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Embark on my 100-day journey into the world of Machine Learning! Explore daily logs, code snippets, and hands-on projects as I dive into ML fundamentals, advanced concepts, and real-world applications. Join the learning adventure, contribute, and let's shape the future of AI together! 🤖✨

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.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
 
 

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100 Days of ML Challenge

Machine Learning

Machine Learning

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

  1. Skill Enhancement: Sharpening my skills in machine learning, deep learning, and related technologies.
  2. Project Development: Creating meaningful projects to apply theoretical knowledge to real-world scenarios.
  3. 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:

Let the machine learning adventure begin! 🤖✨

About

Embark on my 100-day journey into the world of Machine Learning! Explore daily logs, code snippets, and hands-on projects as I dive into ML fundamentals, advanced concepts, and real-world applications. Join the learning adventure, contribute, and let's shape the future of AI together! 🤖✨

Topics

python machine-learning machine-learning-algorithms jupyter-notebook ml machinelearning pyth 100daysofcode 100-days-of-ml-code

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