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Code with Mahzaib, Lahore (2025)

30/07/2023

Post Collaborated with .science.beginners 🙌.

Step by Step Guide to Make a Perfect Data Science Resume.

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25/07/2023

So you can find Machine Learning projects in Python with Datasets available.

Find projects from the link in the bio!

24/07/2023

Sure! Here is a list of some commonly used Python functions in pandas, presented in bullet points
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22/07/2023

SHAP (SHapley Additive exPlanations) Python package is a popular library used for explaining the output of machine learning models. It provides a unified way to interpret the predictions made by complex models and helps users understand the importance of different features in influencing model predictions.

Here are some of the main functions and classes provided by the SHAP package:

1. `shap.Explainer`: This class is used to create an explainer object, which is essential for computing SHAP values. It takes a trained model and a data set as input and prepares the explainer for further computations.

2. `explainer.shap_values`: This function computes the SHAP values for a given instance or a set of instances. It explains the output of the model based on the provided input data.

3. `explainer.expected_value`: This function returns the expected model output for a given dataset. It represents the average model prediction over the dataset.

4. `shap.summary_plot`: This function generates a summary plot to display the impact of each feature on model predictions. It provides a quick overview of feature importances.

5. `shap.dependence_plot`: This function creates dependence plots, allowing you to visualize how the model's output changes concerning a specific feature.

6. `shap.force_plot`: This function generates a force plot, which provides an individualized explanation for a single prediction, breaking down the contribution of each feature.

7. `shap.waterfall_plot`: This function creates a waterfall plot to show the step-by-step contributions of each feature to the final prediction.

8. `shap.force_plot`: This function creates a force plot for an individual prediction, similar to `shap.force_plot`, but with more detailed information for each feature.

9. `shap.TreeExplainer`: This class is a specialized explainer for tree-based models, which can offer faster computations for these types of models.

10. `shap.DeepExplainer`: This class is a specialized explainer for deep learning models, designed to handle complex architectures like TensorFlow and Keras.

19/05/2023

Thanks for being a part of Code with Mahzaib. I am grateful to all of you for being a part of my family.

I am excited to share the most adavance knowledge to all of you guys.

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12/05/2023

Post Collaborated with .science.beginners 🤝

Big data is a critical component of data science, as it provides the necessary raw material for data scientists to analyze and extract insights. With the exponential growth in data generation, the ability to work with and derive insights from large datasets has become increasingly important.

There are generally three types of big data based on the variety, velocity, and volume of data:

Structured data
Unstructured data
Semi-structured data

09/05/2023

Post Collaborated with .science.beginners 🤝

There are several types of SQL queries that you can use to manipulate and retrieve data from a relational database. Here are some of the most common types of SQL queries:

SELECT: This query is used to retrieve data from one or more tables in a database.

INSERT: This query is used to add new records to a table in a database.

UPDATE: This query is used to modify existing records in a table in a database.

DELETE: This query is used to delete records from a table in a database.

JOIN: This query is used to combine data from two or more tables in a database based on a common column.

GROUP BY: This query is used to group rows in a table based on the values in one or more columns.

HAVING: This query is used to filter the results of a GROUP BY query based on a condition that applies to the aggregated data.

ORDER BY: This query is used to sort the results of a SELECT query by one or more columns.

These are just a few of the most common types of SQL queries. There are many other types of queries and functions available in SQL, depending on the specific database management system you are working with.

09/05/2023

Post Collaborated with .science.beginners 🤝

There are several types of SQL queries that you can use to manipulate and retrieve data from a relational database. Here are some of the most common types of SQL queries:

SELECT: This query is used to retrieve data from one or more tables in a database.

INSERT: This query is used to add new records to a table in a database.

UPDATE: This query is used to modify existing records in a table in a database.

DELETE: This query is used to delete records from a table in a database.

JOIN: This query is used to combine data from two or more tables in a database based on a common column.

GROUP BY: This query is used to group rows in a table based on the values in one or more columns.

HAVING: This query is used to filter the results of a GROUP BY query based on a condition that applies to the aggregated data.

ORDER BY: This query is used to sort the results of a SELECT query by one or more columns.

These are just a few of the most common types of SQL queries. There are many other types of queries and functions available in SQL, depending on the specific database management system you are working with.

21/04/2023

Post Collaborated with .science.beginners ✨

Sure, here's a summary of our conversation on data science tools:

◼️Languages for Data Science:

▪️Python and R are two of the most popular programming languages for data science

▪️Other languages used in data science include SQL, Java, Scala, and MATLAB

◼️Data Platforms in Data Science:

▪️Hadoop, Spark, and NoSQL databases are popular platforms for managing and processing large volumes of data in data science

◼️Data Exploration and Visualization tools in Machine Learning:

▪️Popular data exploration and visualization tools include Pandas, Matplotlib, Seaborn, and Tableau

◼️Machine Learning tools in Data Science:

▪️Popular machine learning libraries include scikit-learn, TensorFlow, and PyTorch

◼️Artificial Intelligence tools for Data Science:

▪️AI tools used in data science include natural language processing (NLP) libraries, computer vision libraries, and reinforcement learning libraries

◼️Development tools in Data Science:

▪️Popular development tools for data science include Jupyter Notebook, RStudio, Spyder, SQL Workbench/J, Git, and Docker

◼️Data Ingestion tools for Data Science:

▪️Popular data ingestion tools in data science include Apache Kafka, Apache NiFi, Apache Flume, AWS Glue, and Apache Sqoop

I hope that helps summarize our conversation!

