100 Days of ML Challenge

Welcome to my 100 Days of Machine Learning Challenge repository! 🚀

Overview

This repository documents my journey through the 100 Days of ML Challenge, where I committed to dedicating time every day to learn and apply concepts in the vast field of Machine Learning. From fundamentals to advanced topics, this challenge has been an exciting exploration of algorithms, models, and real-world applications.

What to Expect

Daily Logs: Find daily updates on my progress, insights gained, and challenges faced throughout the 100 days.
Code and Notebooks: Access code implementations, Jupyter notebooks, and scripts developed during the challenge.
Projects: Discover hands-on projects undertaken during the challenge, showcasing practical applications of machine learning concepts.
Resources: A curated list of resources, tutorials, and references that have been instrumental in my learning journey.

Goals

Skill Enhancement: Sharpening my skills in machine learning, deep learning, and related technologies.
Project Development: Creating meaningful projects to apply theoretical knowledge to real-world scenarios.
Community Engagement: Connecting with fellow learners, exchanging ideas, and contributing to the broader machine learning community.

How to Use

Feel free to explore the repository, learn from the code, and adapt it for your own projects. Whether you're a beginner or an experienced practitioner, your feedback and suggestions are always welcome!

Connect with Me

Let's connect! Share your thoughts, questions, or just say hi:

LinkedIn: Aniket Nerkar

Let the machine learning adventure begin! 🤖✨

Name		Name	Last commit message	Last commit date
Latest commit History 85 Commits
.ipynb_checkpoints		.ipynb_checkpoints
1.Handling CSV Files		1.Handling CSV Files
10. Standardization Feature Scaling		10. Standardization Feature Scaling
100-days-of-machine-learning-main/100-days-of-machine-learning-main		100-days-of-machine-learning-main/100-days-of-machine-learning-main
11. Normalization- Feature Scalling		11. Normalization- Feature Scalling
12. Ordinal Encoding and Label Encoding		12. Ordinal Encoding and Label Encoding
13. One Hot Encoding		13. One Hot Encoding
14. Column-transformer		14. Column-transformer
15. Sklearn Pipelines		15. Sklearn Pipelines
16. Func Transformer_Log		16. Func Transformer_Log
16. Function transformer		16. Function transformer
17.Power Transformer		17.Power Transformer
18. Bining & Binarization		18. Bining & Binarization
19. Handling Mixed Variables		19. Handling Mixed Variables
2. Handling Json & SQL Files		2. Handling Json & SQL Files
20.Handling_Date_Time		20.Handling_Date_Time
21.Handling Missing Data-Complete Case Analysis		21.Handling Missing Data-Complete Case Analysis
22.Handling Missing Data_Numerical		22.Handling Missing Data_Numerical
23. Handling_Missing_Catagorical _Data		23. Handling_Missing_Catagorical _Data
24. Missing_Indicator		24. Missing_Indicator
25.KNN Imputer		25.KNN Imputer
26. Iterative Imputer		26. Iterative Imputer
27.Outliers		27.Outliers
28.Z score Method for Outliers		28.Z score Method for Outliers
29.Outliers Detection using IQR method		29.Outliers Detection using IQR method
3. Fetching Data Using API		3. Fetching Data Using API
30.Outliers Detection Using Percentile Method		30.Outliers Detection Using Percentile Method
31. Feature Construction and Feature Scalling		31. Feature Construction and Feature Scalling
32. Curse Of Dimensionality		32. Curse Of Dimensionality
33. PCA _Introduction (Principal Component Analysis)		33. PCA _Introduction (Principal Component Analysis)
34.PCA_part 2		34.PCA_part 2
35.Linear_Regression_simple		35.Linear_Regression_simple
36.Linear_Regression_From_Scratch		36.Linear_Regression_From_Scratch
37.Regression_Metrics		37.Regression_Metrics
38.Multiple Linear Regression		38.Multiple Linear Regression
39.Multiple Linear Regression From scratch		39.Multiple Linear Regression From scratch
4. Fetching Data using Web Scraping		4. Fetching Data using Web Scraping
40.Gradient_Descent_From_Scratch		40.Gradient_Descent_From_Scratch
41.Batch_Gradient_Descent		41.Batch_Gradient_Descent
42.Stochastic_gradient_Descent		42.Stochastic_gradient_Descent
43.Mini_Batch_Gradient_Descent_Scratch		43.Mini_Batch_Gradient_Descent_Scratch
44.Polynomial Regression		44.Polynomial Regression
45.Bias_Variance_Trade_Off_(Overfitting & Undefitting)		45.Bias_Variance_Trade_Off_(Overfitting & Undefitting)
46.Ridge Regression part_1		46.Ridge Regression part_1
47.Ridge Regression part_2		47.Ridge Regression part_2
48.Ridge Regression part_3		48.Ridge Regression part_3
49. 5_key_Understanding_Ridge_Regression		49. 5_key_Understanding_Ridge_Regression
5. Understanding your data		5. Understanding your data
50.Lasso_Regression		50.Lasso_Regression
51.ElasticNet_Regression		51.ElasticNet_Regression
52.Logistic_Regression_Perceptron_Trick		52.Logistic_Regression_Perceptron_Trick
53.Sigmoid_function		53.Sigmoid_function
54.Logistic_Regression_Gradient_Descent		54.Logistic_Regression_Gradient_Descent
55.Classification_metrics		55.Classification_metrics
56.Softmax Logistic Regression		56.Softmax Logistic Regression
57.Decision_Tree_intuition		57.Decision_Tree_intuition
58.Decision_Tree_Hyper_parameters		58.Decision_Tree_Hyper_parameters
59.Decision_Tree_Regression_Trees		59.Decision_Tree_Regression_Trees
6. EDA using Univariate Anakysis		6. EDA using Univariate Anakysis
60.d_treeviz_library		60.d_treeviz_library
61.Ensemble_Learning_Overview		61.Ensemble_Learning_Overview
62.Voting_Ensemble		62.Voting_Ensemble
63.Bagging_Ensemble		63.Bagging_Ensemble
64.Bagging_Classifier		64.Bagging_Classifier
65.Bagging_Regressor		65.Bagging_Regressor
66.Random_Forest		66.Random_Forest
67.Adaboost		67.Adaboost
68.K_means_Clustering		68.K_means_Clustering
69.Gradient Boosting Regression		69.Gradient Boosting Regression
7. Multivariate Analysis		7. Multivariate Analysis
70.Gradient Boosting Classifier		70.Gradient Boosting Classifier
71.Stacking_and_Blending		71.Stacking_and_Blending
72.Agglomerative_Hierarchical_clustering		72.Agglomerative_Hierarchical_clustering
73.K_nearest_neighbor		73.K_nearest_neighbor
74.Assumptions_of_Linear_Regression		74.Assumptions_of_Linear_Regression
75.Support_Vector_Machine		75.Support_Vector_Machine
76.Naive_byes		76.Naive_byes
77.XGBOOST		77.XGBOOST
78.DBSCAN_Algorithm		78.DBSCAN_Algorithm
8. Pandas Profiling		8. Pandas Profiling
9. Feature Engineering		9. Feature Engineering
README.md		README.md

jasoncobra3/100_days_ML

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