𝟓 𝐅𝐫𝐞𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐀𝐠𝐞𝐧𝐭𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐂𝐨𝐝𝐢𝐧𝐠😍

Want to Create AI Automations & Agents Without Writing a Single Line of Code?🧑‍💻

These 5 free YouTube tutorials will take you from complete beginner to automation expert in record time.🧑‍🎓✨️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4lhYwhn

Just pure, actionable automation skills — for free.✅️

Python Libraries & Frameworks

COMMON TERMINOLOGIES IN PYTHON - PART 1

Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?

In this series, we would be looking at the common Terminologies in python.

It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:

IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.

Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately

System Python - This is the version of python that comes with your operating system

Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions

REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)

Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.

Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function

Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.

Note: A return value can be any of these variable types: handle, integer, object, or string

Script - This is a file where you store your python code in a text file and execute all of the code with a single command

Script files - this is a file containing a group of python scripts

React ♥️ for more

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

Learn Fundamental Skills with Free Online Courses & Earn Certificates

- AI
- GenAI
- Data Science
- BigData 
- Python
- UI/UX ,Cloud
- Machine Learning
- Cyber Security 

𝐋𝐢𝐧𝐤 👇:- 

https://pdlink.in/4ovjVWY

Enroll for FREE & Get Certified 🎓

How a SQL query gets executed internally - Lets see step by step!

We all know SQL, but most of us do not understand the internals of it.

Let me take an example to explain this better.

Select p.plan_name, count(plan_id) as total_count
From plans p
Join subscriptions s on s.plan_id=p.plan_id
Where p.plan_name !=’premium’
Group by p.plan_name
Having count(plan_id) > 100
Order by p.plan_name
Limit 10;

Step 01: Get the table data required to run the sql query
Operations: FROM, JOIN (From plans p, Join subscriptions s)

Step 02: Filter the data rows
Operations: WHERE (where p.plan_name=’premium’)

Step 03: Group the data
Operations: GROUP (group by p.plan_name)

Step 04: Filter the grouped data
Operations: HAVING (having count(plan_id) > 100)

Step 05: Select the data columns
Operations: SELECT (select p.plan_name, count(p.plan_id)

Step 06: Order the data
Operations: ORDER BY (order by p.plan_name)

Step 07: Limit the data rows
Operations: LIMIT (limit 100)

Knowing the Internals really help.

SQL Query Logical Order

𝐁𝐞𝐬𝐭 𝐖𝐚𝐲 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐒𝐐𝐋 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐅𝐫𝐞𝐞 𝐂𝐨𝐮𝐫𝐬𝐞𝐬, 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐒𝐢𝐭𝐞𝐬 & 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐏𝐫𝐞𝐩 😍

Whether you’re aiming for a data analytics career or preparing for top tech interviews, SQL is a non-negotiable skill🧑‍🎓✨️

With the right roadmap, you can go from absolute beginner to confident pro—without spending a single rupee.💰💥

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/45tpAUM

All The Best 🎊

Creating a data science and machine learning project involves several steps, from defining the problem to deploying the model. Here is a general outline of how you can create a data science and ML project:

1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data.

2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping.

3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks.

4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis.

5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model.

6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one.

7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics.

8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed.

9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible.

10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain.

Python Project Ideas 👆

🚀2025’s Most In-Demand IT Training Courses in Networking, Data Science, Artificial Intelligence, Cyber Security, and Cloud Computing including #Python#, #Excel#, #AWS#, #Azure#, #CompTIA#, #PMP#, #CCNA#, #CCNP#⬇️📕

What You Get (FOR FREE!):

✅Enroll Free Trainings:
https://www.spotoexam.com/free-training-sns/?id=snswyxbzyo

✅Free CCNA Training Courses:
https://www.spotoexam.com/ccna-free-course-sns/?id=snswyxbzyo

✅Free CCIE Training Courses:
https://www.spotoexam.com/studyccie-free-sns?id=snswyxbzyo

✅Free PMP Traing Courses:
https://www.spotoexam.com/pmp-free-course-sns/?id=snswyxbzyo

✅Download Free IT Ebooks:
https://www.spotoexam.com/networkacademy-sns/?id=snswyxbzyo

Whether you’re a complete beginner or looking to upskill, hopeful the FREE Study Materials will help all of you. No hidden fees!! No catch!! Just pure value.👍

🔗 Contact us for 1v1 Help: https://wa.link/bx8cf1

📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗶𝗻 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱/𝗣𝘂𝗻𝗲 😍

Looking to become a Data Analyst? It’s one of the most in-demand roles in tech — and the best part? No coding required!

🔥 Learn Data Analytics with Real-time Projects ,Hands-on Tools

✨ Highlights:
✅ 100% Placement Support
✅ 500+ Hiring Partners
✅ Weekly Hiring Drives

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄:- 👇

🔹 Hyderabad :- https://pdlink.in/4kFhjn3

🔹 Pune:- https://pdlink.in/45p4GrC

Hurry Up 🏃‍♂️! Limited seats are available.

Essential Python and SQL topics for data analysts 😄👇

Python Topics:

1. Data Structures
   - Lists, Tuples, and Dictionaries
   - NumPy Arrays for numerical data

2. Data Manipulation
   - Pandas DataFrames for structured data
   - Data Cleaning and Preprocessing techniques
   - Data Transformation and Reshaping

3. Data Visualization
   - Matplotlib for basic plotting
   - Seaborn for statistical visualizations
   - Plotly for interactive charts

4. Statistical Analysis
   - Descriptive Statistics
   - Hypothesis Testing
   - Regression Analysis

5. Machine Learning
   - Scikit-Learn for machine learning models
   - Model Building, Training, and Evaluation
   - Feature Engineering and Selection

6. Time Series Analysis
   - Handling Time Series Data
   - Time Series Forecasting
   - Anomaly Detection

7. Python Fundamentals
   - Control Flow (if statements, loops)
   - Functions and Modular Code
   - Exception Handling
   - File

SQL Topics:

1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters

2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY

3. Data Filtering
- WHERE Clause
- ORDER BY

4. Data Joins
- JOIN Operations
- Subqueries

5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization

6. Database Management
- Connecting to Databases
- SQLAlchemy

7. Database Design
- Data Types
- Normalization

Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!

Python Resources - https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Hope it helps :)