductName) and a table Sales (SaleID, ProductID, Quantity).

SELECT p.ProductID, p.ProductName
FROM Products p
LEFT JOIN Sales s ON p.ProductID = s.ProductID
WHERE s.ProductID IS NULL;

4. Given a table Orders with columns OrderID, CustomerID, OrderDate, and a table OrderItems with columns OrderID, ItemID, Quantity, write a query to find the customer with the highest total order quantity.

SELECT o.CustomerID, SUM(oi.Quantity) AS TotalQuantity
FROM Orders o
JOIN OrderItems oi ON o.OrderID = oi.OrderID
GROUP BY o.CustomerID
ORDER BY TotalQuantity DESC
LIMIT 1;

5. Write a SQL query to find the earliest order date for each customer from a table Orders (OrderID, CustomerID, OrderDate).

SELECT CustomerID, MIN(OrderDate) AS EarliestOrderDate
FROM Orders
GROUP BY CustomerID;

6. Given a table Employees with columns EmployeeID, Name, ManagerID, write a query to find the number of direct reports for each manager.

SELECT ManagerID, COUNT(*) AS NumberOfReports
FROM Employees
WHERE ManagerID IS NOT NULL
GROUP BY ManagerID;


7. Given a table Customers with columns CustomerID, Name, JoinDate, and a table Orders with columns OrderID, CustomerID, OrderDate, write a query to find customers who placed their first order within the last 30 days.

SELECT c.CustomerID, c. Name
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
WHERE o.OrderDate = (SELECT MIN(o2.OrderDate) FROM Orders o2 WHERE o2.CustomerID = c.CustomerID)
AND o.OrderDate >= CURRENT_DATE - INTERVAL '30 day';

GridSearchCV vs RandomizedSearchCV in Machine Learning: Differences, Advantages & Disadvantages of Each, and Use Cases

1. GridSearchCV
- Definition: GridSearchCV is an exhaustive search over specified parameter values for an estimator. It uses cross-validation to evaluate the performance of each combination of parameter values.

How it Works:
- Parameter Grid: Define a grid of parameters to search over.
- Exhaustive Search: Evaluate all possible combinations of parameters in the grid.
- Cross-Validation: For each combination, perform cross-validation to assess the model's performance.
- Best Parameters: Select the combination that results in the best performance based on a predefined metric (e.g., accuracy, F1-score).

2. RandomizedSearchCV
- Definition: RandomizedSearchCV performs a random search over specified parameter values for an estimator. It samples a fixed number of parameter settings from the specified distributions.

How it Works:
- Parameter Distributions: Define distributions from which to sample parameter values.
- Random Sampling: Randomly sample a fixed number of parameter combinations.
- Cross-Validation: For each sampled combination, perform cross-validation to assess the model's performance.
- Best Parameters: Select the combination that results in the best performance based on a predefined metric.

Advantages and Disadvantages
- GridSearchCV:
-- Advantages:
1. Exhaustive Search: Guarantees finding the optimal combination within the specified grid.
2. Deterministic: Always produces the same results for the same parameter grid and data.
-- Disadvantages:
1. Computationally Expensive: Evaluates all combinations, which can be very slow for large grids.
2. Scalability Issues: Not feasible for high-dimensional parameter spaces.

- RandomizedSearchCV:
-- Advantages:
1. Efficiency: Can be faster than GridSearchCV by evaluating a fixed number of parameter combinations.
2. Scalability: More feasible for high-dimensional parameter spaces.
3. Exploration: Can potentially find good parameter combinations that GridSearchCV might miss due to its limited grid.
-- Disadvantages:
1. Non-Exhaustive: May not find the optimal combination if the number of iterations is too low.
2. Randomness: Results can vary between runs unless a random seed is set.

Use Cases
- GridSearchCV:
1. Small Parameter Spaces: Suitable when the parameter grid is small and computational resources are sufficient.
2. High Precision: When the goal is to find the exact optimal parameters within the defined grid.
3. Limited Time Constraint: When there is enough time to perform an exhaustive search.
- RandomizedSearchCV:
1. Large Parameter Spaces: Suitable for larger and high-dimensional parameter spaces where an exhaustive search is impractical.
2. Time Efficiency: When there is a need to balance between time and performance, providing a good solution quickly.
3. Exploratory Analysis: Useful in the early stages of model tuning to quickly identify promising parameter regions.