✅ 30 AI Terms Explained....

Your biggest enemy 𝐅𝐄𝐀𝐑 𝐨𝐟 𝐑𝐞𝐣𝐞𝐜𝐭𝐢𝐨𝐧

People hesitate to apply for many opportunities just because of fear of rejection.

However, not applying means you are automatically rejecting yourself. They usually think I will start applying after 6-8 months with full preparation.

Do you really think it will work ??? Interview calls usually take months 😅

My suggestion would be to start applying after 10 days to 1 month of preparation . Try to give as many interviews as you can. In this way, you will learn 👇🏻

🌴 Frequently asked questions
🌴 Interview pattern
🌴 How to tweak your answers?

Give a try ,even in the worst scenario, you will get some interview experience. That experience will eventually help you in the future

All the best 👍👍

ML Engineer Roadmap 👆

Cheatsheet Machine Learning Algorithms🌟

Andrew Ng just released two new AI Python courses for beginners!

The course teaches how to write code using AI.

If you're thinking about learning to code, now is the perfect time to do so.

https://deeplearning.ai/short-courses/ai-python-for-beginners/

CHECKING FRAUD

Learning and Practicing SQL: Resources and Platforms

1. https://sqlbolt.com/
2. https://sqlzoo.net/
3. https://www.codecademy.com/learn/learn-sql
4. https://www.w3schools.com/sql/
5. https://www.hackerrank.com/domains/sql
6. https://www.windowfunctions.com/
7. https://selectstarsql.com/
8. https://quip.com/2gwZArKuWk7W
9. https://leetcode.com/problemset/database/
10. http://thedatamonk.com

ChatGPT can help you land your dream job twice as fast. Here are 8 powerful ChatGPT prompts will 10X your interview chances.

Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166

1. Customizing Your Resume ChatGPT prompt: "Can you make changes to my resume to fit the [Job Title] role at [Company]? Here's the job denoscription: [Paste Job Denoscription], and resume: [Paste Resume]."

2. Creating a Professional Summary ChatGPT prompt: "Using my resume, can you create a professional summary for me aligned to this [Job Title]." [Paste Resume]

3. Understanding Job Denoscriptions ChatGPT prompt: "What are the main responsibilities for this job? Please list the top three responsibilities required for [Job Title]." [Paste Job Denoscription]

4. Improving Your Resume Bullets ChatGPT prompt: "Please rewrite this bullet point from my resume using clear and impactful language while highlighting my accomplishments. [Paste Resume]"

5. Writing a LinkedIn Summary ChatGPT prompt: "Can you write a summary for my LinkedIn profile using my resume [Paste Resume]?"

6. Job Applications with ChatGPT ChatGPT prompt: "Can you identify my [Skills] experience from my resume [Paste Resume]? Please describe my specific [Skills] experience in conversational, clear language as if you were me."

7. Crafting Your Cover Letter ChatGPT prompt: "Can you write a personalized cover letter for the [Job Title] position at [Company]? Here's the job denoscription: [Paste Job Denoscription], and my current resume: [Paste Resume]."

8. Preparing for Interviews ChatGPT prompt: "What skills and experiences should I emphasize during an interview for the [Job Title] role in [Specific Industry]?"

ENJOY LEARNING 👍👍

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.

Like if you need similar content 😄👍

SQL Basics for Beginners: Must-Know Concepts

1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.

2. SQL Syntax
SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).

3. SQL Data Types
Databases store data in different formats. The most common data types are:
- INT (Integer): For whole numbers.
- VARCHAR(n) or TEXT: For storing text data.
- DATE: For dates.
- DECIMAL: For precise decimal values, often used in financial calculations.

4. Basic SQL Queries
Here are some fundamental SQL operations:

- SELECT Statement: Used to retrieve data from a database.

     SELECT column1, column2 FROM table_name;
     

- WHERE Clause: Filters data based on conditions.

     SELECT * FROM table_name WHERE condition;
     

- ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     

- LIMIT: Limits the number of rows returned.

     SELECT * FROM table_name LIMIT 5;
     

5. Filtering Data with WHERE Clause
The WHERE clause helps you filter data based on a condition:

   SELECT * FROM employees WHERE salary > 50000;
   

You can use comparison operators like:
- =: Equal to
- >: Greater than
- <: Less than
- LIKE: For pattern matching

6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.

     SELECT COUNT(*) FROM table_name;
     

- SUM(): Adds up values in a column.

     SELECT SUM(salary) FROM employees;
     

- AVG(): Calculates the average value.

     SELECT AVG(salary) FROM employees;
     

- GROUP BY: Groups rows that have the same values into summary rows.

     SELECT department, AVG(salary) FROM employees GROUP BY department;
     

7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.

     SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     

- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.

     SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     

8. Inserting Data
To add new data to a table, you use the INSERT INTO statement:

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   

9. Updating Data
You can update existing data in a table using the UPDATE statement:

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   

10. Deleting Data
To remove data from a table, use the DELETE statement:

    DELETE FROM employees WHERE name = 'John Doe';
    

Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier

Like this post if you need more 👍❤️

Hope it helps :)

Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so.

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.

Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.

Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

React ❤️ for more free resources