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advanced_data_visualization_tools

The present hands-on lab mainly uses Immigration to Canada dataset and employs advanced visualization tools such as word cloud, and waffle plot to display relations between features within the dataset.

applied_ml_4_dummies

This course was developed to teach ML from scratch up to TensorFlow and PyTorch implementation.

cfrp_reinforced_hdd_overbend

This project employs machine learning and synthetic dataset to predict the peak equivalent stress imposed on a CFRP wrapped HDD overbend

dashboarding_with_python

This repository presents step by step approach to create an interactive dashboard using the Dash and the Plotly graphing libraries. The analyses are carried on Airline Reporting Carrier On-Time Performance dataset from Data Asset eXchange

data_exploratory_analysis

Data exploratory analysis on Nutrition Facts for McDonald's Menu

docsgpt

This app allows users to easily query a PDF document using OpenAI's GPT-3 language model in Google Colab, utilizing Google Drive for storage.

farhad-davaripour

leet_code

Practicing Leet Code examples.

linear_regression_core_principles

Building a Linear Regression Model from scratch.

llm

Reviewing LLM courses and adding distilled notes

machine_learning_with_python

The present notebook provides data analysis on several datasets using different machine learning (ML) techniques including supervised ML, unsupervised ML, and recommender system

nlp_distilled_notes

In this repository, I've consolidated my summarized notes from various NLP (Natural Language Processing) topics that I've reviewed.

polynomial_models_and_regularization

This lecture covers polynomial regression for complex relationships and regularization techniques to prevent overfitting, emphasizing model sophistication and generalizability.

python-for-data-science-ai-development

In this repository, a few hands-on practice learning labs for data science are presented. These labs are built as a part of IBM Data Science Professional Certificate. The IBM Data Science program consists of 10 online courses that will provide the most updated tools and skills including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modelling, and machine learning algorithms. The skeleton of the labs is provided within the online courses.

recommender_system_distilled_notes

A curated collection of essential notes on recommender systems, distilled for quick insights and easy understanding.

spacex_first_stage_data_analysis

SpaceX is a well-known private company famous for several historic milestones in launching rockets and successfully returning the first stage. Due to the importance of successful first stage landing, the present work uses Machine Learning (ML) models to predict the outcome of the first stage landing.

stanford-cs229-spring2023-notes

CS229 course notes from Stanford University on machine learning, covering lectures, and fundamental concepts and algorithms. A comprehensive resource for students and anyone interested in machine learning.

step_by_step_data_analysis_auto_dataset

step by step approach for data wrangling, descriptive statistical analysis, predictive analysis, model development, model evaluation, and decision making.