100-Days-Of-DataScience

I'm Punith V T, diving into a 100-day data science immersion from Python fundamentals to real-world applications. This space will be a live documentation of my journey, where code meets curiosity. Let's connect, learn, and code together. Click ⭐ on GitHub to stay tuned for updates on my work!

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

100-Days-Of-DataScience

About

Releases

Packages

Languages

Name		Name	Last commit message	Last commit date
Latest commit History 28 Commits
01. Day1 Python Decision Making		01. Day1 Python Decision Making
02. Day2 Python Data Structures		02. Day2 Python Data Structures
03. Day3 Python Functions and Recursion		03. Day3 Python Functions and Recursion
04. Day04 Python Objects and Classes		04. Day04 Python Objects and Classes
05. Day05 Python Modules		05. Day05 Python Modules
06. Day06 NumPy		06. Day06 NumPy
07. Day07 NumPy Random		07. Day07 NumPy Random
08. Day08 Pandas Tutorial		08. Day08 Pandas Tutorial
09. Day09 Pandas Data Manipulation		09. Day09 Pandas Data Manipulation
10. Day10 Pandas Data Cleaning		10. Day10 Pandas Data Cleaning
11. Day11 Matplotlib Tutorial 1		11. Day11 Matplotlib Tutorial 1
12. Day12 Matplotlib Tutorial 2		12. Day12 Matplotlib Tutorial 2
13. Day13 Matplotlib Tutorial 3		13. Day13 Matplotlib Tutorial 3
14. Day14 Seaborn Tutorial 1		14. Day14 Seaborn Tutorial 1
15. Day15 Seaborn Tutorial 2		15. Day15 Seaborn Tutorial 2
Day-100 Brain Tumor Classification		Day-100 Brain Tumor Classification
Day-16 Python Revision CheatSheet		Day-16 Python Revision CheatSheet
Day-17 Numpy Revision CheatSheet		Day-17 Numpy Revision CheatSheet
Day-18 Pandas Revision CheatSheet		Day-18 Pandas Revision CheatSheet
Day-19 Matplotlib Revision CheatSheet		Day-19 Matplotlib Revision CheatSheet
Day-20 Seaborn Revision CheatSheet		Day-20 Seaborn Revision CheatSheet
Day-21 Evaluation Metrics		Day-21 Evaluation Metrics
Day-22 Evaluation Metrics		Day-22 Evaluation Metrics
Day-23 Train-Test_Split		Day-23 Train-Test_Split
Day-24 Cross-Validation		Day-24 Cross-Validation
Day-25 Implementation Cross-Validation Techniques		Day-25 Implementation Cross-Validation Techniques
Day-26 Introduction to Machine Learning		Day-26 Introduction to Machine Learning
Day-27 Type Of Machine Learning		Day-27 Type Of Machine Learning
Day-28 linear Regression		Day-28 linear Regression
Day-29 Simple Linear Regression		Day-29 Simple Linear Regression
Day-30 Multiple Linear Regression		Day-30 Multiple Linear Regression
Day-31 Implementation Multiple-linear-regression		Day-31 Implementation Multiple-linear-regression
Day-32 ML Classification		Day-32 ML Classification
Day-33 Logistic Regression		Day-33 Logistic Regression
Day-34 Introduction to SVM		Day-34 Introduction to SVM
Day-35 Implementation Of SVM		Day-35 Implementation Of SVM
Day-36 Introduction to KNN		Day-36 Introduction to KNN
Day-37 Implementation of KNN		Day-37 Implementation of KNN
Day-38 Decision Tree Algorithm		Day-38 Decision Tree Algorithm
Day-39 Implementation of Decisison Tree Algorithm		Day-39 Implementation of Decisison Tree Algorithm
Day-40 Random Forest Introduction		Day-40 Random Forest Introduction
Day-41 Random Forest Implementation		Day-41 Random Forest Implementation
Day-42 Hyperparameter Tuning Random Forest		Day-42 Hyperparameter Tuning Random Forest
Day-43 Ensamble Learning Implementation		Day-43 Ensamble Learning Implementation
Day-44 Naive Bayes Introduction		Day-44 Naive Bayes Introduction
Day-45 Implementation of Naive Bayes		Day-45 Implementation of Naive Bayes
Day-46 Intro to Unsupervised Learning		Day-46 Intro to Unsupervised Learning
Day-47 Introduction to K-Means Clustering		Day-47 Introduction to K-Means Clustering