21/04/2023

Sure, here's a summary of our conversation on data science tools:

◼️Languages for Data Science:

▪️Python and R are two of the most popular programming languages for data science

▪️Other languages used in data science include SQL, Java, Scala, and MATLAB

◼️Data Platforms in Data Science:

▪️Hadoop, Spark, and NoSQL databases are popular platforms for managing and processing large volumes of data in data science

◼️Data Exploration and Visualization tools in Machine Learning:

▪️Popular data exploration and visualization tools include Pandas, Matplotlib, Seaborn, and Tableau

◼️Machine Learning tools in Data Science:

▪️Popular machine learning libraries include scikit-learn, TensorFlow, and PyTorch

◼️Artificial Intelligence tools for Data Science:

▪️AI tools used in data science include natural language processing (NLP) libraries, computer vision libraries, and reinforcement learning libraries

◼️Development tools in Data Science:

▪️Popular development tools for data science include Jupyter Notebook, RStudio, Spyder, SQL Workbench/J, Git, and Docker

◼️Data Ingestion tools for Data Science:

▪️Popular data ingestion tools in data science include Apache Kafka, Apache NiFi, Apache Flume, AWS Glue, and Apache Sqoop

I hope that helps summarize our conversation!

18/04/2023

Coding tests a variety of abilities. It hones problem-solving and analysis skills, such as finding errors and thinking logically. Further, coding often helps people develop teamwork and interpersonal skills since software and application projects are often cross-disciplinary and collaborative.

Here are ten websites that offer coding drills for programmers to improve their coding skills:

HackerRank
CodeWars
LeetCode
Project Euler
Codecademy
CodingBat
TopCoder
CodeFights
Codewars.io
HackerEarth

12/04/2023

The roadmap for web development can vary depending on your learning objectives, but here are some common steps that you can follow:

Learn HTML: Start with the basics of HTML, which is the language that provides the structure and content of web pages.

Learn CSS: After mastering HTML, learn CSS, which is used to style and visually enhance HTML elements.

Learn JavaScript: Once you have a good understanding of HTML and CSS, move on to learning JavaScript, which is a programming language used for dynamic and interactive web pages.

Learn a Framework or Library: Next, choose a popular front-end framework or library such as React, Angular, or Vue.js. These frameworks and libraries are built on top of JavaScript and make it easier to create complex and dynamic web applications.

Learn Back-End Development: After you have a good grasp of front-end development, learn back-end development. This involves learning a back-end programming language such as Node.js, Python, Ruby, or PHP.

Learn Databases: Familiarize yourself with databases and database management systems such as MySQL, PostgreSQL, and MongoDB.

Learn Server Administration: Learn how to set up and maintain web servers using tools such as Apache, NGINX, and Microsoft IIS.

Learn APIs and Web Services: APIs and web services are essential for integrating different web applications and services. Learn about REST APIs, SOAP APIs, and GraphQL.

Learn Security Best Practices: As a web developer, you need to be aware of security vulnerabilities and how to prevent them. Learn about HTTPS, secure coding practices, and security testing.

10/04/2023

Here are some Python project ideas that can be great additions to your resume:

Web Scraper: Build a Python script that extracts data from websites and saves it in a structured format such as a CSV file. This can be useful for collecting data for research, analysis, or for building datasets.

Data Analysis Tool: Use Python to create a tool that performs exploratory data analysis, data visualization, and statistical analysis on a dataset. This can demonstrate your skills in data manipulation, visualization, and analysis.

Image Recognition System: Build a Python script that uses machine learning to identify objects in images. This can be useful for a wide range of applications such as facial recognition, object detection, and more.

Chatbot: Develop a conversational chatbot using Python that can answer common questions, provide recommendations, and even conduct simple transactions.

Sentiment Analysis Tool: Use Python to build a tool that performs sentiment analysis on text data. This can be useful for analyzing customer feedback, social media posts, and more.

Recommendation System: Develop a recommendation system using Python that provides personalized recommendations based on user behavior or preferences. This can be useful for e-commerce websites, social media platforms, and more.

Automated Testing Tool: Build a Python script that automates the testing of software applications. This can be useful for software development teams to ensure that their code is working as expected.

These are just a few project ideas to get you started. Choose a project that aligns with your interests and career goals, and be sure to showcase your skills and accomplishments on your resume.

09/04/2023

Hi guys, This post is for you. 👋

How to scrape Youtube Video Comments in Python and store in CSV file.

Code is available inside the post.

Link: https://lnkd.in/dk7CP5rY

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