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Code with Mahzaib, Lahore (2026)

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!

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