Day-48 Clustering Full Notes		Day-48 Clustering Full Notes
Day-49 DBSCAN Clustering Algorithm Implementation		Day-49 DBSCAN Clustering Algorithm Implementation
Day-50 Implementation of hierarchical-clustering		Day-50 Implementation of hierarchical-clustering
Day-51 Gaussian Misture Model Introduction		Day-51 Gaussian Misture Model Introduction
Day-52 Implementation of Guassian Model Mixture		Day-52 Implementation of Guassian Model Mixture
Day-53 Fuzzy C-Mean Clustering implementation		Day-53 Fuzzy C-Mean Clustering implementation
Day-54 Association Rule Learning Concept		Day-54 Association Rule Learning Concept
Day-55 Apriori Algorithm introduction		Day-55 Apriori Algorithm introduction
Day-56 Implementation of Apriori Algorithm		Day-56 Implementation of Apriori Algorithm
Day-57 Intro FP - Growth Algorithm		Day-57 Intro FP - Growth Algorithm
Day-58 PCA Breast Cancer Dataset		Day-58 PCA Breast Cancer Dataset
Day-59 PCA Introduction		Day-59 PCA Introduction
Day-60 LDA - WineQuality		Day-60 LDA - WineQuality
Day-61 Reinforcement Learning Introduction		Day-61 Reinforcement Learning Introduction
Day-62 Q - Learning BTC Dataset		Day-62 Q - Learning BTC Dataset
Day-63 DQN Algorithm Salesforce dataset		Day-63 DQN Algorithm Salesforce dataset
Day-64 Genetic Algorithm		Day-64 Genetic Algorithm
Day-65 Implementation of Genetic Algorithm		Day-65 Implementation of Genetic Algorithm
Day-66 Regularization Techniques in ML		Day-66 Regularization Techniques in ML
Day-67 Ridge Regression implementation		Day-67 Ridge Regression implementation
Day-68 Lasso Regression		Day-68 Lasso Regression
Day-69 ElasticNet Regression		Day-69 ElasticNet Regression
Day-70 Data Analytics Introduction		Day-70 Data Analytics Introduction
Day-71 Time Series Analysis		Day-71 Time Series Analysis
Day-72 SimpleTime Series Method		Day-72 SimpleTime Series Method
Day-73 Exponential smoothing methods		Day-73 Exponential smoothing methods
Day-74 Auto Agressive methods		Day-74 Auto Agressive methods
Day-75 Explainable AI (XAI)		Day-75 Explainable AI (XAI)
Day-76 Explainable Ai(shap)		Day-76 Explainable Ai(shap)
Day-77 Explainable AI - Lime		Day-77 Explainable AI - Lime
Day-78 Introduction of NLP		Day-78 Introduction of NLP
Day-79 nlp-text-classification		Day-79 nlp-text-classification
Day-80 NLP Password Strength		Day-80 NLP Password Strength
Day-81 NLP Spam Detection		Day-81 NLP Spam Detection
Day-82 Oversampling - Undersampling		Day-82 Oversampling - Undersampling
Day-83 Twitter Sentiment Analysis		Day-83 Twitter Sentiment Analysis
Day-84 Emotion Detection - NLP		Day-84 Emotion Detection - NLP
Day-85 Language Detection - NLP		Day-85 Language Detection - NLP
Day-86 Data Mining And Warehousing		Day-86 Data Mining And Warehousing
Day-87 Classification Of Data Mining		Day-87 Classification Of Data Mining
Day-88 Classification and Predication in DMW		Day-88 Classification and Predication in DMW
Day-89 Intro to MySql Database		Day-89 Intro to MySql Database
Day-90 Sql Cheatsheet		Day-90 Sql Cheatsheet
Day-91 Intro to Web Scrapping		Day-91 Intro to Web Scrapping
Day-92 CheatSheet Web Scraping		Day-92 CheatSheet Web Scraping
Day-93 Intro to Big Data		Day-93 Intro to Big Data
Day-94 Intro to computer Vision		Day-94 Intro to computer Vision
Day-95 computer Vision Image processing		Day-95 computer Vision Image processing
Day-96 Image Processing		Day-96 Image Processing
Day-97 WaterMark using CV		Day-97 WaterMark using CV
Day-98 texture feature extraction		Day-98 texture feature extraction
Day-99 Lane Detection		Day-99 Lane Detection

SENATOROVAI/100-Days-Of-DataScience

Folders and files

Latest commit

History

Repository files navigation

100-Days-Of-DataScience

About

Resources

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages